final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NBA Predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[GitHub repo](https://github.com/sobyl/NBA_Prediction/tree/master)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Team Members\n",
    "Yu Liu, Hui Du, Siyuan Ji."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contribution\n",
    "Yu Liu: Data collection, Data Cleaning, model with XGB and MVP prediction.\n",
    "\n",
    "Hui Du: Data Cleaning, models with LR, SVM.\n",
    "\n",
    "Siyuan Ji: Data collection MVP prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset and motivation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is collect from [basketball reference](https://www.basketball-reference.com), which has almost all data of NBA. We try to collect NBA data to analyse every team and player's game performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actual task definition/Research Question "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are trying to analyze the recent 5 years NBA games results to predict win or lose of every game, and to analyze MVP votings recent years to predict NBA normal season most valuable player.\n",
    "\n",
    "input: tables from [basketball reference](https://www.basketball-reference.com).\n",
    "\n",
    "output: predict results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Literature review"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reference some data features from [MVP prediction model](https://towardsdatascience.com/nba-mvp-predictor-c700e50e0917). \n",
    "\n",
    "In our work, we get mean of 10 recent game results before the game we analyse as the base data to predict the result of this game. And we get recent years MVP votings results and player performances of entire normal season to predict their voting rankings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_intermediate_results = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Team List and their Abbreviations\n",
    "We get all NBA/ABA temas' names and their abbreviations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.basketball-reference.com/teams/'\n",
    "html = requests.get(url).content\n",
    "df_list = pd.read_html(html)\n",
    "tames = df_list[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Glossary:  \n",
    "    Lg -- League  From -- First year  To -- Last year  G -- Games  W -- Wins  L -- Losses  \n",
    "    W/L% -- Win-Loss Percentage  Plyfs -- Years team made the playoffs  \n",
    "    Div -- Years team finished first (or tied for first) in the division  \n",
    "    Conf -- Years team won the conference championship  Champ -- Years team won the league championship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Franchise</th>\n",
       "      <th>Lg</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>Yrs</th>\n",
       "      <th>G</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>W/L%</th>\n",
       "      <th>Plyfs</th>\n",
       "      <th>Div</th>\n",
       "      <th>Conf</th>\n",
       "      <th>Champ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1950</td>\n",
       "      <td>2019</td>\n",
       "      <td>70</td>\n",
       "      <td>5552</td>\n",
       "      <td>2746</td>\n",
       "      <td>2806</td>\n",
       "      <td>0.495</td>\n",
       "      <td>46</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1969</td>\n",
       "      <td>2019</td>\n",
       "      <td>51</td>\n",
       "      <td>4134</td>\n",
       "      <td>2048</td>\n",
       "      <td>2086</td>\n",
       "      <td>0.495</td>\n",
       "      <td>33</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>St. Louis Hawks</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1956</td>\n",
       "      <td>1968</td>\n",
       "      <td>13</td>\n",
       "      <td>1005</td>\n",
       "      <td>553</td>\n",
       "      <td>452</td>\n",
       "      <td>0.550</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Milwaukee Hawks</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1952</td>\n",
       "      <td>1955</td>\n",
       "      <td>4</td>\n",
       "      <td>281</td>\n",
       "      <td>91</td>\n",
       "      <td>190</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tri-Cities Blackhawks</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1950</td>\n",
       "      <td>1951</td>\n",
       "      <td>2</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>78</td>\n",
       "      <td>0.409</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NBA/BAA</td>\n",
       "      <td>1947</td>\n",
       "      <td>2019</td>\n",
       "      <td>73</td>\n",
       "      <td>5724</td>\n",
       "      <td>3378</td>\n",
       "      <td>2346</td>\n",
       "      <td>0.590</td>\n",
       "      <td>56</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>NBA/ABA</td>\n",
       "      <td>1968</td>\n",
       "      <td>2019</td>\n",
       "      <td>52</td>\n",
       "      <td>4222</td>\n",
       "      <td>1824</td>\n",
       "      <td>2398</td>\n",
       "      <td>0.432</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>NBA</td>\n",
       "      <td>2013</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>574</td>\n",
       "      <td>242</td>\n",
       "      <td>332</td>\n",
       "      <td>0.422</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Jersey Nets</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1978</td>\n",
       "      <td>2012</td>\n",
       "      <td>35</td>\n",
       "      <td>2822</td>\n",
       "      <td>1186</td>\n",
       "      <td>1636</td>\n",
       "      <td>0.420</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>New York Nets</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1977</td>\n",
       "      <td>1977</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>22</td>\n",
       "      <td>60</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>New York Nets</td>\n",
       "      <td>ABA</td>\n",
       "      <td>1969</td>\n",
       "      <td>1976</td>\n",
       "      <td>8</td>\n",
       "      <td>666</td>\n",
       "      <td>338</td>\n",
       "      <td>328</td>\n",
       "      <td>0.508</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>New Jersey Americans</td>\n",
       "      <td>ABA</td>\n",
       "      <td>1968</td>\n",
       "      <td>1968</td>\n",
       "      <td>1</td>\n",
       "      <td>78</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1989</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "      <td>2330</td>\n",
       "      <td>1027</td>\n",
       "      <td>1303</td>\n",
       "      <td>0.441</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>NBA</td>\n",
       "      <td>2015</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>410</td>\n",
       "      <td>192</td>\n",
       "      <td>218</td>\n",
       "      <td>0.468</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Charlotte Bobcats</td>\n",
       "      <td>NBA</td>\n",
       "      <td>2005</td>\n",
       "      <td>2014</td>\n",
       "      <td>10</td>\n",
       "      <td>804</td>\n",
       "      <td>293</td>\n",
       "      <td>511</td>\n",
       "      <td>0.364</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1989</td>\n",
       "      <td>2002</td>\n",
       "      <td>14</td>\n",
       "      <td>1116</td>\n",
       "      <td>542</td>\n",
       "      <td>574</td>\n",
       "      <td>0.486</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1967</td>\n",
       "      <td>2019</td>\n",
       "      <td>53</td>\n",
       "      <td>4297</td>\n",
       "      <td>2205</td>\n",
       "      <td>2092</td>\n",
       "      <td>0.513</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1971</td>\n",
       "      <td>2019</td>\n",
       "      <td>49</td>\n",
       "      <td>3970</td>\n",
       "      <td>1848</td>\n",
       "      <td>2122</td>\n",
       "      <td>0.465</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1981</td>\n",
       "      <td>2019</td>\n",
       "      <td>39</td>\n",
       "      <td>3150</td>\n",
       "      <td>1572</td>\n",
       "      <td>1578</td>\n",
       "      <td>0.499</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>NBA/ABA</td>\n",
       "      <td>1968</td>\n",
       "      <td>2019</td>\n",
       "      <td>52</td>\n",
       "      <td>4222</td>\n",
       "      <td>2116</td>\n",
       "      <td>2106</td>\n",
       "      <td>0.501</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1977</td>\n",
       "      <td>2019</td>\n",
       "      <td>43</td>\n",
       "      <td>3478</td>\n",
       "      <td>1703</td>\n",
       "      <td>1775</td>\n",
       "      <td>0.490</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>ABA</td>\n",
       "      <td>1975</td>\n",
       "      <td>1976</td>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>125</td>\n",
       "      <td>43</td>\n",
       "      <td>0.744</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Denver Rockets</td>\n",
       "      <td>ABA</td>\n",
       "      <td>1968</td>\n",
       "      <td>1974</td>\n",
       "      <td>7</td>\n",
       "      <td>576</td>\n",
       "      <td>288</td>\n",
       "      <td>288</td>\n",
       "      <td>0.500</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>NBA/BAA</td>\n",
       "      <td>1949</td>\n",
       "      <td>2019</td>\n",
       "      <td>71</td>\n",
       "      <td>5614</td>\n",
       "      <td>2733</td>\n",
       "      <td>2881</td>\n",
       "      <td>0.487</td>\n",
       "      <td>42</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1958</td>\n",
       "      <td>2019</td>\n",
       "      <td>62</td>\n",
       "      <td>4995</td>\n",
       "      <td>2420</td>\n",
       "      <td>2575</td>\n",
       "      <td>0.484</td>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Fort Wayne Pistons</td>\n",
       "      <td>NBA/BAA</td>\n",
       "      <td>1949</td>\n",
       "      <td>1957</td>\n",
       "      <td>9</td>\n",
       "      <td>619</td>\n",
       "      <td>313</td>\n",
       "      <td>306</td>\n",
       "      <td>0.506</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>NBA/BAA</td>\n",
       "      <td>1947</td>\n",
       "      <td>2019</td>\n",
       "      <td>73</td>\n",
       "      <td>5720</td>\n",
       "      <td>2772</td>\n",
       "      <td>2948</td>\n",
       "      <td>0.485</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1972</td>\n",
       "      <td>2019</td>\n",
       "      <td>48</td>\n",
       "      <td>3888</td>\n",
       "      <td>1884</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.485</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>San Francisco Warriors</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1963</td>\n",
       "      <td>1971</td>\n",
       "      <td>9</td>\n",
       "      <td>729</td>\n",
       "      <td>330</td>\n",
       "      <td>399</td>\n",
       "      <td>0.453</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Philadelphia Warriors</td>\n",
       "      <td>NBA/BAA</td>\n",
       "      <td>1947</td>\n",
       "      <td>1962</td>\n",
       "      <td>16</td>\n",
       "      <td>1103</td>\n",
       "      <td>558</td>\n",
       "      <td>545</td>\n",
       "      <td>0.506</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>NBA</td>\n",
       "      <td>2009</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>886</td>\n",
       "      <td>538</td>\n",
       "      <td>348</td>\n",
       "      <td>0.607</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Seattle SuperSonics</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1968</td>\n",
       "      <td>2008</td>\n",
       "      <td>41</td>\n",
       "      <td>3330</td>\n",
       "      <td>1745</td>\n",
       "      <td>1585</td>\n",
       "      <td>0.524</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1990</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "      <td>2412</td>\n",
       "      <td>1158</td>\n",
       "      <td>1254</td>\n",
       "      <td>0.480</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1950</td>\n",
       "      <td>2019</td>\n",
       "      <td>70</td>\n",
       "      <td>5550</td>\n",
       "      <td>2857</td>\n",
       "      <td>2693</td>\n",
       "      <td>0.515</td>\n",
       "      <td>49</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1964</td>\n",
       "      <td>2019</td>\n",
       "      <td>56</td>\n",
       "      <td>4537</td>\n",
       "      <td>2281</td>\n",
       "      <td>2256</td>\n",
       "      <td>0.503</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Syracuse Nationals</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1950</td>\n",
       "      <td>1963</td>\n",
       "      <td>14</td>\n",
       "      <td>1013</td>\n",
       "      <td>576</td>\n",
       "      <td>437</td>\n",
       "      <td>0.569</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1969</td>\n",
       "      <td>2019</td>\n",
       "      <td>51</td>\n",
       "      <td>4134</td>\n",
       "      <td>2186</td>\n",
       "      <td>1948</td>\n",
       "      <td>0.529</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1971</td>\n",
       "      <td>2019</td>\n",
       "      <td>49</td>\n",
       "      <td>3970</td>\n",
       "      <td>2134</td>\n",
       "      <td>1836</td>\n",
       "      <td>0.538</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>NBA/BAA</td>\n",
       "      <td>1949</td>\n",
       "      <td>2019</td>\n",
       "      <td>71</td>\n",
       "      <td>5615</td>\n",
       "      <td>2562</td>\n",
       "      <td>3053</td>\n",
       "      <td>0.456</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1986</td>\n",
       "      <td>2019</td>\n",
       "      <td>34</td>\n",
       "      <td>2740</td>\n",
       "      <td>1156</td>\n",
       "      <td>1584</td>\n",
       "      <td>0.422</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Kansas City Kings</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1976</td>\n",
       "      <td>1985</td>\n",
       "      <td>10</td>\n",
       "      <td>820</td>\n",
       "      <td>381</td>\n",
       "      <td>439</td>\n",
       "      <td>0.465</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Kansas City-Omaha Kings</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1973</td>\n",
       "      <td>1975</td>\n",
       "      <td>3</td>\n",
       "      <td>246</td>\n",
       "      <td>113</td>\n",
       "      <td>133</td>\n",
       "      <td>0.459</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Cincinnati Royals</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1958</td>\n",
       "      <td>1972</td>\n",
       "      <td>15</td>\n",
       "      <td>1189</td>\n",
       "      <td>555</td>\n",
       "      <td>634</td>\n",
       "      <td>0.467</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Rochester Royals</td>\n",
       "      <td>NBA/BAA</td>\n",
       "      <td>1949</td>\n",
       "      <td>1957</td>\n",
       "      <td>9</td>\n",
       "      <td>620</td>\n",
       "      <td>357</td>\n",
       "      <td>263</td>\n",
       "      <td>0.576</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NBA/ABA</td>\n",
       "      <td>1968</td>\n",
       "      <td>2019</td>\n",
       "      <td>52</td>\n",
       "      <td>4222</td>\n",
       "      <td>2540</td>\n",
       "      <td>1682</td>\n",
       "      <td>0.602</td>\n",
       "      <td>47</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1977</td>\n",
       "      <td>2019</td>\n",
       "      <td>43</td>\n",
       "      <td>3478</td>\n",
       "      <td>2162</td>\n",
       "      <td>1316</td>\n",
       "      <td>0.622</td>\n",
       "      <td>39</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>ABA</td>\n",
       "      <td>1974</td>\n",
       "      <td>1976</td>\n",
       "      <td>3</td>\n",
       "      <td>252</td>\n",
       "      <td>146</td>\n",
       "      <td>106</td>\n",
       "      <td>0.579</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Texas Chaparrals</td>\n",
       "      <td>ABA</td>\n",
       "      <td>1971</td>\n",
       "      <td>1971</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>30</td>\n",
       "      <td>54</td>\n",
       "      <td>0.357</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Dallas Chaparrals</td>\n",
       "      <td>ABA</td>\n",
       "      <td>1968</td>\n",
       "      <td>1973</td>\n",
       "      <td>5</td>\n",
       "      <td>408</td>\n",
       "      <td>202</td>\n",
       "      <td>206</td>\n",
       "      <td>0.495</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1996</td>\n",
       "      <td>2019</td>\n",
       "      <td>24</td>\n",
       "      <td>1920</td>\n",
       "      <td>902</td>\n",
       "      <td>1018</td>\n",
       "      <td>0.470</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1975</td>\n",
       "      <td>2019</td>\n",
       "      <td>45</td>\n",
       "      <td>3642</td>\n",
       "      <td>1964</td>\n",
       "      <td>1678</td>\n",
       "      <td>0.539</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1980</td>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>3232</td>\n",
       "      <td>1803</td>\n",
       "      <td>1429</td>\n",
       "      <td>0.558</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>New Orleans Jazz</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1975</td>\n",
       "      <td>1979</td>\n",
       "      <td>5</td>\n",
       "      <td>410</td>\n",
       "      <td>161</td>\n",
       "      <td>249</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1962</td>\n",
       "      <td>2019</td>\n",
       "      <td>58</td>\n",
       "      <td>4697</td>\n",
       "      <td>2128</td>\n",
       "      <td>2569</td>\n",
       "      <td>0.453</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Washington Wizards</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1998</td>\n",
       "      <td>2019</td>\n",
       "      <td>22</td>\n",
       "      <td>1756</td>\n",
       "      <td>750</td>\n",
       "      <td>1006</td>\n",
       "      <td>0.427</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Washington Bullets</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1975</td>\n",
       "      <td>1997</td>\n",
       "      <td>23</td>\n",
       "      <td>1886</td>\n",
       "      <td>887</td>\n",
       "      <td>999</td>\n",
       "      <td>0.470</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Capital Bullets</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1974</td>\n",
       "      <td>1974</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>47</td>\n",
       "      <td>35</td>\n",
       "      <td>0.573</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Baltimore Bullets</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1964</td>\n",
       "      <td>1973</td>\n",
       "      <td>10</td>\n",
       "      <td>813</td>\n",
       "      <td>401</td>\n",
       "      <td>412</td>\n",
       "      <td>0.493</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Chicago Zephyrs</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1963</td>\n",
       "      <td>1963</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>25</td>\n",
       "      <td>55</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Chicago Packers</td>\n",
       "      <td>NBA</td>\n",
       "      <td>1962</td>\n",
       "      <td>1962</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>18</td>\n",
       "      <td>62</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Franchise       Lg  From    To  Yrs     G     W     L  \\\n",
       "0             Atlanta Hawks      NBA  1950  2019   70  5552  2746  2806   \n",
       "1             Atlanta Hawks      NBA  1969  2019   51  4134  2048  2086   \n",
       "2           St. Louis Hawks      NBA  1956  1968   13  1005   553   452   \n",
       "3           Milwaukee Hawks      NBA  1952  1955    4   281    91   190   \n",
       "4     Tri-Cities Blackhawks      NBA  1950  1951    2   132    54    78   \n",
       "5            Boston Celtics  NBA/BAA  1947  2019   73  5724  3378  2346   \n",
       "6             Brooklyn Nets  NBA/ABA  1968  2019   52  4222  1824  2398   \n",
       "7             Brooklyn Nets      NBA  2013  2019    7   574   242   332   \n",
       "8           New Jersey Nets      NBA  1978  2012   35  2822  1186  1636   \n",
       "9             New York Nets      NBA  1977  1977    1    82    22    60   \n",
       "10            New York Nets      ABA  1969  1976    8   666   338   328   \n",
       "11     New Jersey Americans      ABA  1968  1968    1    78    36    42   \n",
       "12        Charlotte Hornets      NBA  1989  2019   29  2330  1027  1303   \n",
       "13        Charlotte Hornets      NBA  2015  2019    5   410   192   218   \n",
       "14        Charlotte Bobcats      NBA  2005  2014   10   804   293   511   \n",
       "15        Charlotte Hornets      NBA  1989  2002   14  1116   542   574   \n",
       "16            Chicago Bulls      NBA  1967  2019   53  4297  2205  2092   \n",
       "17      Cleveland Cavaliers      NBA  1971  2019   49  3970  1848  2122   \n",
       "18         Dallas Mavericks      NBA  1981  2019   39  3150  1572  1578   \n",
       "19           Denver Nuggets  NBA/ABA  1968  2019   52  4222  2116  2106   \n",
       "20           Denver Nuggets      NBA  1977  2019   43  3478  1703  1775   \n",
       "21           Denver Nuggets      ABA  1975  1976    2   168   125    43   \n",
       "22           Denver Rockets      ABA  1968  1974    7   576   288   288   \n",
       "23          Detroit Pistons  NBA/BAA  1949  2019   71  5614  2733  2881   \n",
       "24          Detroit Pistons      NBA  1958  2019   62  4995  2420  2575   \n",
       "25       Fort Wayne Pistons  NBA/BAA  1949  1957    9   619   313   306   \n",
       "26    Golden State Warriors  NBA/BAA  1947  2019   73  5720  2772  2948   \n",
       "27    Golden State Warriors      NBA  1972  2019   48  3888  1884  2004   \n",
       "28   San Francisco Warriors      NBA  1963  1971    9   729   330   399   \n",
       "29    Philadelphia Warriors  NBA/BAA  1947  1962   16  1103   558   545   \n",
       "..                      ...      ...   ...   ...  ...   ...   ...   ...   \n",
       "55    Oklahoma City Thunder      NBA  2009  2019   11   886   538   348   \n",
       "56      Seattle SuperSonics      NBA  1968  2008   41  3330  1745  1585   \n",
       "57            Orlando Magic      NBA  1990  2019   30  2412  1158  1254   \n",
       "58       Philadelphia 76ers      NBA  1950  2019   70  5550  2857  2693   \n",
       "59       Philadelphia 76ers      NBA  1964  2019   56  4537  2281  2256   \n",
       "60       Syracuse Nationals      NBA  1950  1963   14  1013   576   437   \n",
       "61             Phoenix Suns      NBA  1969  2019   51  4134  2186  1948   \n",
       "62   Portland Trail Blazers      NBA  1971  2019   49  3970  2134  1836   \n",
       "63         Sacramento Kings  NBA/BAA  1949  2019   71  5615  2562  3053   \n",
       "64         Sacramento Kings      NBA  1986  2019   34  2740  1156  1584   \n",
       "65        Kansas City Kings      NBA  1976  1985   10   820   381   439   \n",
       "66  Kansas City-Omaha Kings      NBA  1973  1975    3   246   113   133   \n",
       "67        Cincinnati Royals      NBA  1958  1972   15  1189   555   634   \n",
       "68         Rochester Royals  NBA/BAA  1949  1957    9   620   357   263   \n",
       "69        San Antonio Spurs  NBA/ABA  1968  2019   52  4222  2540  1682   \n",
       "70        San Antonio Spurs      NBA  1977  2019   43  3478  2162  1316   \n",
       "71        San Antonio Spurs      ABA  1974  1976    3   252   146   106   \n",
       "72         Texas Chaparrals      ABA  1971  1971    1    84    30    54   \n",
       "73        Dallas Chaparrals      ABA  1968  1973    5   408   202   206   \n",
       "74          Toronto Raptors      NBA  1996  2019   24  1920   902  1018   \n",
       "75                Utah Jazz      NBA  1975  2019   45  3642  1964  1678   \n",
       "76                Utah Jazz      NBA  1980  2019   40  3232  1803  1429   \n",
       "77         New Orleans Jazz      NBA  1975  1979    5   410   161   249   \n",
       "78       Washington Wizards      NBA  1962  2019   58  4697  2128  2569   \n",
       "79       Washington Wizards      NBA  1998  2019   22  1756   750  1006   \n",
       "80       Washington Bullets      NBA  1975  1997   23  1886   887   999   \n",
       "81          Capital Bullets      NBA  1974  1974    1    82    47    35   \n",
       "82        Baltimore Bullets      NBA  1964  1973   10   813   401   412   \n",
       "83          Chicago Zephyrs      NBA  1963  1963    1    80    25    55   \n",
       "84          Chicago Packers      NBA  1962  1962    1    80    18    62   \n",
       "\n",
       "     W/L%  Plyfs  Div  Conf  Champ  \n",
       "0   0.495     46   11     0      1  \n",
       "1   0.495     33    5     0      0  \n",
       "2   0.550     12    6     0      1  \n",
       "3   0.324      0    0     0      0  \n",
       "4   0.409      1    0     0      0  \n",
       "5   0.590     56   31     9     17  \n",
       "6   0.432     27    5     2      2  \n",
       "7   0.422      4    0     0      0  \n",
       "8   0.420     16    4     2      0  \n",
       "9   0.268      0    0     0      0  \n",
       "10  0.508      7    1     0      2  \n",
       "11  0.462      0    0     0      0  \n",
       "12  0.441     10    0     0      0  \n",
       "13  0.468      1    0     0      0  \n",
       "14  0.364      2    0     0      0  \n",
       "15  0.486      7    0     0      0  \n",
       "16  0.513     35    9     6      6  \n",
       "17  0.465     22    7     5      1  \n",
       "18  0.499     21    3     2      1  \n",
       "19  0.501     34   11     0      0  \n",
       "20  0.490     25    8     0      0  \n",
       "21  0.744      2    2     0      0  \n",
       "22  0.500      7    1     0      0  \n",
       "23  0.487     42   11     5      3  \n",
       "24  0.484     34    9     5      3  \n",
       "25  0.506      8    2     0      0  \n",
       "26  0.485     35   12     5      6  \n",
       "27  0.485     18    7     5      4  \n",
       "28  0.453      5    2     0      0  \n",
       "29  0.506     12    3     0      2  \n",
       "..    ...    ...  ...   ...    ...  \n",
       "55  0.607      9    5     1      0  \n",
       "56  0.524     22    6     3      1  \n",
       "57  0.480     15    6     2      0  \n",
       "58  0.515     49   11     5      3  \n",
       "59  0.503     35    8     5      2  \n",
       "60  0.569     14    3     0      1  \n",
       "61  0.529     29    6     2      0  \n",
       "62  0.538     35    6     3      1  \n",
       "63  0.456     29    5     0      1  \n",
       "64  0.422     10    2     0      0  \n",
       "65  0.465      4    1     0      0  \n",
       "66  0.459      1    0     0      0  \n",
       "67  0.467      7    0     0      0  \n",
       "68  0.576      7    2     0      1  \n",
       "69  0.602     47   22     6      5  \n",
       "70  0.622     39   22     6      5  \n",
       "71  0.579      3    0     0      0  \n",
       "72  0.357      1    0     0      0  \n",
       "73  0.495      4    0     0      0  \n",
       "74  0.470     11    6     0      0  \n",
       "75  0.539     28    9     2      0  \n",
       "76  0.558     28    9     2      0  \n",
       "77  0.393      0    0     0      0  \n",
       "78  0.453     29    8     4      1  \n",
       "79  0.427      8    1     0      0  \n",
       "80  0.470     13    2     3      1  \n",
       "81  0.573      1    1     0      0  \n",
       "82  0.493      7    4     1      0  \n",
       "83  0.313      0    0     0      0  \n",
       "84  0.225      0    0     0      0  \n",
       "\n",
       "[85 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "team_l = list(tames[tames.To==2019].Franchise.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate 30 NBA teams' names and their abbreviations nowadays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "teams_d = {}\n",
    "for team in team_l:\n",
    "    name = team.split(' ')\n",
    "    if len(name) == 2:\n",
    "        abb = name[0][:3].upper()\n",
    "    else:\n",
    "        abb = ''\n",
    "        for word in name:\n",
    "            abb += word[0].upper()\n",
    "    team_name = '_'.join(name)\n",
    "    teams_d[team_name] = abb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Atlanta_Hawks': 'ATL',\n",
       " 'Boston_Celtics': 'BOS',\n",
       " 'Brooklyn_Nets': 'BRK',\n",
       " 'Charlotte_Hornets': 'CHO',\n",
       " 'Chicago_Bulls': 'CHI',\n",
       " 'Cleveland_Cavaliers': 'CLE',\n",
       " 'Dallas_Mavericks': 'DAL',\n",
       " 'Denver_Nuggets': 'DEN',\n",
       " 'Detroit_Pistons': 'DET',\n",
       " 'Golden_State_Warriors': 'GSW',\n",
       " 'Houston_Rockets': 'HOU',\n",
       " 'Indiana_Pacers': 'IND',\n",
       " 'Los_Angeles_Clippers': 'LAC',\n",
       " 'Los_Angeles_Lakers': 'LAL',\n",
       " 'Memphis_Grizzlies': 'MEM',\n",
       " 'Miami_Heat': 'MIA',\n",
       " 'Milwaukee_Bucks': 'MIL',\n",
       " 'Minnesota_Timberwolves': 'MIN',\n",
       " 'New_Orleans_Pelicans': 'NOP',\n",
       " 'New_York_Knicks': 'NYK',\n",
       " 'Oklahoma_City_Thunder': 'OKC',\n",
       " 'Orlando_Magic': 'ORL',\n",
       " 'Philadelphia_76ers': 'PHI',\n",
       " 'Phoenix_Suns': 'PHO',\n",
       " 'Portland_Trail_Blazers': 'POR',\n",
       " 'Sacramento_Kings': 'SAC',\n",
       " 'San_Antonio_Spurs': 'SAS',\n",
       " 'Toronto_Raptors': 'TOR',\n",
       " 'Utah_Jazz': 'UTA',\n",
       " 'Washington_Wizards': 'WAS'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams_d['Oklahoma_City_Thunder'] = 'OKC'\n",
    "teams_d['Brooklyn_Nets'] = 'NJN'\n",
    "teams_d['Charlotte_Hornets'] = 'CHO'\n",
    "teams_d['Brooklyn_Nets'] = 'BRK'\n",
    "teams_d['Portland_Trail_Blazers'] = 'POR'\n",
    "teams_d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Team Schedules and Results\n",
    "We can get a team's whole shchedules and game results in a normal season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.basketball-reference.com/teams/LAL/2019_games.html'\n",
    "html = requests.get(url).content\n",
    "df_list = pd.read_html(html)\n",
    "lakers = df_list[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop some rows with duplicated column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([20, 41, 62, 83], dtype='int64')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lakers[lakers.G=='G'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lakers.drop(lakers[lakers.G=='G'].index, inplace=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lakers.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Glossary:\n",
    "    G -- Games\n",
    "    Start (ET) -- Time zone = ET\n",
    "    Tm -- Points\n",
    "    Opp -- Opponent Points\n",
    "    W -- Wins\n",
    "    L -- Losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82, 15)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lakers.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>G</th>\n",
       "      <th>Date</th>\n",
       "      <th>Start (ET)</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Opponent</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Opp</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>Streak</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Thu, Oct 18, 2018</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box Score</td>\n",
       "      <td>@</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>L 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Sat, Oct 20, 2018</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box Score</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>L 2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mon, Oct 22, 2018</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box Score</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>L</td>\n",
       "      <td>OT</td>\n",
       "      <td>142</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>L 3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Wed, Oct 24, 2018</td>\n",
       "      <td>10:00p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box Score</td>\n",
       "      <td>@</td>\n",
       "      <td>Phoenix Suns</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>W 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Thu, Oct 25, 2018</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box Score</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121</td>\n",
       "      <td>114</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>W 2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Sat, Oct 27, 2018</td>\n",
       "      <td>8:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box Score</td>\n",
       "      <td>@</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>L 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Mon, Oct 29, 2018</td>\n",
       "      <td>8:00p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box Score</td>\n",
       "      <td>@</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120</td>\n",
       "      <td>124</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>L 2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Wed, Oct 31, 2018</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box Score</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114</td>\n",
       "      <td>113</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>W 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Sat, Nov 3, 2018</td>\n",
       "      <td>10:00p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box Score</td>\n",
       "      <td>@</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114</td>\n",
       "      <td>110</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>W 2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Sun, Nov 4, 2018</td>\n",
       "      <td>9:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Box Score</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "      <td>121</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>L 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    G               Date Start (ET)  Unnamed: 3 Unnamed: 4 Unnamed: 5  \\\n",
       "0   1  Thu, Oct 18, 2018     10:30p         NaN  Box Score          @   \n",
       "1   2  Sat, Oct 20, 2018     10:30p         NaN  Box Score        NaN   \n",
       "2   3  Mon, Oct 22, 2018     10:30p         NaN  Box Score        NaN   \n",
       "3   4  Wed, Oct 24, 2018     10:00p         NaN  Box Score          @   \n",
       "4   5  Thu, Oct 25, 2018     10:30p         NaN  Box Score        NaN   \n",
       "5   6  Sat, Oct 27, 2018      8:30p         NaN  Box Score          @   \n",
       "6   7  Mon, Oct 29, 2018      8:00p         NaN  Box Score          @   \n",
       "7   8  Wed, Oct 31, 2018     10:30p         NaN  Box Score        NaN   \n",
       "8   9   Sat, Nov 3, 2018     10:00p         NaN  Box Score          @   \n",
       "9  10   Sun, Nov 4, 2018      9:30p         NaN  Box Score        NaN   \n",
       "\n",
       "                 Opponent Unnamed: 7 Unnamed: 8   Tm  Opp  W  L Streak Notes  \n",
       "0  Portland Trail Blazers          L        NaN  119  128  0  1    L 1   NaN  \n",
       "1         Houston Rockets          L        NaN  115  124  0  2    L 2   NaN  \n",
       "2       San Antonio Spurs          L         OT  142  143  0  3    L 3   NaN  \n",
       "3            Phoenix Suns          W        NaN  131  113  1  3    W 1   NaN  \n",
       "4          Denver Nuggets          W        NaN  121  114  2  3    W 2   NaN  \n",
       "5       San Antonio Spurs          L        NaN  106  110  2  4    L 1   NaN  \n",
       "6  Minnesota Timberwolves          L        NaN  120  124  2  5    L 2   NaN  \n",
       "7        Dallas Mavericks          W        NaN  114  113  3  5    W 1   NaN  \n",
       "8  Portland Trail Blazers          W        NaN  114  110  4  5    W 2   NaN  \n",
       "9         Toronto Raptors          L        NaN  107  121  4  6    L 1   NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lakers.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From table above we can see there are some coolumns with missing names or values, we will deal with it later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generate a dictionary with 30 teams' schedules from Season 2015 to Season 2018, and drop some colunms only have nan values, rename coolumns, and change some columns' type from string to numeric.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = [2019,2018,2017,2016,2015]\n",
    "seasons = {}\n",
    "to_num_list = ['W', 'L']\n",
    "for year in years:\n",
    "    seasons[year]={}\n",
    "    for name, add in teams_d.items():\n",
    "        url = 'https://www.basketball-reference.com/teams/' + add + '/' + str(year) + '_games.html'\n",
    "        html = requests.get(url).content\n",
    "        df_list = pd.read_html(html)\n",
    "        df = df_list[0]\n",
    "        df.drop(df[df.G=='G'].index, inplace=True)\n",
    "        df.reset_index(drop=True, inplace=True)\n",
    "        df['Opponent'] = df.Opponent.map(lambda x: '_'.join(x.split(' ')))\n",
    "        df['Date'] = df.Date.map(lambda x: ''.join(str(pd.to_datetime(x)).split(' ')[0].split('-')))\n",
    "        df.drop(df.columns[[3, 4]], axis=1, inplace=True)\n",
    "        df.rename(columns = {'Unnamed: 5':'Guest','Unnamed: 7':'Win_or_Lose','Unnamed: 8':'OverTime'}, inplace=True)\n",
    "        df[to_num_list] = df[to_num_list].apply(pd.to_numeric)\n",
    "        seasons[year][name] = df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>G</th>\n",
       "      <th>Date</th>\n",
       "      <th>Start (ET)</th>\n",
       "      <th>Guest</th>\n",
       "      <th>Opponent</th>\n",
       "      <th>Win_or_Lose</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Opp</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>Streak</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20141028</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Houston_Rockets</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>L 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20141029</td>\n",
       "      <td>10:00p</td>\n",
       "      <td>@</td>\n",
       "      <td>Phoenix_Suns</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>L 2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>20141031</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Los_Angeles_Clippers</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>L 3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>20141101</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>@</td>\n",
       "      <td>Golden_State_Warriors</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>L 4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>20141104</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Phoenix_Suns</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>L 5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>20141109</td>\n",
       "      <td>9:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charlotte_Hornets</td>\n",
       "      <td>W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>W 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20141111</td>\n",
       "      <td>8:00p</td>\n",
       "      <td>@</td>\n",
       "      <td>Memphis_Grizzlies</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102</td>\n",
       "      <td>107</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>L 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>20141112</td>\n",
       "      <td>8:00p</td>\n",
       "      <td>@</td>\n",
       "      <td>New_Orleans_Pelicans</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>L 2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>20141114</td>\n",
       "      <td>10:00p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San_Antonio_Spurs</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>L 3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>20141116</td>\n",
       "      <td>9:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Golden_State_Warriors</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>L 4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    G      Date Start (ET) Guest               Opponent Win_or_Lose OverTime  \\\n",
       "0   1  20141028     10:30p   NaN        Houston_Rockets           L      NaN   \n",
       "1   2  20141029     10:00p     @           Phoenix_Suns           L      NaN   \n",
       "2   3  20141031     10:30p   NaN   Los_Angeles_Clippers           L      NaN   \n",
       "3   4  20141101     10:30p     @  Golden_State_Warriors           L      NaN   \n",
       "4   5  20141104     10:30p   NaN           Phoenix_Suns           L      NaN   \n",
       "5   6  20141109      9:30p   NaN      Charlotte_Hornets           W      NaN   \n",
       "6   7  20141111      8:00p     @      Memphis_Grizzlies           L      NaN   \n",
       "7   8  20141112      8:00p     @   New_Orleans_Pelicans           L      NaN   \n",
       "8   9  20141114     10:00p   NaN      San_Antonio_Spurs           L      NaN   \n",
       "9  10  20141116      9:30p   NaN  Golden_State_Warriors           L      NaN   \n",
       "\n",
       "    Tm  Opp  W  L Streak Notes  \n",
       "0   90  108  0  1    L 1   NaN  \n",
       "1   99  119  0  2    L 2   NaN  \n",
       "2  111  118  0  3    L 3   NaN  \n",
       "3  104  127  0  4    L 4   NaN  \n",
       "4  106  112  0  5    L 5   NaN  \n",
       "5  107   92  1  5    W 1   NaN  \n",
       "6  102  107  1  6    L 1   NaN  \n",
       "7  102  109  1  7    L 2   NaN  \n",
       "8   80   93  1  8    L 3   NaN  \n",
       "9  115  136  1  9    L 4   NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons[2015]['Los_Angeles_Lakers'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the dictionary generated above, we combine a team's 5 year schedules together and change \"Win_or_Lose\" coulmn to bool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = range(2015,2019)\n",
    "team_schedules = {}\n",
    "to_num_list = ['G', 'W', 'L', 'Tm', 'Opp']\n",
    "for name, add in teams_d.items():\n",
    "    team_schedules[name] = pd.DataFrame()\n",
    "    for year in years:\n",
    "        url = 'https://www.basketball-reference.com/teams/' + add + '/' + str(year) + '_games.html'\n",
    "        html = requests.get(url).content\n",
    "        df_list = pd.read_html(html)\n",
    "        df = df_list[0]\n",
    "        df.drop(df[df.G=='G'].index, inplace=True)\n",
    "        df.reset_index(drop=True, inplace=True)\n",
    "        df['Opponent'] = df.Opponent.map(lambda x: '_'.join(x.split(' ')))\n",
    "        df['Date'] = df.Date.map(lambda x: ''.join(str(pd.to_datetime(x)).split(' ')[0].split('-')))\n",
    "        df.drop(df.columns[[3, 4]], axis=1, inplace=True)\n",
    "        df.rename(columns = {'Unnamed: 5':'Guest','Unnamed: 7':'Win_or_Lose','Unnamed: 8':'OverTime'}, inplace=True)\n",
    "        df[to_num_list] = df[to_num_list].apply(pd.to_numeric)\n",
    "        team_schedules[name] = team_schedules[name].append(df, ignore_index=True, sort=False)\n",
    "    team_schedules[name]['Win_or_Lose'] = team_schedules[name]['Win_or_Lose'].apply(lambda x: 0 if x=='L' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(328, 13)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_schedules['Los_Angeles_Lakers'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>G</th>\n",
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       "      <th>Guest</th>\n",
       "      <th>Opponent</th>\n",
       "      <th>Win_or_Lose</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Opp</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>Streak</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20141028</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Houston_Rockets</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>L 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20141029</td>\n",
       "      <td>10:00p</td>\n",
       "      <td>@</td>\n",
       "      <td>Phoenix_Suns</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>L 2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>20141031</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Los_Angeles_Clippers</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>L 3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>20141101</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>@</td>\n",
       "      <td>Golden_State_Warriors</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>L 4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>20141104</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Phoenix_Suns</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>L 5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>20141109</td>\n",
       "      <td>9:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charlotte_Hornets</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>W 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20141111</td>\n",
       "      <td>8:00p</td>\n",
       "      <td>@</td>\n",
       "      <td>Memphis_Grizzlies</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102</td>\n",
       "      <td>107</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>L 1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>20141112</td>\n",
       "      <td>8:00p</td>\n",
       "      <td>@</td>\n",
       "      <td>New_Orleans_Pelicans</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>L 2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>20141114</td>\n",
       "      <td>10:00p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San_Antonio_Spurs</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>L 3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>20141116</td>\n",
       "      <td>9:30p</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Golden_State_Warriors</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>L 4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    G      Date Start (ET) Guest               Opponent  Win_or_Lose OverTime  \\\n",
       "0   1  20141028     10:30p   NaN        Houston_Rockets            0      NaN   \n",
       "1   2  20141029     10:00p     @           Phoenix_Suns            0      NaN   \n",
       "2   3  20141031     10:30p   NaN   Los_Angeles_Clippers            0      NaN   \n",
       "3   4  20141101     10:30p     @  Golden_State_Warriors            0      NaN   \n",
       "4   5  20141104     10:30p   NaN           Phoenix_Suns            0      NaN   \n",
       "5   6  20141109      9:30p   NaN      Charlotte_Hornets            1      NaN   \n",
       "6   7  20141111      8:00p     @      Memphis_Grizzlies            0      NaN   \n",
       "7   8  20141112      8:00p     @   New_Orleans_Pelicans            0      NaN   \n",
       "8   9  20141114     10:00p   NaN      San_Antonio_Spurs            0      NaN   \n",
       "9  10  20141116      9:30p   NaN  Golden_State_Warriors            0      NaN   \n",
       "\n",
       "    Tm  Opp  W  L Streak Notes  \n",
       "0   90  108  0  1    L 1   NaN  \n",
       "1   99  119  0  2    L 2   NaN  \n",
       "2  111  118  0  3    L 3   NaN  \n",
       "3  104  127  0  4    L 4   NaN  \n",
       "4  106  112  0  5    L 5   NaN  \n",
       "5  107   92  1  5    W 1   NaN  \n",
       "6  102  107  1  6    L 1   NaN  \n",
       "7  102  109  1  7    L 2   NaN  \n",
       "8   80   93  1  8    L 3   NaN  \n",
       "9  115  136  1  9    L 4   NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_schedules['Los_Angeles_Lakers'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can depend whether a team in one game is Home or Visitor like this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons[2019]['Los_Angeles_Lakers'].iloc[0].Guest=='@'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get Box Score Stats per game, which have all player's performance data and game results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "games = {}\n",
    "for i in range(82):\n",
    "    team = 'Los_Angeles_Lakers'\n",
    "    year = 2019\n",
    "    date = seasons[year][team].iloc[i].Date\n",
    "    guest = seasons[year][team].iloc[i].Guest=='@'\n",
    "    opponent = seasons[year][team].iloc[i].Opponent\n",
    "    if guest:\n",
    "        url = 'https://www.basketball-reference.com/boxscores/'+str(date)+str(0)+teams_d[opponent]+'.html'\n",
    "        html = requests.get(url).content\n",
    "        df_list = pd.read_html(html)\n",
    "        df1 = df_list[0]\n",
    "        df2 = df_list[1]\n",
    "        games[i] = pd.concat([df1, df2], axis=1)\n",
    "    else:\n",
    "        url = 'https://www.basketball-reference.com/boxscores/'+str(date)+str(0)+teams_d[team]+'.html'\n",
    "        html = requests.get(url).content\n",
    "        df_list = pd.read_html(html)\n",
    "        df1 = df_list[2]\n",
    "        df2 = df_list[3]\n",
    "        games[i] = pd.concat([df1, df2], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Glossary:\n",
    "    MP -- Minutes Played  FG -- Field Goals  FGA -- Field Goal Attempts  FG% -- Field Goal Percentage\n",
    "    3P -- 3-Point Field Goals  3PA -- 3-Point Field Goal Attempts  3P% -- 3-Point Field Goal Percentage\n",
    "    FT -- Free Throws  FTA -- Free Throw Attempts  FT% -- Free Throw Percentage  ORB -- Offensive Rebounds\n",
    "    DRB -- Defensive Rebounds  TRB -- Total Rebounds  AST -- Assists  STL -- Steals  BLK -- Blocks  \n",
    "    TOV -- Turnovers  PF -- Personal Fouls  PTS -- Points  +/- -- Plus/Minus  MP -- Minutes Played\n",
    "    TS% -- True Shooting Percentage  A measure of shooting efficiency that takes into account 2-point field goals, 3-point field goals, and free throws.\n",
    "    eFG% -- Effective Field Goal Percentage  This statistic adjusts for the fact that a 3-point field goal is worth one more point than a 2-point field goal.\n",
    "    3PAr -- 3-Point Attempt Rate  Percentage of FG Attempts from 3-Point Range\n",
    "    FTr -- Free Throw Attempt Rate Number of FT Attempts Per FG Attempt\n",
    "    ORB% -- Offensive Rebound Percentage  An estimate of the percentage of available offensive rebounds a player grabbed while he was on the floor.\n",
    "    DRB% -- Defensive Rebound Percentage  An estimate of the percentage of available defensive rebounds a player grabbed while he was on the floor.\n",
    "    TRB% -- Total Rebound Percentage  An estimate of the percentage of available rebounds a player grabbed while he was on the floor.\n",
    "    AST% -- Assist Percentage  An estimate of the percentage of teammate field goals a player assisted while he was on the floor.\n",
    "    STL% -- Steal Percentage  An estimate of the percentage of opponent possessions that end with a steal by the player while he was on the floor.\n",
    "    BLK% -- Block Percentage  An estimate of the percentage of opponent two-point field goal attempts blocked by the player while he was on the floor.\n",
    "    TOV% -- Turnover Percentage  An estimate of turnovers committed per 100 plays.\n",
    "    USG% -- Usage Percentage  An estimate of the percentage of team plays used by a player while he was on the floor.\n",
    "    ORtg -- Offensive Rating  An estimate of points produced (players) or scored (teams) per 100 possessions\n",
    "    DRtg -- Defensive Rating  An estimate of points allowed per 100 possessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0_level_0</th>\n",
       "      <th colspan=\"9\" halign=\"left\">Basic Box Score Stats</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Advanced Box Score Stats</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Starters</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FT</th>\n",
       "      <th>FTA</th>\n",
       "      <th>...</th>\n",
       "      <th>ORB%</th>\n",
       "      <th>DRB%</th>\n",
       "      <th>TRB%</th>\n",
       "      <th>AST%</th>\n",
       "      <th>STL%</th>\n",
       "      <th>BLK%</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>ORtg</th>\n",
       "      <th>DRtg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>34:40</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>.588</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>.200</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.4</td>\n",
       "      <td>17.7</td>\n",
       "      <td>47.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>28.1</td>\n",
       "      <td>136</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kyle Kuzma</td>\n",
       "      <td>34:17</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>.444</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>.333</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>108</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lonzo Ball</td>\n",
       "      <td>33:15</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>.500</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>.400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>29.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.1</td>\n",
       "      <td>16.3</td>\n",
       "      <td>110</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Josh Hart</td>\n",
       "      <td>31:55</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>.300</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>.167</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.7</td>\n",
       "      <td>19.1</td>\n",
       "      <td>89</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JaVale McGee</td>\n",
       "      <td>31:43</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>.625</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>17.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>9.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>142</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Reserves</td>\n",
       "      <td>MP</td>\n",
       "      <td>FG</td>\n",
       "      <td>FGA</td>\n",
       "      <td>FG%</td>\n",
       "      <td>3P</td>\n",
       "      <td>3PA</td>\n",
       "      <td>3P%</td>\n",
       "      <td>FT</td>\n",
       "      <td>FTA</td>\n",
       "      <td>...</td>\n",
       "      <td>ORB%</td>\n",
       "      <td>DRB%</td>\n",
       "      <td>TRB%</td>\n",
       "      <td>AST%</td>\n",
       "      <td>STL%</td>\n",
       "      <td>BLK%</td>\n",
       "      <td>TOV%</td>\n",
       "      <td>USG%</td>\n",
       "      <td>ORtg</td>\n",
       "      <td>DRtg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lance Stephenson</td>\n",
       "      <td>24:45</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>.455</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>.667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>20.2</td>\n",
       "      <td>118</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kentavious Caldwell-Pope</td>\n",
       "      <td>22:01</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>.400</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>.500</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.4</td>\n",
       "      <td>14.9</td>\n",
       "      <td>97</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Johnathan Williams</td>\n",
       "      <td>12:28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>82</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sviatoslav Mykhailiuk</td>\n",
       "      <td>12:24</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>.667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>151</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Michael Beasley</td>\n",
       "      <td>2:32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ivica Zubac</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>...</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rajon Rondo</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>...</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Brandon Ingram</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>Not With Team</td>\n",
       "      <td>...</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "      <td>Did Not Play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Team Totals</td>\n",
       "      <td>240</td>\n",
       "      <td>46</td>\n",
       "      <td>92</td>\n",
       "      <td>.500</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>.345</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>20.9</td>\n",
       "      <td>76.7</td>\n",
       "      <td>48.8</td>\n",
       "      <td>58.7</td>\n",
       "      <td>12.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>11.3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>114.4</td>\n",
       "      <td>107.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0_level_0 Basic Box Score Stats                 \\\n",
       "                    Starters                    MP             FG   \n",
       "0               LeBron James                 34:40             10   \n",
       "1                 Kyle Kuzma                 34:17              8   \n",
       "2                 Lonzo Ball                 33:15              5   \n",
       "3                  Josh Hart                 31:55              3   \n",
       "4               JaVale McGee                 31:43             10   \n",
       "5                   Reserves                    MP             FG   \n",
       "6           Lance Stephenson                 24:45              5   \n",
       "7   Kentavious Caldwell-Pope                 22:01              2   \n",
       "8         Johnathan Williams                 12:28              1   \n",
       "9      Sviatoslav Mykhailiuk                 12:24              2   \n",
       "10           Michael Beasley                  2:32              0   \n",
       "11               Ivica Zubac          Did Not Play   Did Not Play   \n",
       "12               Rajon Rondo         Not With Team  Not With Team   \n",
       "13            Brandon Ingram         Not With Team  Not With Team   \n",
       "14               Team Totals                   240             46   \n",
       "\n",
       "                                                                               \\\n",
       "              FGA            FG%             3P            3PA            3P%   \n",
       "0              17           .588              1              5           .200   \n",
       "1              18           .444              2              6           .333   \n",
       "2              10           .500              2              5           .400   \n",
       "3              10           .300              1              6           .167   \n",
       "4              16           .625              0              0            NaN   \n",
       "5             FGA            FG%             3P            3PA            3P%   \n",
       "6              11           .455              2              3           .667   \n",
       "7               5           .400              2              4           .500   \n",
       "8               2           .500              0              0            NaN   \n",
       "9               3           .667              0              0            NaN   \n",
       "10              0            NaN              0              0            NaN   \n",
       "11   Did Not Play   Did Not Play   Did Not Play   Did Not Play   Did Not Play   \n",
       "12  Not With Team  Not With Team  Not With Team  Not With Team  Not With Team   \n",
       "13  Not With Team  Not With Team  Not With Team  Not With Team  Not With Team   \n",
       "14             92           .500             10             29           .345   \n",
       "\n",
       "                                  ... Advanced Box Score Stats                 \\\n",
       "               FT            FTA  ...                     ORB%           DRB%   \n",
       "0               7             10  ...                      0.0           35.4   \n",
       "1               4              4  ...                      0.0            9.8   \n",
       "2               0              0  ...                      0.0           20.1   \n",
       "3               5              6  ...                      0.0           17.5   \n",
       "4               1              1  ...                     17.6            7.0   \n",
       "5              FT            FTA  ...                     ORB%           DRB%   \n",
       "6               0              0  ...                      9.0            9.0   \n",
       "7               2              2  ...                      0.0           10.1   \n",
       "8               0              0  ...                      9.0            9.0   \n",
       "9               0              0  ...                      9.0            9.0   \n",
       "10              0              0  ...                      0.0            0.0   \n",
       "11   Did Not Play   Did Not Play  ...            Not With Team  Not With Team   \n",
       "12  Not With Team  Not With Team  ...            Not With Team  Not With Team   \n",
       "13  Not With Team  Not With Team  ...             Did Not Play   Did Not Play   \n",
       "14             19             23  ...                     20.9           76.7   \n",
       "\n",
       "                                                                               \\\n",
       "             TRB%           AST%           STL%           BLK%           TOV%   \n",
       "0            17.7           47.4            3.9            0.0            8.5   \n",
       "1             4.9            0.0            1.3            4.1            4.8   \n",
       "2            10.1           29.8            6.8            0.0           23.1   \n",
       "3             8.7            3.6            2.8            0.0           13.7   \n",
       "4            12.3            9.8            1.4            2.2            0.0   \n",
       "5            TRB%           AST%           STL%           BLK%           TOV%   \n",
       "6             9.0           21.4            1.8            0.0            8.3   \n",
       "7             5.1            0.0            0.0            0.0           25.4   \n",
       "8             9.0            0.0            0.0            0.0           33.3   \n",
       "9             9.0           10.1            0.0            0.0            0.0   \n",
       "10            0.0            0.0            0.0            0.0          100.0   \n",
       "11  Not With Team  Not With Team  Not With Team  Not With Team  Not With Team   \n",
       "12  Not With Team  Not With Team  Not With Team  Not With Team  Not With Team   \n",
       "13   Did Not Play   Did Not Play   Did Not Play   Did Not Play   Did Not Play   \n",
       "14           48.8           58.7           12.3            4.3           11.3   \n",
       "\n",
       "                                                 \n",
       "             USG%           ORtg           DRtg  \n",
       "0            28.1            136            101  \n",
       "1            25.2            108            109  \n",
       "2            16.3            110             98  \n",
       "3            19.1             89            107  \n",
       "4            21.6            142            110  \n",
       "5            USG%           ORtg           DRtg  \n",
       "6            20.2            118            111  \n",
       "7            14.9             97            115  \n",
       "8            10.0             82            115  \n",
       "9            10.1            151            115  \n",
       "10           16.5              0            116  \n",
       "11  Not With Team  Not With Team  Not With Team  \n",
       "12  Not With Team  Not With Team  Not With Team  \n",
       "13   Did Not Play   Did Not Play   Did Not Play  \n",
       "14          100.0          114.4          107.8  \n",
       "\n",
       "[15 rows x 37 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get all  Box Score Stats category by team from Season 2015 to Season 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if use_intermediate_results == False:\n",
    "    game_results = {}\n",
    "    for team, add in teams_d.items():\n",
    "        game_results[team] = pd.DataFrame()\n",
    "        for i in range(len(team_schedules[team])):        \n",
    "            date = team_schedules[team].iloc[i].Date\n",
    "            guest = team_schedules[team].iloc[i].Guest=='@'\n",
    "            opponent = team_schedules[team].iloc[i].Opponent\n",
    "            if guest:\n",
    "                url = 'https://www.basketball-reference.com/boxscores/'+str(date)+str(0)+teams_d[opponent]+'.html'\n",
    "                html = requests.get(url).content\n",
    "                df_list = pd.read_html(html)\n",
    "                df = pd.concat([df_list[0], df_list[1]], axis=1)\n",
    "            else:\n",
    "                url = 'https://www.basketball-reference.com/boxscores/'+str(date)+str(0)+teams_d[team]+'.html'\n",
    "                html = requests.get(url).content\n",
    "                df_list = pd.read_html(html)\n",
    "                df = pd.concat([df_list[2], df_list[3]], axis=1)\n",
    "            df.columns = df.columns.droplevel() \n",
    "            df = df.loc[:,~df.columns.duplicated()]\n",
    "            df = df[df.Starters=='Team Totals']\n",
    "            df.drop(['MP', '+/-', 'USG%'], axis=1, inplace=True)\n",
    "            df['Date'] = date\n",
    "            game_results[team] = game_results[team].append(df, ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_intermediate_results == False:\n",
    "    for team, df in game_results.items():\n",
    "        di = './game_results/' + str(team) + '.pkl'\n",
    "        df.to_pickle(di)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_intermediate_results == True:\n",
    "    game_results = {}\n",
    "    for team in teams_d:\n",
    "        di = './game_results/' + str(team) + '.pkl'\n",
    "        game_results[team] = pd.read_pickle(di)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Starters</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FT</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT%</th>\n",
       "      <th>...</th>\n",
       "      <th>ORB%</th>\n",
       "      <th>DRB%</th>\n",
       "      <th>TRB%</th>\n",
       "      <th>AST%</th>\n",
       "      <th>STL%</th>\n",
       "      <th>BLK%</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>ORtg</th>\n",
       "      <th>DRtg</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Team Totals</td>\n",
       "      <td>28</td>\n",
       "      <td>79</td>\n",
       "      <td>.354</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>.300</td>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>.795</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>64.1</td>\n",
       "      <td>43.4</td>\n",
       "      <td>57.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>10.3</td>\n",
       "      <td>99.0</td>\n",
       "      <td>118.8</td>\n",
       "      <td>20141028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Team Totals</td>\n",
       "      <td>37</td>\n",
       "      <td>86</td>\n",
       "      <td>.430</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>.308</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>.840</td>\n",
       "      <td>...</td>\n",
       "      <td>27.9</td>\n",
       "      <td>74.3</td>\n",
       "      <td>48.7</td>\n",
       "      <td>54.1</td>\n",
       "      <td>9.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>105.7</td>\n",
       "      <td>127.0</td>\n",
       "      <td>20141029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Team Totals</td>\n",
       "      <td>42</td>\n",
       "      <td>82</td>\n",
       "      <td>.512</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>.350</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>.714</td>\n",
       "      <td>...</td>\n",
       "      <td>23.1</td>\n",
       "      <td>83.8</td>\n",
       "      <td>52.6</td>\n",
       "      <td>57.1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>12.9</td>\n",
       "      <td>114.7</td>\n",
       "      <td>122.0</td>\n",
       "      <td>20141031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Team Totals</td>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "      <td>.466</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>.286</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>.720</td>\n",
       "      <td>...</td>\n",
       "      <td>35.4</td>\n",
       "      <td>62.5</td>\n",
       "      <td>46.3</td>\n",
       "      <td>51.2</td>\n",
       "      <td>8.9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>103.2</td>\n",
       "      <td>126.0</td>\n",
       "      <td>20141101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Team Totals</td>\n",
       "      <td>35</td>\n",
       "      <td>88</td>\n",
       "      <td>.398</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>.375</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "      <td>.732</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6</td>\n",
       "      <td>70.5</td>\n",
       "      <td>48.5</td>\n",
       "      <td>45.7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>106.5</td>\n",
       "      <td>112.5</td>\n",
       "      <td>20141104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Team Totals</td>\n",
       "      <td>42</td>\n",
       "      <td>82</td>\n",
       "      <td>.512</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>.353</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>.773</td>\n",
       "      <td>...</td>\n",
       "      <td>22.9</td>\n",
       "      <td>71.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>66.7</td>\n",
       "      <td>8.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>119.3</td>\n",
       "      <td>102.6</td>\n",
       "      <td>20141109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Team Totals</td>\n",
       "      <td>41</td>\n",
       "      <td>87</td>\n",
       "      <td>.471</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>.318</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>.765</td>\n",
       "      <td>...</td>\n",
       "      <td>36.6</td>\n",
       "      <td>65.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>53.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>115.5</td>\n",
       "      <td>121.2</td>\n",
       "      <td>20141111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Team Totals</td>\n",
       "      <td>35</td>\n",
       "      <td>84</td>\n",
       "      <td>.417</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>.333</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>.781</td>\n",
       "      <td>...</td>\n",
       "      <td>23.4</td>\n",
       "      <td>77.1</td>\n",
       "      <td>46.3</td>\n",
       "      <td>42.9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>109.3</td>\n",
       "      <td>116.8</td>\n",
       "      <td>20141112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Team Totals</td>\n",
       "      <td>28</td>\n",
       "      <td>75</td>\n",
       "      <td>.373</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>.167</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>.710</td>\n",
       "      <td>...</td>\n",
       "      <td>22.9</td>\n",
       "      <td>83.3</td>\n",
       "      <td>51.1</td>\n",
       "      <td>60.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.8</td>\n",
       "      <td>12.8</td>\n",
       "      <td>89.6</td>\n",
       "      <td>104.2</td>\n",
       "      <td>20141114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Team Totals</td>\n",
       "      <td>39</td>\n",
       "      <td>99</td>\n",
       "      <td>.394</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>.188</td>\n",
       "      <td>34</td>\n",
       "      <td>43</td>\n",
       "      <td>.791</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3</td>\n",
       "      <td>71.8</td>\n",
       "      <td>48.5</td>\n",
       "      <td>43.6</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>112.0</td>\n",
       "      <td>132.5</td>\n",
       "      <td>20141116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Starters  FG FGA   FG% 3P 3PA   3P%  FT FTA   FT%  ...  ORB%  DRB%  \\\n",
       "0  Team Totals  28  79  .354  3  10  .300  31  39  .795  ...  25.0  64.1   \n",
       "1  Team Totals  37  86  .430  4  13  .308  21  25  .840  ...  27.9  74.3   \n",
       "2  Team Totals  42  82  .512  7  20  .350  20  28  .714  ...  23.1  83.8   \n",
       "3  Team Totals  41  88  .466  4  14  .286  18  25  .720  ...  35.4  62.5   \n",
       "4  Team Totals  35  88  .398  6  16  .375  30  41  .732  ...  31.6  70.5   \n",
       "5  Team Totals  42  82  .512  6  17  .353  17  22  .773  ...  22.9  71.4   \n",
       "6  Team Totals  41  87  .471  7  22  .318  13  17  .765  ...  36.6  65.7   \n",
       "7  Team Totals  35  84  .417  7  21  .333  25  32  .781  ...  23.4  77.1   \n",
       "8  Team Totals  28  75  .373  2  12  .167  22  31  .710  ...  22.9  83.3   \n",
       "9  Team Totals  39  99  .394  3  16  .188  34  43  .791  ...  33.3  71.8   \n",
       "\n",
       "   TRB%  AST% STL%  BLK%  TOV%   ORtg   DRtg      Date  \n",
       "0  43.4  57.1  7.7   6.8  10.3   99.0  118.8  20141028  \n",
       "1  48.7  54.1  9.6   6.0  11.8  105.7  127.0  20141029  \n",
       "2  52.6  57.1  5.2   5.3  12.9  114.7  122.0  20141031  \n",
       "3  46.3  51.2  8.9   5.0  18.2  103.2  126.0  20141101  \n",
       "4  48.5  45.7  7.0  11.0  10.2  106.5  112.5  20141104  \n",
       "5  49.4  66.7  8.9   3.1  10.7  119.3  102.6  20141109  \n",
       "6  50.0  53.7  9.1   9.0  12.1  115.5  121.2  20141111  \n",
       "7  46.3  42.9  8.6   7.9   8.4  109.3  116.8  20141112  \n",
       "8  51.1  60.7  6.7   8.8  12.8   89.6  104.2  20141114  \n",
       "9  48.5  43.6  8.8   6.9   5.6  112.0  132.5  20141116  \n",
       "\n",
       "[10 rows x 33 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_results['Los_Angeles_Lakers'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x135dd1e80>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pd.to_numeric(game_results['Los_Angeles_Lakers']['ORtg']))\n",
    "plt.plot(pd.to_numeric(game_results['Golden_State_Warriors']['ORtg']))\n",
    "plt.ylabel('Offensive Rating')\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "plt.legend(['Los_Angeles_Lakers','Golden_State_Warriors'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From plot above we can see Warriors's Offensive Rating is mainly higher than Lakers, and we know the Warriors's team record is much better than Lakers recent years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get all Box game schedules from Season 2015 to Season 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedules = pd.DataFrame()\n",
    "for year in [2015,2016,2017,2018]:\n",
    "    for month in ['october', 'november', 'december', 'january', 'february', 'march', 'april', 'may', 'june']:\n",
    "        url = 'https://www.basketball-reference.com/leagues/NBA_'+ str(year) +'_games-'+ month +'.html'\n",
    "        html = requests.get(url).content\n",
    "        df_list = pd.read_html(html)\n",
    "        df = df_list[0]\n",
    "        if 'Playoffs' in list(df.Date):\n",
    "            df.drop(df[df.Date=='Playoffs'].index, inplace=True)\n",
    "        df.drop(df[df.Date=='Date'].index, inplace=True)\n",
    "        df.drop(df.columns[[6, 9]], axis=1, inplace=True)\n",
    "        df['Date'] = df.Date.map(lambda x: ''.join(str(pd.to_datetime(x)).split(' ')[0].split('-')))\n",
    "        df.rename(columns = {'Unnamed: 7':'OverTime'}, inplace=True)\n",
    "        df.reset_index(drop=True, inplace=True)\n",
    "        df['Visitor/Neutral'] = df['Visitor/Neutral'].map(lambda x: '_'.join(x.split(' ')))\n",
    "        df['Home/Neutral'] = df['Home/Neutral'].map(lambda x: '_'.join(x.split(' ')))\n",
    "        schedules = schedules.append(df, ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5248, 8)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedules.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Start (ET)</th>\n",
       "      <th>Visitor/Neutral</th>\n",
       "      <th>PTS</th>\n",
       "      <th>Home/Neutral</th>\n",
       "      <th>PTS.1</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>Attend.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20141028</td>\n",
       "      <td>10:30p</td>\n",
       "      <td>Houston_Rockets</td>\n",
       "      <td>108</td>\n",
       "      <td>Los_Angeles_Lakers</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20141028</td>\n",
       "      <td>8:00p</td>\n",
       "      <td>Orlando_Magic</td>\n",
       "      <td>84</td>\n",
       "      <td>New_Orleans_Pelicans</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20141028</td>\n",
       "      <td>8:00p</td>\n",
       "      <td>Dallas_Mavericks</td>\n",
       "      <td>100</td>\n",
       "      <td>San_Antonio_Spurs</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20141029</td>\n",
       "      <td>7:30p</td>\n",
       "      <td>Brooklyn_Nets</td>\n",
       "      <td>105</td>\n",
       "      <td>Boston_Celtics</td>\n",
       "      <td>121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20141029</td>\n",
       "      <td>7:00p</td>\n",
       "      <td>Milwaukee_Bucks</td>\n",
       "      <td>106</td>\n",
       "      <td>Charlotte_Hornets</td>\n",
       "      <td>108</td>\n",
       "      <td>OT</td>\n",
       "      <td>19439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20141029</td>\n",
       "      <td>9:00p</td>\n",
       "      <td>Detroit_Pistons</td>\n",
       "      <td>79</td>\n",
       "      <td>Denver_Nuggets</td>\n",
       "      <td>89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20141029</td>\n",
       "      <td>7:00p</td>\n",
       "      <td>Philadelphia_76ers</td>\n",
       "      <td>91</td>\n",
       "      <td>Indiana_Pacers</td>\n",
       "      <td>103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20141029</td>\n",
       "      <td>8:00p</td>\n",
       "      <td>Minnesota_Timberwolves</td>\n",
       "      <td>101</td>\n",
       "      <td>Memphis_Grizzlies</td>\n",
       "      <td>105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20141029</td>\n",
       "      <td>7:30p</td>\n",
       "      <td>Washington_Wizards</td>\n",
       "      <td>95</td>\n",
       "      <td>Miami_Heat</td>\n",
       "      <td>107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20141029</td>\n",
       "      <td>8:00p</td>\n",
       "      <td>Chicago_Bulls</td>\n",
       "      <td>104</td>\n",
       "      <td>New_York_Knicks</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date Start (ET)         Visitor/Neutral  PTS          Home/Neutral  \\\n",
       "0  20141028     10:30p         Houston_Rockets  108    Los_Angeles_Lakers   \n",
       "1  20141028      8:00p           Orlando_Magic   84  New_Orleans_Pelicans   \n",
       "2  20141028      8:00p        Dallas_Mavericks  100     San_Antonio_Spurs   \n",
       "3  20141029      7:30p           Brooklyn_Nets  105        Boston_Celtics   \n",
       "4  20141029      7:00p         Milwaukee_Bucks  106     Charlotte_Hornets   \n",
       "5  20141029      9:00p         Detroit_Pistons   79        Denver_Nuggets   \n",
       "6  20141029      7:00p      Philadelphia_76ers   91        Indiana_Pacers   \n",
       "7  20141029      8:00p  Minnesota_Timberwolves  101     Memphis_Grizzlies   \n",
       "8  20141029      7:30p      Washington_Wizards   95            Miami_Heat   \n",
       "9  20141029      8:00p           Chicago_Bulls  104       New_York_Knicks   \n",
       "\n",
       "  PTS.1 OverTime Attend.  \n",
       "0    90      NaN   18997  \n",
       "1   101      NaN   17097  \n",
       "2   101      NaN   19615  \n",
       "3   121      NaN   18624  \n",
       "4   108       OT   19439  \n",
       "5    89      NaN   17136  \n",
       "6   103      NaN   18165  \n",
       "7   105      NaN   17731  \n",
       "8   107      NaN   19744  \n",
       "9    80      NaN   19812  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schedules.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get mean of 10 recent game results before the game we analyse as the base data to predict the result of this game. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "if use_intermediate_results == False:\n",
    "    predic_win = pd.DataFrame()\n",
    "    for i in range(200,5247):\n",
    "        date = schedules.iloc[i].Date\n",
    "        visitor = schedules.iloc[i]['Visitor/Neutral']\n",
    "        home = schedules.iloc[i]['Home/Neutral']\n",
    "        idx_1 = team_schedules[visitor][team_schedules[visitor]['Date']== str(date)].index\n",
    "        if idx_1.values:\n",
    "            idx_v = idx_1[0]\n",
    "            df = game_results[visitor].iloc[idx_v-10:idx_v].drop('Date', axis=1)\n",
    "            d = {}\n",
    "            for col_name in df.columns[1:]:\n",
    "                v_name = col_name + str('_v')\n",
    "                d[v_name] = pd.to_numeric(df[col_name]).mean()\n",
    "            d['wn_v'] = sum(team_schedules[visitor].iloc[idx_v-10:idx_v]['Win_or_Lose'])   \n",
    "            idx_2 = team_schedules[home][team_schedules[home]['Date']== str(date)].index\n",
    "        # if idx_2.values:\n",
    "            idx_v = idx_2[0]\n",
    "            df = game_results[home].iloc[idx_v-10:idx_v].drop('Date', axis=1)\n",
    "            for col_name in df.columns[1:]:\n",
    "                h_name = col_name + str('_h')\n",
    "                d[h_name] = pd.to_numeric(df[col_name]).mean()\n",
    "            d['wn_h'] = sum(team_schedules[home].iloc[idx_v-10:idx_v]['Win_or_Lose'])  \n",
    "            d['home_win_score'] = int(schedules.iloc[i]['PTS.1']) - int(schedules.iloc[i]['PTS'])\n",
    "            d['home'] = home\n",
    "            d['visitor'] = visitor\n",
    "            predic_win = predic_win.append(pd.DataFrame([d]), ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_intermediate_results == False:\n",
    "    predic_win.to_pickle('predic_win.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_intermediate_results == True:\n",
    "    predic_win = pd.read_pickle(\"./predic_win.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4720, 67)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predic_win.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3P%_h</th>\n",
       "      <th>3P%_v</th>\n",
       "      <th>3PA_h</th>\n",
       "      <th>3PA_v</th>\n",
       "      <th>3PAr_h</th>\n",
       "      <th>3PAr_v</th>\n",
       "      <th>3P_h</th>\n",
       "      <th>3P_v</th>\n",
       "      <th>AST%_h</th>\n",
       "      <th>AST%_v</th>\n",
       "      <th>...</th>\n",
       "      <th>TRB_v</th>\n",
       "      <th>TS%_h</th>\n",
       "      <th>TS%_v</th>\n",
       "      <th>eFG%_h</th>\n",
       "      <th>eFG%_v</th>\n",
       "      <th>home</th>\n",
       "      <th>home_win_score</th>\n",
       "      <th>visitor</th>\n",
       "      <th>wn_h</th>\n",
       "      <th>wn_v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3364</td>\n",
       "      <td>0.4241</td>\n",
       "      <td>35.5</td>\n",
       "      <td>18.9</td>\n",
       "      <td>0.4560</td>\n",
       "      <td>0.2348</td>\n",
       "      <td>11.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>59.86</td>\n",
       "      <td>54.55</td>\n",
       "      <td>...</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0.5302</td>\n",
       "      <td>0.5562</td>\n",
       "      <td>0.4877</td>\n",
       "      <td>0.5217</td>\n",
       "      <td>Houston_Rockets</td>\n",
       "      <td>5</td>\n",
       "      <td>New_York_Knicks</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3075</td>\n",
       "      <td>0.4163</td>\n",
       "      <td>25.5</td>\n",
       "      <td>24.9</td>\n",
       "      <td>0.3156</td>\n",
       "      <td>0.2997</td>\n",
       "      <td>7.8</td>\n",
       "      <td>10.4</td>\n",
       "      <td>55.79</td>\n",
       "      <td>63.01</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.4844</td>\n",
       "      <td>0.5713</td>\n",
       "      <td>0.4514</td>\n",
       "      <td>0.5324</td>\n",
       "      <td>Philadelphia_76ers</td>\n",
       "      <td>-10</td>\n",
       "      <td>Portland_Trail_Blazers</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3926</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>22.8</td>\n",
       "      <td>26.3</td>\n",
       "      <td>0.2774</td>\n",
       "      <td>0.3087</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>53.75</td>\n",
       "      <td>51.72</td>\n",
       "      <td>...</td>\n",
       "      <td>42.6</td>\n",
       "      <td>0.5692</td>\n",
       "      <td>0.5605</td>\n",
       "      <td>0.5156</td>\n",
       "      <td>0.5145</td>\n",
       "      <td>Toronto_Raptors</td>\n",
       "      <td>4</td>\n",
       "      <td>Phoenix_Suns</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3452</td>\n",
       "      <td>0.3588</td>\n",
       "      <td>20.1</td>\n",
       "      <td>19.2</td>\n",
       "      <td>0.2671</td>\n",
       "      <td>0.2366</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.30</td>\n",
       "      <td>62.12</td>\n",
       "      <td>...</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.5463</td>\n",
       "      <td>0.5337</td>\n",
       "      <td>0.5063</td>\n",
       "      <td>0.4956</td>\n",
       "      <td>Utah_Jazz</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago_Bulls</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3623</td>\n",
       "      <td>0.3571</td>\n",
       "      <td>27.1</td>\n",
       "      <td>19.7</td>\n",
       "      <td>0.3183</td>\n",
       "      <td>0.2444</td>\n",
       "      <td>9.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>55.97</td>\n",
       "      <td>61.11</td>\n",
       "      <td>...</td>\n",
       "      <td>42.1</td>\n",
       "      <td>0.5456</td>\n",
       "      <td>0.5364</td>\n",
       "      <td>0.5078</td>\n",
       "      <td>0.5006</td>\n",
       "      <td>Denver_Nuggets</td>\n",
       "      <td>5</td>\n",
       "      <td>Chicago_Bulls</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.3828</td>\n",
       "      <td>0.3833</td>\n",
       "      <td>22.6</td>\n",
       "      <td>25.2</td>\n",
       "      <td>0.3153</td>\n",
       "      <td>0.3081</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.7</td>\n",
       "      <td>60.45</td>\n",
       "      <td>65.05</td>\n",
       "      <td>...</td>\n",
       "      <td>44.7</td>\n",
       "      <td>0.5717</td>\n",
       "      <td>0.5830</td>\n",
       "      <td>0.5281</td>\n",
       "      <td>0.5456</td>\n",
       "      <td>Miami_Heat</td>\n",
       "      <td>-17</td>\n",
       "      <td>Golden_State_Warriors</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.2681</td>\n",
       "      <td>0.3794</td>\n",
       "      <td>18.1</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0.2189</td>\n",
       "      <td>0.2770</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>58.92</td>\n",
       "      <td>50.73</td>\n",
       "      <td>...</td>\n",
       "      <td>45.8</td>\n",
       "      <td>0.5123</td>\n",
       "      <td>0.5020</td>\n",
       "      <td>0.4707</td>\n",
       "      <td>0.4663</td>\n",
       "      <td>Milwaukee_Bucks</td>\n",
       "      <td>12</td>\n",
       "      <td>Detroit_Pistons</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.3627</td>\n",
       "      <td>0.3411</td>\n",
       "      <td>20.5</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0.2424</td>\n",
       "      <td>0.1953</td>\n",
       "      <td>7.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>56.51</td>\n",
       "      <td>54.25</td>\n",
       "      <td>...</td>\n",
       "      <td>45.8</td>\n",
       "      <td>0.5560</td>\n",
       "      <td>0.5615</td>\n",
       "      <td>0.5166</td>\n",
       "      <td>0.4982</td>\n",
       "      <td>New_Orleans_Pelicans</td>\n",
       "      <td>-10</td>\n",
       "      <td>Sacramento_Kings</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.3376</td>\n",
       "      <td>0.3363</td>\n",
       "      <td>14.9</td>\n",
       "      <td>24.8</td>\n",
       "      <td>0.1782</td>\n",
       "      <td>0.3067</td>\n",
       "      <td>5.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>65.98</td>\n",
       "      <td>64.53</td>\n",
       "      <td>...</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.5304</td>\n",
       "      <td>0.5605</td>\n",
       "      <td>0.4934</td>\n",
       "      <td>0.5168</td>\n",
       "      <td>Washington_Wizards</td>\n",
       "      <td>-4</td>\n",
       "      <td>Atlanta_Hawks</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.3293</td>\n",
       "      <td>0.3903</td>\n",
       "      <td>25.3</td>\n",
       "      <td>21.4</td>\n",
       "      <td>0.3071</td>\n",
       "      <td>0.2598</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>61.82</td>\n",
       "      <td>52.24</td>\n",
       "      <td>...</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0.5524</td>\n",
       "      <td>0.5710</td>\n",
       "      <td>0.5075</td>\n",
       "      <td>0.5188</td>\n",
       "      <td>Atlanta_Hawks</td>\n",
       "      <td>-11</td>\n",
       "      <td>Toronto_Raptors</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    3P%_h   3P%_v  3PA_h  3PA_v  3PAr_h  3PAr_v  3P_h  3P_v  AST%_h  AST%_v  \\\n",
       "0  0.3364  0.4241   35.5   18.9  0.4560  0.2348  11.8   7.6   59.86   54.55   \n",
       "1  0.3075  0.4163   25.5   24.9  0.3156  0.2997   7.8  10.4   55.79   63.01   \n",
       "2  0.3926  0.3582   22.8   26.3  0.2774  0.3087   8.8   9.5   53.75   51.72   \n",
       "3  0.3452  0.3588   20.1   19.2  0.2671  0.2366   6.9   7.0   63.30   62.12   \n",
       "4  0.3623  0.3571   27.1   19.7  0.3183  0.2444   9.7   7.2   55.97   61.11   \n",
       "5  0.3828  0.3833   22.6   25.2  0.3153  0.3081   8.6   9.7   60.45   65.05   \n",
       "6  0.2681  0.3794   18.1   23.2  0.2189  0.2770   4.8   8.7   58.92   50.73   \n",
       "7  0.3627  0.3411   20.5   15.6  0.2424  0.1953   7.5   5.1   56.51   54.25   \n",
       "8  0.3376  0.3363   14.9   24.8  0.1782  0.3067   5.2   8.4   65.98   64.53   \n",
       "9  0.3293  0.3903   25.3   21.4  0.3071  0.2598   8.4   8.4   61.82   52.24   \n",
       "\n",
       "   ...  TRB_v   TS%_h   TS%_v  eFG%_h  eFG%_v                  home  \\\n",
       "0  ...   38.5  0.5302  0.5562  0.4877  0.5217       Houston_Rockets   \n",
       "1  ...   48.0  0.4844  0.5713  0.4514  0.5324    Philadelphia_76ers   \n",
       "2  ...   42.6  0.5692  0.5605  0.5156  0.5145       Toronto_Raptors   \n",
       "3  ...   43.0  0.5463  0.5337  0.5063  0.4956             Utah_Jazz   \n",
       "4  ...   42.1  0.5456  0.5364  0.5078  0.5006        Denver_Nuggets   \n",
       "5  ...   44.7  0.5717  0.5830  0.5281  0.5456            Miami_Heat   \n",
       "6  ...   45.8  0.5123  0.5020  0.4707  0.4663       Milwaukee_Bucks   \n",
       "7  ...   45.8  0.5560  0.5615  0.5166  0.4982  New_Orleans_Pelicans   \n",
       "8  ...   39.4  0.5304  0.5605  0.4934  0.5168    Washington_Wizards   \n",
       "9  ...   39.5  0.5524  0.5710  0.5075  0.5188         Atlanta_Hawks   \n",
       "\n",
       "   home_win_score                 visitor  wn_h  wn_v  \n",
       "0               5         New_York_Knicks     7     2  \n",
       "1             -10  Portland_Trail_Blazers     0     9  \n",
       "2               4            Phoenix_Suns     9     6  \n",
       "3              -2           Chicago_Bulls     4     6  \n",
       "4               5           Chicago_Bulls     5     6  \n",
       "5             -17   Golden_State_Warriors     5     8  \n",
       "6              12         Detroit_Pistons     5     3  \n",
       "7             -10        Sacramento_Kings     6     6  \n",
       "8              -4           Atlanta_Hawks     8     6  \n",
       "9             -11         Toronto_Raptors     6     9  \n",
       "\n",
       "[10 rows x 67 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predic_win.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "nba_df = predic_win.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3P%_h</th>\n",
       "      <th>3P%_v</th>\n",
       "      <th>3PA_h</th>\n",
       "      <th>3PA_v</th>\n",
       "      <th>3PAr_h</th>\n",
       "      <th>3PAr_v</th>\n",
       "      <th>3P_h</th>\n",
       "      <th>3P_v</th>\n",
       "      <th>AST%_h</th>\n",
       "      <th>AST%_v</th>\n",
       "      <th>...</th>\n",
       "      <th>TS%_h</th>\n",
       "      <th>TS%_v</th>\n",
       "      <th>eFG%_h</th>\n",
       "      <th>eFG%_v</th>\n",
       "      <th>home</th>\n",
       "      <th>home_win_score</th>\n",
       "      <th>visitor</th>\n",
       "      <th>wn_h</th>\n",
       "      <th>wn_v</th>\n",
       "      <th>home_win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3364</td>\n",
       "      <td>0.4241</td>\n",
       "      <td>35.5</td>\n",
       "      <td>18.9</td>\n",
       "      <td>0.4560</td>\n",
       "      <td>0.2348</td>\n",
       "      <td>11.8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>59.86</td>\n",
       "      <td>54.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5302</td>\n",
       "      <td>0.5562</td>\n",
       "      <td>0.4877</td>\n",
       "      <td>0.5217</td>\n",
       "      <td>Houston_Rockets</td>\n",
       "      <td>5</td>\n",
       "      <td>New_York_Knicks</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3075</td>\n",
       "      <td>0.4163</td>\n",
       "      <td>25.5</td>\n",
       "      <td>24.9</td>\n",
       "      <td>0.3156</td>\n",
       "      <td>0.2997</td>\n",
       "      <td>7.8</td>\n",
       "      <td>10.4</td>\n",
       "      <td>55.79</td>\n",
       "      <td>63.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4844</td>\n",
       "      <td>0.5713</td>\n",
       "      <td>0.4514</td>\n",
       "      <td>0.5324</td>\n",
       "      <td>Philadelphia_76ers</td>\n",
       "      <td>-10</td>\n",
       "      <td>Portland_Trail_Blazers</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3926</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>22.8</td>\n",
       "      <td>26.3</td>\n",
       "      <td>0.2774</td>\n",
       "      <td>0.3087</td>\n",
       "      <td>8.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>53.75</td>\n",
       "      <td>51.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5692</td>\n",
       "      <td>0.5605</td>\n",
       "      <td>0.5156</td>\n",
       "      <td>0.5145</td>\n",
       "      <td>Toronto_Raptors</td>\n",
       "      <td>4</td>\n",
       "      <td>Phoenix_Suns</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3452</td>\n",
       "      <td>0.3588</td>\n",
       "      <td>20.1</td>\n",
       "      <td>19.2</td>\n",
       "      <td>0.2671</td>\n",
       "      <td>0.2366</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.30</td>\n",
       "      <td>62.12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5463</td>\n",
       "      <td>0.5337</td>\n",
       "      <td>0.5063</td>\n",
       "      <td>0.4956</td>\n",
       "      <td>Utah_Jazz</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago_Bulls</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3623</td>\n",
       "      <td>0.3571</td>\n",
       "      <td>27.1</td>\n",
       "      <td>19.7</td>\n",
       "      <td>0.3183</td>\n",
       "      <td>0.2444</td>\n",
       "      <td>9.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>55.97</td>\n",
       "      <td>61.11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5456</td>\n",
       "      <td>0.5364</td>\n",
       "      <td>0.5078</td>\n",
       "      <td>0.5006</td>\n",
       "      <td>Denver_Nuggets</td>\n",
       "      <td>5</td>\n",
       "      <td>Chicago_Bulls</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.3828</td>\n",
       "      <td>0.3833</td>\n",
       "      <td>22.6</td>\n",
       "      <td>25.2</td>\n",
       "      <td>0.3153</td>\n",
       "      <td>0.3081</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9.7</td>\n",
       "      <td>60.45</td>\n",
       "      <td>65.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5717</td>\n",
       "      <td>0.5830</td>\n",
       "      <td>0.5281</td>\n",
       "      <td>0.5456</td>\n",
       "      <td>Miami_Heat</td>\n",
       "      <td>-17</td>\n",
       "      <td>Golden_State_Warriors</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.2681</td>\n",
       "      <td>0.3794</td>\n",
       "      <td>18.1</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0.2189</td>\n",
       "      <td>0.2770</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>58.92</td>\n",
       "      <td>50.73</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5123</td>\n",
       "      <td>0.5020</td>\n",
       "      <td>0.4707</td>\n",
       "      <td>0.4663</td>\n",
       "      <td>Milwaukee_Bucks</td>\n",
       "      <td>12</td>\n",
       "      <td>Detroit_Pistons</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.3627</td>\n",
       "      <td>0.3411</td>\n",
       "      <td>20.5</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0.2424</td>\n",
       "      <td>0.1953</td>\n",
       "      <td>7.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>56.51</td>\n",
       "      <td>54.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5560</td>\n",
       "      <td>0.5615</td>\n",
       "      <td>0.5166</td>\n",
       "      <td>0.4982</td>\n",
       "      <td>New_Orleans_Pelicans</td>\n",
       "      <td>-10</td>\n",
       "      <td>Sacramento_Kings</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.3376</td>\n",
       "      <td>0.3363</td>\n",
       "      <td>14.9</td>\n",
       "      <td>24.8</td>\n",
       "      <td>0.1782</td>\n",
       "      <td>0.3067</td>\n",
       "      <td>5.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>65.98</td>\n",
       "      <td>64.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5304</td>\n",
       "      <td>0.5605</td>\n",
       "      <td>0.4934</td>\n",
       "      <td>0.5168</td>\n",
       "      <td>Washington_Wizards</td>\n",
       "      <td>-4</td>\n",
       "      <td>Atlanta_Hawks</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.3293</td>\n",
       "      <td>0.3903</td>\n",
       "      <td>25.3</td>\n",
       "      <td>21.4</td>\n",
       "      <td>0.3071</td>\n",
       "      <td>0.2598</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>61.82</td>\n",
       "      <td>52.24</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5524</td>\n",
       "      <td>0.5710</td>\n",
       "      <td>0.5075</td>\n",
       "      <td>0.5188</td>\n",
       "      <td>Atlanta_Hawks</td>\n",
       "      <td>-11</td>\n",
       "      <td>Toronto_Raptors</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    3P%_h   3P%_v  3PA_h  3PA_v  3PAr_h  3PAr_v  3P_h  3P_v  AST%_h  AST%_v  \\\n",
       "0  0.3364  0.4241   35.5   18.9  0.4560  0.2348  11.8   7.6   59.86   54.55   \n",
       "1  0.3075  0.4163   25.5   24.9  0.3156  0.2997   7.8  10.4   55.79   63.01   \n",
       "2  0.3926  0.3582   22.8   26.3  0.2774  0.3087   8.8   9.5   53.75   51.72   \n",
       "3  0.3452  0.3588   20.1   19.2  0.2671  0.2366   6.9   7.0   63.30   62.12   \n",
       "4  0.3623  0.3571   27.1   19.7  0.3183  0.2444   9.7   7.2   55.97   61.11   \n",
       "5  0.3828  0.3833   22.6   25.2  0.3153  0.3081   8.6   9.7   60.45   65.05   \n",
       "6  0.2681  0.3794   18.1   23.2  0.2189  0.2770   4.8   8.7   58.92   50.73   \n",
       "7  0.3627  0.3411   20.5   15.6  0.2424  0.1953   7.5   5.1   56.51   54.25   \n",
       "8  0.3376  0.3363   14.9   24.8  0.1782  0.3067   5.2   8.4   65.98   64.53   \n",
       "9  0.3293  0.3903   25.3   21.4  0.3071  0.2598   8.4   8.4   61.82   52.24   \n",
       "\n",
       "   ...   TS%_h   TS%_v  eFG%_h  eFG%_v                  home  home_win_score  \\\n",
       "0  ...  0.5302  0.5562  0.4877  0.5217       Houston_Rockets               5   \n",
       "1  ...  0.4844  0.5713  0.4514  0.5324    Philadelphia_76ers             -10   \n",
       "2  ...  0.5692  0.5605  0.5156  0.5145       Toronto_Raptors               4   \n",
       "3  ...  0.5463  0.5337  0.5063  0.4956             Utah_Jazz              -2   \n",
       "4  ...  0.5456  0.5364  0.5078  0.5006        Denver_Nuggets               5   \n",
       "5  ...  0.5717  0.5830  0.5281  0.5456            Miami_Heat             -17   \n",
       "6  ...  0.5123  0.5020  0.4707  0.4663       Milwaukee_Bucks              12   \n",
       "7  ...  0.5560  0.5615  0.5166  0.4982  New_Orleans_Pelicans             -10   \n",
       "8  ...  0.5304  0.5605  0.4934  0.5168    Washington_Wizards              -4   \n",
       "9  ...  0.5524  0.5710  0.5075  0.5188         Atlanta_Hawks             -11   \n",
       "\n",
       "                  visitor  wn_h  wn_v  home_win  \n",
       "0         New_York_Knicks     7     2         1  \n",
       "1  Portland_Trail_Blazers     0     9         0  \n",
       "2            Phoenix_Suns     9     6         1  \n",
       "3           Chicago_Bulls     4     6         0  \n",
       "4           Chicago_Bulls     5     6         1  \n",
       "5   Golden_State_Warriors     5     8         0  \n",
       "6         Detroit_Pistons     5     3         1  \n",
       "7        Sacramento_Kings     6     6         0  \n",
       "8           Atlanta_Hawks     8     6         0  \n",
       "9         Toronto_Raptors     6     9         0  \n",
       "\n",
       "[10 rows x 68 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def trans(score):\n",
    "    if score > 0:\n",
    "        return 1\n",
    "    else: return 0\n",
    "nba_df['home_win'] = nba_df.home_win_score.map(trans)\n",
    "nba_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4720, 64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_X = nba_df.drop(['home', 'home_win_score', 'visitor', 'home_win'],axis = 1)\n",
    "nba_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr = nba_df.corr()\n",
    "plt.figure(figsize = (20,20))\n",
    "sns.heatmap(corr, annot=False, cmap=plt.cm.rainbow)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get corr greater than 0.3 with wn home\n",
    "selected_feat_home = abs(corr['wn_h'])>0.4\n",
    "home_feature_df = selected_feat_home[selected_feat_home==True].to_frame()\n",
    "home_feature_list = list(home_feature_df.index)\n",
    "len(home_feature_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get corr greater than 0.3 with wn vistor\n",
    "selected_feat_vis = abs(corr['wn_v'])>0.4\n",
    "vis_feature_df = selected_feat_vis[selected_feat_vis==True].to_frame()\n",
    "vis_feature_list = list(vis_feature_df.index)\n",
    "len(vis_feature_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['3P%_h',\n",
       " 'DRtg_h',\n",
       " 'FG%_h',\n",
       " 'FG_h',\n",
       " 'ORtg_h',\n",
       " 'PTS_h',\n",
       " 'TS%_h',\n",
       " 'eFG%_h',\n",
       " 'wn_h',\n",
       " '3P%_v',\n",
       " 'DRtg_v',\n",
       " 'FG%_v',\n",
       " 'FG_v',\n",
       " 'ORtg_v',\n",
       " 'PTS_v',\n",
       " 'TS%_v',\n",
       " 'eFG%_v',\n",
       " 'wn_v']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features = home_feature_list + vis_feature_list\n",
    "selected_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4720, 18)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huidu_X = nba_df.loc[:, selected_features]\n",
    "huidu_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3P%_h</th>\n",
       "      <th>DRtg_h</th>\n",
       "      <th>FG%_h</th>\n",
       "      <th>FG_h</th>\n",
       "      <th>ORtg_h</th>\n",
       "      <th>PTS_h</th>\n",
       "      <th>TS%_h</th>\n",
       "      <th>eFG%_h</th>\n",
       "      <th>wn_h</th>\n",
       "      <th>3P%_v</th>\n",
       "      <th>DRtg_v</th>\n",
       "      <th>FG%_v</th>\n",
       "      <th>FG_v</th>\n",
       "      <th>ORtg_v</th>\n",
       "      <th>PTS_v</th>\n",
       "      <th>TS%_v</th>\n",
       "      <th>eFG%_v</th>\n",
       "      <th>wn_v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3364</td>\n",
       "      <td>99.49</td>\n",
       "      <td>0.4113</td>\n",
       "      <td>32.0</td>\n",
       "      <td>102.12</td>\n",
       "      <td>94.7</td>\n",
       "      <td>0.5302</td>\n",
       "      <td>0.4877</td>\n",
       "      <td>7</td>\n",
       "      <td>0.4241</td>\n",
       "      <td>112.48</td>\n",
       "      <td>0.4742</td>\n",
       "      <td>38.0</td>\n",
       "      <td>109.41</td>\n",
       "      <td>98.2</td>\n",
       "      <td>0.5562</td>\n",
       "      <td>0.5217</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3075</td>\n",
       "      <td>109.28</td>\n",
       "      <td>0.4033</td>\n",
       "      <td>32.6</td>\n",
       "      <td>91.74</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.4844</td>\n",
       "      <td>0.4514</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4163</td>\n",
       "      <td>100.66</td>\n",
       "      <td>0.4700</td>\n",
       "      <td>39.0</td>\n",
       "      <td>112.84</td>\n",
       "      <td>105.7</td>\n",
       "      <td>0.5713</td>\n",
       "      <td>0.5324</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3926</td>\n",
       "      <td>99.78</td>\n",
       "      <td>0.4623</td>\n",
       "      <td>38.0</td>\n",
       "      <td>115.23</td>\n",
       "      <td>107.1</td>\n",
       "      <td>0.5692</td>\n",
       "      <td>0.5156</td>\n",
       "      <td>9</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>104.23</td>\n",
       "      <td>0.4586</td>\n",
       "      <td>38.9</td>\n",
       "      <td>108.48</td>\n",
       "      <td>105.8</td>\n",
       "      <td>0.5605</td>\n",
       "      <td>0.5145</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3452</td>\n",
       "      <td>111.95</td>\n",
       "      <td>0.4600</td>\n",
       "      <td>34.8</td>\n",
       "      <td>105.36</td>\n",
       "      <td>93.9</td>\n",
       "      <td>0.5463</td>\n",
       "      <td>0.5063</td>\n",
       "      <td>4</td>\n",
       "      <td>0.3588</td>\n",
       "      <td>105.26</td>\n",
       "      <td>0.4527</td>\n",
       "      <td>36.7</td>\n",
       "      <td>106.11</td>\n",
       "      <td>98.4</td>\n",
       "      <td>0.5337</td>\n",
       "      <td>0.4956</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3623</td>\n",
       "      <td>111.67</td>\n",
       "      <td>0.4507</td>\n",
       "      <td>38.5</td>\n",
       "      <td>110.39</td>\n",
       "      <td>105.5</td>\n",
       "      <td>0.5456</td>\n",
       "      <td>0.5078</td>\n",
       "      <td>5</td>\n",
       "      <td>0.3571</td>\n",
       "      <td>105.67</td>\n",
       "      <td>0.4560</td>\n",
       "      <td>36.8</td>\n",
       "      <td>105.84</td>\n",
       "      <td>98.3</td>\n",
       "      <td>0.5364</td>\n",
       "      <td>0.5006</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.3828</td>\n",
       "      <td>107.37</td>\n",
       "      <td>0.4679</td>\n",
       "      <td>33.8</td>\n",
       "      <td>106.45</td>\n",
       "      <td>93.9</td>\n",
       "      <td>0.5717</td>\n",
       "      <td>0.5281</td>\n",
       "      <td>5</td>\n",
       "      <td>0.3833</td>\n",
       "      <td>99.15</td>\n",
       "      <td>0.4857</td>\n",
       "      <td>39.5</td>\n",
       "      <td>107.31</td>\n",
       "      <td>105.6</td>\n",
       "      <td>0.5830</td>\n",
       "      <td>0.5456</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.2681</td>\n",
       "      <td>108.17</td>\n",
       "      <td>0.4413</td>\n",
       "      <td>36.7</td>\n",
       "      <td>101.92</td>\n",
       "      <td>95.6</td>\n",
       "      <td>0.5123</td>\n",
       "      <td>0.4707</td>\n",
       "      <td>5</td>\n",
       "      <td>0.3794</td>\n",
       "      <td>106.46</td>\n",
       "      <td>0.4136</td>\n",
       "      <td>34.5</td>\n",
       "      <td>102.49</td>\n",
       "      <td>93.8</td>\n",
       "      <td>0.5020</td>\n",
       "      <td>0.4663</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.3627</td>\n",
       "      <td>109.41</td>\n",
       "      <td>0.4724</td>\n",
       "      <td>39.9</td>\n",
       "      <td>112.91</td>\n",
       "      <td>104.2</td>\n",
       "      <td>0.5560</td>\n",
       "      <td>0.5166</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3411</td>\n",
       "      <td>109.00</td>\n",
       "      <td>0.4657</td>\n",
       "      <td>37.3</td>\n",
       "      <td>112.59</td>\n",
       "      <td>106.6</td>\n",
       "      <td>0.5615</td>\n",
       "      <td>0.4982</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.3376</td>\n",
       "      <td>100.10</td>\n",
       "      <td>0.4624</td>\n",
       "      <td>38.5</td>\n",
       "      <td>105.03</td>\n",
       "      <td>99.2</td>\n",
       "      <td>0.5304</td>\n",
       "      <td>0.4934</td>\n",
       "      <td>8</td>\n",
       "      <td>0.3363</td>\n",
       "      <td>108.36</td>\n",
       "      <td>0.4643</td>\n",
       "      <td>37.6</td>\n",
       "      <td>109.12</td>\n",
       "      <td>102.3</td>\n",
       "      <td>0.5605</td>\n",
       "      <td>0.5168</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.3293</td>\n",
       "      <td>109.06</td>\n",
       "      <td>0.4560</td>\n",
       "      <td>37.7</td>\n",
       "      <td>109.16</td>\n",
       "      <td>102.7</td>\n",
       "      <td>0.5524</td>\n",
       "      <td>0.5075</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3903</td>\n",
       "      <td>100.35</td>\n",
       "      <td>0.4679</td>\n",
       "      <td>38.5</td>\n",
       "      <td>114.87</td>\n",
       "      <td>107.5</td>\n",
       "      <td>0.5710</td>\n",
       "      <td>0.5188</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    3P%_h  DRtg_h   FG%_h  FG_h  ORtg_h  PTS_h   TS%_h  eFG%_h  wn_h   3P%_v  \\\n",
       "0  0.3364   99.49  0.4113  32.0  102.12   94.7  0.5302  0.4877     7  0.4241   \n",
       "1  0.3075  109.28  0.4033  32.6   91.74   88.6  0.4844  0.4514     0  0.4163   \n",
       "2  0.3926   99.78  0.4623  38.0  115.23  107.1  0.5692  0.5156     9  0.3582   \n",
       "3  0.3452  111.95  0.4600  34.8  105.36   93.9  0.5463  0.5063     4  0.3588   \n",
       "4  0.3623  111.67  0.4507  38.5  110.39  105.5  0.5456  0.5078     5  0.3571   \n",
       "5  0.3828  107.37  0.4679  33.8  106.45   93.9  0.5717  0.5281     5  0.3833   \n",
       "6  0.2681  108.17  0.4413  36.7  101.92   95.6  0.5123  0.4707     5  0.3794   \n",
       "7  0.3627  109.41  0.4724  39.9  112.91  104.2  0.5560  0.5166     6  0.3411   \n",
       "8  0.3376  100.10  0.4624  38.5  105.03   99.2  0.5304  0.4934     8  0.3363   \n",
       "9  0.3293  109.06  0.4560  37.7  109.16  102.7  0.5524  0.5075     6  0.3903   \n",
       "\n",
       "   DRtg_v   FG%_v  FG_v  ORtg_v  PTS_v   TS%_v  eFG%_v  wn_v  \n",
       "0  112.48  0.4742  38.0  109.41   98.2  0.5562  0.5217     2  \n",
       "1  100.66  0.4700  39.0  112.84  105.7  0.5713  0.5324     9  \n",
       "2  104.23  0.4586  38.9  108.48  105.8  0.5605  0.5145     6  \n",
       "3  105.26  0.4527  36.7  106.11   98.4  0.5337  0.4956     6  \n",
       "4  105.67  0.4560  36.8  105.84   98.3  0.5364  0.5006     6  \n",
       "5   99.15  0.4857  39.5  107.31  105.6  0.5830  0.5456     8  \n",
       "6  106.46  0.4136  34.5  102.49   93.8  0.5020  0.4663     3  \n",
       "7  109.00  0.4657  37.3  112.59  106.6  0.5615  0.4982     6  \n",
       "8  108.36  0.4643  37.6  109.12  102.3  0.5605  0.5168     6  \n",
       "9  100.35  0.4679  38.5  114.87  107.5  0.5710  0.5188     9  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huidu_X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4720,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = nba_df['home_win']\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = 2\n",
    "X_train, X_val, y_train, y_val =  train_test_split(huidu_X, y, test_size=.2, random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3P%_h</th>\n",
       "      <th>DRtg_h</th>\n",
       "      <th>FG%_h</th>\n",
       "      <th>FG_h</th>\n",
       "      <th>ORtg_h</th>\n",
       "      <th>PTS_h</th>\n",
       "      <th>TS%_h</th>\n",
       "      <th>eFG%_h</th>\n",
       "      <th>wn_h</th>\n",
       "      <th>3P%_v</th>\n",
       "      <th>DRtg_v</th>\n",
       "      <th>FG%_v</th>\n",
       "      <th>FG_v</th>\n",
       "      <th>ORtg_v</th>\n",
       "      <th>PTS_v</th>\n",
       "      <th>TS%_v</th>\n",
       "      <th>eFG%_v</th>\n",
       "      <th>wn_v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2038</th>\n",
       "      <td>0.2899</td>\n",
       "      <td>109.56</td>\n",
       "      <td>0.4172</td>\n",
       "      <td>35.5</td>\n",
       "      <td>102.96</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.5079</td>\n",
       "      <td>0.4509</td>\n",
       "      <td>5</td>\n",
       "      <td>0.3585</td>\n",
       "      <td>114.43</td>\n",
       "      <td>0.4786</td>\n",
       "      <td>38.8</td>\n",
       "      <td>107.01</td>\n",
       "      <td>104.6</td>\n",
       "      <td>0.5646</td>\n",
       "      <td>0.5150</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3593</th>\n",
       "      <td>0.3856</td>\n",
       "      <td>105.28</td>\n",
       "      <td>0.4754</td>\n",
       "      <td>37.7</td>\n",
       "      <td>104.18</td>\n",
       "      <td>98.9</td>\n",
       "      <td>0.5669</td>\n",
       "      <td>0.5368</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3126</td>\n",
       "      <td>105.06</td>\n",
       "      <td>0.4391</td>\n",
       "      <td>39.6</td>\n",
       "      <td>108.38</td>\n",
       "      <td>104.6</td>\n",
       "      <td>0.5247</td>\n",
       "      <td>0.4906</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.2914</td>\n",
       "      <td>109.91</td>\n",
       "      <td>0.4531</td>\n",
       "      <td>38.6</td>\n",
       "      <td>109.79</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.5456</td>\n",
       "      <td>0.5062</td>\n",
       "      <td>5</td>\n",
       "      <td>0.3070</td>\n",
       "      <td>107.36</td>\n",
       "      <td>0.4381</td>\n",
       "      <td>36.8</td>\n",
       "      <td>107.41</td>\n",
       "      <td>102.5</td>\n",
       "      <td>0.5333</td>\n",
       "      <td>0.4774</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3860</th>\n",
       "      <td>0.3261</td>\n",
       "      <td>109.98</td>\n",
       "      <td>0.4784</td>\n",
       "      <td>37.6</td>\n",
       "      <td>108.58</td>\n",
       "      <td>101.5</td>\n",
       "      <td>0.5631</td>\n",
       "      <td>0.5241</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3684</td>\n",
       "      <td>103.32</td>\n",
       "      <td>0.4548</td>\n",
       "      <td>38.4</td>\n",
       "      <td>107.99</td>\n",
       "      <td>102.4</td>\n",
       "      <td>0.5551</td>\n",
       "      <td>0.5262</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1745</th>\n",
       "      <td>0.3227</td>\n",
       "      <td>108.16</td>\n",
       "      <td>0.4561</td>\n",
       "      <td>35.4</td>\n",
       "      <td>98.94</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.5286</td>\n",
       "      <td>0.4909</td>\n",
       "      <td>4</td>\n",
       "      <td>0.3360</td>\n",
       "      <td>99.13</td>\n",
       "      <td>0.4456</td>\n",
       "      <td>37.5</td>\n",
       "      <td>100.12</td>\n",
       "      <td>97.8</td>\n",
       "      <td>0.5309</td>\n",
       "      <td>0.4985</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>0.3341</td>\n",
       "      <td>112.09</td>\n",
       "      <td>0.4056</td>\n",
       "      <td>32.3</td>\n",
       "      <td>96.68</td>\n",
       "      <td>87.6</td>\n",
       "      <td>0.4953</td>\n",
       "      <td>0.4455</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3229</td>\n",
       "      <td>108.60</td>\n",
       "      <td>0.4586</td>\n",
       "      <td>39.2</td>\n",
       "      <td>104.18</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.5237</td>\n",
       "      <td>0.4924</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1722</th>\n",
       "      <td>0.3993</td>\n",
       "      <td>110.47</td>\n",
       "      <td>0.4410</td>\n",
       "      <td>35.6</td>\n",
       "      <td>109.24</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.5366</td>\n",
       "      <td>0.4842</td>\n",
       "      <td>7</td>\n",
       "      <td>0.3164</td>\n",
       "      <td>107.26</td>\n",
       "      <td>0.4778</td>\n",
       "      <td>38.1</td>\n",
       "      <td>108.08</td>\n",
       "      <td>99.9</td>\n",
       "      <td>0.5456</td>\n",
       "      <td>0.5018</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>0.3693</td>\n",
       "      <td>119.43</td>\n",
       "      <td>0.4307</td>\n",
       "      <td>36.2</td>\n",
       "      <td>113.17</td>\n",
       "      <td>106.1</td>\n",
       "      <td>0.5437</td>\n",
       "      <td>0.4910</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4230</td>\n",
       "      <td>110.49</td>\n",
       "      <td>0.4786</td>\n",
       "      <td>38.7</td>\n",
       "      <td>108.38</td>\n",
       "      <td>103.9</td>\n",
       "      <td>0.5720</td>\n",
       "      <td>0.5297</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>0.3387</td>\n",
       "      <td>102.40</td>\n",
       "      <td>0.4284</td>\n",
       "      <td>35.5</td>\n",
       "      <td>106.40</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.5229</td>\n",
       "      <td>0.4762</td>\n",
       "      <td>7</td>\n",
       "      <td>0.3517</td>\n",
       "      <td>103.29</td>\n",
       "      <td>0.4518</td>\n",
       "      <td>41.6</td>\n",
       "      <td>112.56</td>\n",
       "      <td>112.3</td>\n",
       "      <td>0.5415</td>\n",
       "      <td>0.4944</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4055</th>\n",
       "      <td>0.3628</td>\n",
       "      <td>113.76</td>\n",
       "      <td>0.4494</td>\n",
       "      <td>38.5</td>\n",
       "      <td>116.15</td>\n",
       "      <td>116.5</td>\n",
       "      <td>0.5896</td>\n",
       "      <td>0.5389</td>\n",
       "      <td>5</td>\n",
       "      <td>0.3623</td>\n",
       "      <td>107.50</td>\n",
       "      <td>0.4970</td>\n",
       "      <td>42.5</td>\n",
       "      <td>113.60</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.5908</td>\n",
       "      <td>0.5547</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       3P%_h  DRtg_h   FG%_h  FG_h  ORtg_h  PTS_h   TS%_h  eFG%_h  wn_h  \\\n",
       "2038  0.2899  109.56  0.4172  35.5  102.96   99.4  0.5079  0.4509     5   \n",
       "3593  0.3856  105.28  0.4754  37.7  104.18   98.9  0.5669  0.5368     6   \n",
       "2019  0.2914  109.91  0.4531  38.6  109.79  109.0  0.5456  0.5062     5   \n",
       "3860  0.3261  109.98  0.4784  37.6  108.58  101.5  0.5631  0.5241     6   \n",
       "1745  0.3227  108.16  0.4561  35.4   98.94   91.0  0.5286  0.4909     4   \n",
       "982   0.3341  112.09  0.4056  32.3   96.68   87.6  0.4953  0.4455     1   \n",
       "1722  0.3993  110.47  0.4410  35.6  109.24   99.5  0.5366  0.4842     7   \n",
       "1923  0.3693  119.43  0.4307  36.2  113.17  106.1  0.5437  0.4910     2   \n",
       "707   0.3387  102.40  0.4284  35.5  106.40   97.0  0.5229  0.4762     7   \n",
       "4055  0.3628  113.76  0.4494  38.5  116.15  116.5  0.5896  0.5389     5   \n",
       "\n",
       "       3P%_v  DRtg_v   FG%_v  FG_v  ORtg_v  PTS_v   TS%_v  eFG%_v  wn_v  \n",
       "2038  0.3585  114.43  0.4786  38.8  107.01  104.6  0.5646  0.5150     3  \n",
       "3593  0.3126  105.06  0.4391  39.6  108.38  104.6  0.5247  0.4906     5  \n",
       "2019  0.3070  107.36  0.4381  36.8  107.41  102.5  0.5333  0.4774     6  \n",
       "3860  0.3684  103.32  0.4548  38.4  107.99  102.4  0.5551  0.5262     5  \n",
       "1745  0.3360   99.13  0.4456  37.5  100.12   97.8  0.5309  0.4985     4  \n",
       "982   0.3229  108.60  0.4586  39.2  104.18   99.4  0.5237  0.4924     4  \n",
       "1722  0.3164  107.26  0.4778  38.1  108.08   99.9  0.5456  0.5018     6  \n",
       "1923  0.4230  110.49  0.4786  38.7  108.38  103.9  0.5720  0.5297     5  \n",
       "707   0.3517  103.29  0.4518  41.6  112.56  112.3  0.5415  0.4944     8  \n",
       "4055  0.3623  107.50  0.4970  42.5  113.60  111.0  0.5908  0.5547     8  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegressionCV(cv=5, random_state=0, max_iter=1000).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_lr = clf.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_svm = SVC(gamma='auto')\n",
    "clf_svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_svm = clf_svm.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "       importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=-1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=None, subsample=0.7, verbosity=1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_reg = xgb.XGBRegressor(learning_rate=0.1, max_depth=3, n_estimators=100, n_jobs=-1, subsample=.7)\n",
    "xgb_reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4720, 64)\n",
      "(4720,)\n"
     ]
    }
   ],
   "source": [
    "X = predic_win.drop(['home', 'home_win_score', 'visitor'], axis=1)\n",
    "print(X.shape)\n",
    "y = predic_win['home_win_score']\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = 2\n",
    "X_train, X_val, y_train, y_val =  train_test_split(X, y, test_size=.2, random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:10:15] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[0]\tvalidation_0-rmse:13.4562\tvalidation_1-rmse:13.821\n",
      "[1]\tvalidation_0-rmse:13.32\tvalidation_1-rmse:13.7098\n",
      "[2]\tvalidation_0-rmse:13.2015\tvalidation_1-rmse:13.617\n",
      "[3]\tvalidation_0-rmse:13.0966\tvalidation_1-rmse:13.5468\n",
      "[4]\tvalidation_0-rmse:12.9999\tvalidation_1-rmse:13.4803\n",
      "[5]\tvalidation_0-rmse:12.9104\tvalidation_1-rmse:13.4285\n",
      "[6]\tvalidation_0-rmse:12.8369\tvalidation_1-rmse:13.374\n",
      "[7]\tvalidation_0-rmse:12.7724\tvalidation_1-rmse:13.3274\n",
      "[8]\tvalidation_0-rmse:12.7047\tvalidation_1-rmse:13.2823\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9]\tvalidation_0-rmse:12.6474\tvalidation_1-rmse:13.2256\n",
      "[10]\tvalidation_0-rmse:12.5965\tvalidation_1-rmse:13.1876\n",
      "[11]\tvalidation_0-rmse:12.5447\tvalidation_1-rmse:13.1705\n",
      "[12]\tvalidation_0-rmse:12.5007\tvalidation_1-rmse:13.1472\n",
      "[13]\tvalidation_0-rmse:12.4573\tvalidation_1-rmse:13.1226\n",
      "[14]\tvalidation_0-rmse:12.4142\tvalidation_1-rmse:13.1016\n",
      "[15]\tvalidation_0-rmse:12.38\tvalidation_1-rmse:13.0793\n",
      "[16]\tvalidation_0-rmse:12.3405\tvalidation_1-rmse:13.0683\n",
      "[17]\tvalidation_0-rmse:12.3119\tvalidation_1-rmse:13.0728\n",
      "[18]\tvalidation_0-rmse:12.2739\tvalidation_1-rmse:13.0569\n",
      "[19]\tvalidation_0-rmse:12.2473\tvalidation_1-rmse:13.048\n",
      "[20]\tvalidation_0-rmse:12.2158\tvalidation_1-rmse:13.0433\n",
      "[21]\tvalidation_0-rmse:12.1908\tvalidation_1-rmse:13.0372\n",
      "[22]\tvalidation_0-rmse:12.1707\tvalidation_1-rmse:13.0091\n",
      "[23]\tvalidation_0-rmse:12.1445\tvalidation_1-rmse:13.0003\n",
      "[24]\tvalidation_0-rmse:12.1224\tvalidation_1-rmse:12.9936\n",
      "[25]\tvalidation_0-rmse:12.0976\tvalidation_1-rmse:13.0054\n",
      "[26]\tvalidation_0-rmse:12.0741\tvalidation_1-rmse:13.005\n",
      "[27]\tvalidation_0-rmse:12.0547\tvalidation_1-rmse:12.9917\n",
      "[28]\tvalidation_0-rmse:12.0301\tvalidation_1-rmse:12.9875\n",
      "[29]\tvalidation_0-rmse:12.0143\tvalidation_1-rmse:12.9766\n",
      "[30]\tvalidation_0-rmse:11.9884\tvalidation_1-rmse:12.974\n",
      "[31]\tvalidation_0-rmse:11.97\tvalidation_1-rmse:12.9627\n",
      "[32]\tvalidation_0-rmse:11.948\tvalidation_1-rmse:12.9597\n",
      "[33]\tvalidation_0-rmse:11.9295\tvalidation_1-rmse:12.9531\n",
      "[34]\tvalidation_0-rmse:11.9113\tvalidation_1-rmse:12.9589\n",
      "[35]\tvalidation_0-rmse:11.8888\tvalidation_1-rmse:12.9684\n",
      "[36]\tvalidation_0-rmse:11.8735\tvalidation_1-rmse:12.9712\n",
      "[37]\tvalidation_0-rmse:11.8516\tvalidation_1-rmse:12.9804\n",
      "[38]\tvalidation_0-rmse:11.8286\tvalidation_1-rmse:12.9763\n",
      "[39]\tvalidation_0-rmse:11.8113\tvalidation_1-rmse:12.9713\n",
      "[40]\tvalidation_0-rmse:11.7852\tvalidation_1-rmse:12.9762\n",
      "[41]\tvalidation_0-rmse:11.7608\tvalidation_1-rmse:12.9661\n",
      "[42]\tvalidation_0-rmse:11.7492\tvalidation_1-rmse:12.9705\n",
      "[43]\tvalidation_0-rmse:11.7313\tvalidation_1-rmse:12.9691\n",
      "[44]\tvalidation_0-rmse:11.7146\tvalidation_1-rmse:12.9666\n",
      "[45]\tvalidation_0-rmse:11.6973\tvalidation_1-rmse:12.9628\n",
      "[46]\tvalidation_0-rmse:11.6784\tvalidation_1-rmse:12.9646\n",
      "[47]\tvalidation_0-rmse:11.6615\tvalidation_1-rmse:12.9624\n",
      "[48]\tvalidation_0-rmse:11.6405\tvalidation_1-rmse:12.9451\n",
      "[49]\tvalidation_0-rmse:11.6306\tvalidation_1-rmse:12.9438\n",
      "[50]\tvalidation_0-rmse:11.614\tvalidation_1-rmse:12.9554\n",
      "[51]\tvalidation_0-rmse:11.5962\tvalidation_1-rmse:12.9596\n",
      "[52]\tvalidation_0-rmse:11.5792\tvalidation_1-rmse:12.9615\n",
      "[53]\tvalidation_0-rmse:11.5589\tvalidation_1-rmse:12.9606\n",
      "[54]\tvalidation_0-rmse:11.5428\tvalidation_1-rmse:12.9591\n",
      "[55]\tvalidation_0-rmse:11.5343\tvalidation_1-rmse:12.9587\n",
      "[56]\tvalidation_0-rmse:11.5256\tvalidation_1-rmse:12.9595\n",
      "[57]\tvalidation_0-rmse:11.5074\tvalidation_1-rmse:12.9568\n",
      "[58]\tvalidation_0-rmse:11.498\tvalidation_1-rmse:12.95\n",
      "[59]\tvalidation_0-rmse:11.484\tvalidation_1-rmse:12.9578\n",
      "[60]\tvalidation_0-rmse:11.4728\tvalidation_1-rmse:12.9599\n",
      "[61]\tvalidation_0-rmse:11.4617\tvalidation_1-rmse:12.9536\n",
      "[62]\tvalidation_0-rmse:11.4517\tvalidation_1-rmse:12.9553\n",
      "[63]\tvalidation_0-rmse:11.443\tvalidation_1-rmse:12.9635\n",
      "[64]\tvalidation_0-rmse:11.4292\tvalidation_1-rmse:12.9634\n",
      "[65]\tvalidation_0-rmse:11.4138\tvalidation_1-rmse:12.956\n",
      "[66]\tvalidation_0-rmse:11.3966\tvalidation_1-rmse:12.9515\n",
      "[67]\tvalidation_0-rmse:11.3801\tvalidation_1-rmse:12.9552\n",
      "[68]\tvalidation_0-rmse:11.3701\tvalidation_1-rmse:12.9542\n",
      "[69]\tvalidation_0-rmse:11.3522\tvalidation_1-rmse:12.946\n",
      "[70]\tvalidation_0-rmse:11.3397\tvalidation_1-rmse:12.9508\n",
      "[71]\tvalidation_0-rmse:11.3322\tvalidation_1-rmse:12.948\n",
      "[72]\tvalidation_0-rmse:11.316\tvalidation_1-rmse:12.9494\n",
      "[73]\tvalidation_0-rmse:11.3026\tvalidation_1-rmse:12.9521\n",
      "[74]\tvalidation_0-rmse:11.2888\tvalidation_1-rmse:12.955\n",
      "[75]\tvalidation_0-rmse:11.277\tvalidation_1-rmse:12.9386\n",
      "[76]\tvalidation_0-rmse:11.2706\tvalidation_1-rmse:12.9314\n",
      "[77]\tvalidation_0-rmse:11.2518\tvalidation_1-rmse:12.9364\n",
      "[78]\tvalidation_0-rmse:11.2483\tvalidation_1-rmse:12.9431\n",
      "[79]\tvalidation_0-rmse:11.2402\tvalidation_1-rmse:12.9453\n",
      "[80]\tvalidation_0-rmse:11.2315\tvalidation_1-rmse:12.9426\n",
      "[81]\tvalidation_0-rmse:11.2194\tvalidation_1-rmse:12.9428\n",
      "[82]\tvalidation_0-rmse:11.2088\tvalidation_1-rmse:12.9402\n",
      "[83]\tvalidation_0-rmse:11.1927\tvalidation_1-rmse:12.9462\n",
      "[84]\tvalidation_0-rmse:11.1847\tvalidation_1-rmse:12.9463\n",
      "[85]\tvalidation_0-rmse:11.1703\tvalidation_1-rmse:12.9541\n",
      "[86]\tvalidation_0-rmse:11.159\tvalidation_1-rmse:12.9511\n",
      "[87]\tvalidation_0-rmse:11.1495\tvalidation_1-rmse:12.9559\n",
      "[88]\tvalidation_0-rmse:11.1341\tvalidation_1-rmse:12.9526\n",
      "[89]\tvalidation_0-rmse:11.1164\tvalidation_1-rmse:12.9605\n",
      "[90]\tvalidation_0-rmse:11.1034\tvalidation_1-rmse:12.9662\n",
      "[91]\tvalidation_0-rmse:11.0933\tvalidation_1-rmse:12.9723\n",
      "[92]\tvalidation_0-rmse:11.0797\tvalidation_1-rmse:12.9764\n",
      "[93]\tvalidation_0-rmse:11.064\tvalidation_1-rmse:12.9712\n",
      "[94]\tvalidation_0-rmse:11.0519\tvalidation_1-rmse:12.9625\n",
      "[95]\tvalidation_0-rmse:11.0392\tvalidation_1-rmse:12.9667\n",
      "[96]\tvalidation_0-rmse:11.0292\tvalidation_1-rmse:12.975\n",
      "[97]\tvalidation_0-rmse:11.0163\tvalidation_1-rmse:12.9788\n",
      "[98]\tvalidation_0-rmse:11.0083\tvalidation_1-rmse:12.9809\n",
      "[99]\tvalidation_0-rmse:10.9945\tvalidation_1-rmse:12.9833\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "       importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=-1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=None, subsample=0.7, verbosity=1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_reg.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = xgb_reg.evals_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_iter = len(results['validation_0']['rmse'])\n",
    "n_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12f74e400>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(list(range(n_iter)), results['validation_0']['rmse'], label='Training rmse error')\n",
    "plt.scatter(list(range(n_iter)), results['validation_1']['rmse'], label='Validation rmse error')\n",
    "plt.grid(True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the performance of this model is not so good, but this is the best model from models we have used. There is no absolute strong team in NBA, every game result is hard to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12fab6588>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(xgb_reg,max_num_features=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From plot above we can see the Defensive, 3-point, Rebound and Assist are keys to win a game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgb_reg.predict(X_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The acuarcy of predict win or lose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_p = y_pred>0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_t = y_val>0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_xgb = sum(y_p == y_t)/len(y_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6610169491525424"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy among LR , SVM and XGB')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cell_text = [[score_lr, score_svm,score_xgb]]\n",
    "columns = ('LR', 'SVM', 'XGB')\n",
    "rows = [\"accuracy\"]\n",
    "fig, ax = plt.subplots()\n",
    "# ax.xaxis.set_visible(False)\n",
    "ax.axis('off')\n",
    "ax.table(cellText=cell_text,rowLabels=rows, colLabels=columns, colWidths = [0.8, 0.8, 0.8], loc=\"center\")\n",
    "ax.set_title(\"Accuracy among LR , SVM and XGB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From result we can see if we only predict home or visitor win, the acuarcy is 66%, which is not bad."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Player Stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also get a play's every game performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.basketball-reference.com/players/j/jamesle01/gamelog/2004/'\n",
    "html = requests.get(url).content\n",
    "df_list = pd.read_html(html)\n",
    "lebron_james = df_list[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Glossary:\n",
    "    Rk -- Rank  G -- Season Game  Age -- Age of Player at the start of February 1st of that season.  Tm -- Team\n",
    "    Opp -- Opponent  GS -- Games Started  MP -- Minutes Played  FG -- Field Goals  FGA -- Field Goal Attempts\n",
    "    FG% -- Field Goal Percentage  3P -- 3-Point Field Goals  3PA -- 3-Point Field Goal Attempts  \n",
    "    3P% -- 3-Point Field Goal Percentage  FT -- Free Throws  FTA -- Free Throw Attempts  \n",
    "    FT% -- Free Throw Percentage  ORB -- Offensive Rebounds  DRB -- Defensive Rebounds  TRB -- Total Rebounds  \n",
    "    AST -- Assists  STL -- Steals  BLK -- Blocks  TOV -- Turnovers  PF -- Personal Fouls  PTS -- Points  \n",
    "    GmSc -- Game Score  +/- -- Plus/Minus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>G</th>\n",
       "      <th>Date</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Opp</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>...</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>GmSc</th>\n",
       "      <th>+/-</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2003-10-29</td>\n",
       "      <td>18-303</td>\n",
       "      <td>CLE</td>\n",
       "      <td>@</td>\n",
       "      <td>SAC</td>\n",
       "      <td>L (-14)</td>\n",
       "      <td>1</td>\n",
       "      <td>42:00</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>24.7</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2003-10-30</td>\n",
       "      <td>18-304</td>\n",
       "      <td>CLE</td>\n",
       "      <td>@</td>\n",
       "      <td>PHO</td>\n",
       "      <td>L (-9)</td>\n",
       "      <td>1</td>\n",
       "      <td>41:00</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>14.7</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2003-11-01</td>\n",
       "      <td>18-306</td>\n",
       "      <td>CLE</td>\n",
       "      <td>@</td>\n",
       "      <td>POR</td>\n",
       "      <td>L (-19)</td>\n",
       "      <td>1</td>\n",
       "      <td>39:00</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2003-11-05</td>\n",
       "      <td>18-310</td>\n",
       "      <td>CLE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DEN</td>\n",
       "      <td>L (-4)</td>\n",
       "      <td>1</td>\n",
       "      <td>41:00</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>11.2</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2003-11-07</td>\n",
       "      <td>18-312</td>\n",
       "      <td>CLE</td>\n",
       "      <td>@</td>\n",
       "      <td>IND</td>\n",
       "      <td>L (-1)</td>\n",
       "      <td>1</td>\n",
       "      <td>44:00</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2003-11-08</td>\n",
       "      <td>18-313</td>\n",
       "      <td>CLE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WAS</td>\n",
       "      <td>W (+13)</td>\n",
       "      <td>1</td>\n",
       "      <td>45:00</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>15.7</td>\n",
       "      <td>+14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2003-11-10</td>\n",
       "      <td>18-315</td>\n",
       "      <td>CLE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NYK</td>\n",
       "      <td>W (+14)</td>\n",
       "      <td>1</td>\n",
       "      <td>34:00</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>15.8</td>\n",
       "      <td>+20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2003-11-12</td>\n",
       "      <td>18-317</td>\n",
       "      <td>CLE</td>\n",
       "      <td>@</td>\n",
       "      <td>MIA</td>\n",
       "      <td>L (-5)</td>\n",
       "      <td>1</td>\n",
       "      <td>42:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>12.8</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2003-11-14</td>\n",
       "      <td>18-319</td>\n",
       "      <td>CLE</td>\n",
       "      <td>@</td>\n",
       "      <td>BOS</td>\n",
       "      <td>L (-9)</td>\n",
       "      <td>1</td>\n",
       "      <td>35:00</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2003-11-15</td>\n",
       "      <td>18-320</td>\n",
       "      <td>CLE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PHI</td>\n",
       "      <td>W (+3)</td>\n",
       "      <td>1</td>\n",
       "      <td>47:00</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>15.6</td>\n",
       "      <td>+8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rk   G        Date     Age   Tm Unnamed: 5  Opp Unnamed: 7 GS     MP  ...  \\\n",
       "0   1   1  2003-10-29  18-303  CLE          @  SAC    L (-14)  1  42:00  ...   \n",
       "1   2   2  2003-10-30  18-304  CLE          @  PHO     L (-9)  1  41:00  ...   \n",
       "2   3   3  2003-11-01  18-306  CLE          @  POR    L (-19)  1  39:00  ...   \n",
       "3   4   4  2003-11-05  18-310  CLE        NaN  DEN     L (-4)  1  41:00  ...   \n",
       "4   5   5  2003-11-07  18-312  CLE          @  IND     L (-1)  1  44:00  ...   \n",
       "5   6   6  2003-11-08  18-313  CLE        NaN  WAS    W (+13)  1  45:00  ...   \n",
       "6   7   7  2003-11-10  18-315  CLE        NaN  NYK    W (+14)  1  34:00  ...   \n",
       "7   8   8  2003-11-12  18-317  CLE          @  MIA     L (-5)  1  42:00  ...   \n",
       "8   9   9  2003-11-14  18-319  CLE          @  BOS     L (-9)  1  35:00  ...   \n",
       "9  10  10  2003-11-15  18-320  CLE        NaN  PHI     W (+3)  1  47:00  ...   \n",
       "\n",
       "  DRB TRB AST STL BLK TOV PF PTS  GmSc  +/-  \n",
       "0   4   6   9   4   0   2  3  25  24.7   -9  \n",
       "1  10  12   8   1   0   7  1  21  14.7   -3  \n",
       "2   4   4   6   2   0   2  3   8   5.0  -21  \n",
       "3   9  11   7   2   3   2  1   7  11.2   -3  \n",
       "4   5   5   3   0   0   7  2  23   9.0   -7  \n",
       "5   3   8   9   1   1   2  1  17  15.7  +14  \n",
       "6   4   5   4   1   1   2  0  17  15.8  +20  \n",
       "7   2   3   7   2   1   4  3  18  12.8   -4  \n",
       "8   4   5   3   0   0   4  3  10   0.8   -1  \n",
       "9   5   5   8   1   2   5  2  22  15.6   +8  \n",
       "\n",
       "[10 rows x 30 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lebron_james.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see Lebron's rookie year performance is really good."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get player stats from Season 2015 to Season 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_num_list = ['Rk', 'Age', 'G', 'GS', 'MP', 'FG', 'FGA', 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%', 'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = [2019,2018,2017,2016,2015]\n",
    "player_stats = {}\n",
    "for year in years:\n",
    "    url = 'https://www.basketball-reference.com/leagues/NBA_'+str(year)+'_per_game.html'\n",
    "    html = requests.get(url).content\n",
    "    df_list = pd.read_html(html)\n",
    "    df = df_list[0]\n",
    "    df.drop(df[df.Rk=='Rk'].index, inplace=True)\n",
    "    df[to_num_list] = df[to_num_list].apply(pd.to_numeric)\n",
    "    player_stats[year] = df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Glossary:\n",
    "    Rk -- Rank  Pos -- Position  Age -- Age of Player at the start of February 1st of that season.\n",
    "    Tm -- Team  G -- Games  GS -- Games Started  MP -- Minutes Played Per Game  FG -- Field Goals Per Game\n",
    "    FGA -- Field Goal Attempts Per Game  FG% -- Field Goal Percentage  3P -- 3-Point Field Goals Per Game\n",
    "    3PA -- 3-Point Field Goal Attempts Per Game  3P% -- FG% on 3-Pt FGAs.  2P -- 2-Point Field Goals Per Game\n",
    "    2PA -- 2-Point Field Goal Attempts Per Game  2P% -- FG% on 2-Pt FGAs.  \n",
    "    eFG% -- Effective Field Goal Percentage  This statistic adjusts for the fact that a 3-point field goal is worth one more point than a 2-point field goal.\n",
    "    FT -- Free Throws Per Game  FTA -- Free Throw Attempts Per Game  FT% -- Free Throw Percentage\n",
    "    ORB -- Offensive Rebounds Per Game  DRB -- Defensive Rebounds Per Game  TRB -- Total Rebounds Per Game  \n",
    "    AST -- Assists Per Game  STL -- Steals Per Game  BLK -- Blocks Per Game  TOV -- Turnovers Per Game\n",
    "    PF -- Personal Fouls Per Game  PTS -- Points Per Game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex Abrines</td>\n",
       "      <td>SG</td>\n",
       "      <td>25</td>\n",
       "      <td>OKC</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Quincy Acy</td>\n",
       "      <td>PF</td>\n",
       "      <td>28</td>\n",
       "      <td>PHO</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jaylen Adams</td>\n",
       "      <td>PG</td>\n",
       "      <td>22</td>\n",
       "      <td>ATL</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Steven Adams</td>\n",
       "      <td>C</td>\n",
       "      <td>25</td>\n",
       "      <td>OKC</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>33.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bam Adebayo</td>\n",
       "      <td>C</td>\n",
       "      <td>21</td>\n",
       "      <td>MIA</td>\n",
       "      <td>82</td>\n",
       "      <td>28</td>\n",
       "      <td>23.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.735</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Deng Adel</td>\n",
       "      <td>SF</td>\n",
       "      <td>21</td>\n",
       "      <td>CLE</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>DeVaughn Akoon-Purcell</td>\n",
       "      <td>SG</td>\n",
       "      <td>25</td>\n",
       "      <td>DEN</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>LaMarcus Aldridge</td>\n",
       "      <td>C</td>\n",
       "      <td>33</td>\n",
       "      <td>SAS</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>33.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>16.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.847</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Rawle Alkins</td>\n",
       "      <td>SG</td>\n",
       "      <td>21</td>\n",
       "      <td>CHI</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.667</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Grayson Allen</td>\n",
       "      <td>SG</td>\n",
       "      <td>23</td>\n",
       "      <td>UTA</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>10.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Jarrett Allen</td>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "      <td>BRK</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>26.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>7.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.709</td>\n",
       "      <td>2.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Kadeem Allen</td>\n",
       "      <td>SG</td>\n",
       "      <td>26</td>\n",
       "      <td>NYK</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>21.9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Al-Farouq Aminu</td>\n",
       "      <td>PF</td>\n",
       "      <td>28</td>\n",
       "      <td>POR</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>28.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.867</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Justin Anderson</td>\n",
       "      <td>SF</td>\n",
       "      <td>25</td>\n",
       "      <td>ATL</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Kyle Anderson</td>\n",
       "      <td>SF</td>\n",
       "      <td>25</td>\n",
       "      <td>MEM</td>\n",
       "      <td>43</td>\n",
       "      <td>40</td>\n",
       "      <td>29.8</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.578</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Ryan Anderson</td>\n",
       "      <td>PF</td>\n",
       "      <td>30</td>\n",
       "      <td>TOT</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>12.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Ryan Anderson</td>\n",
       "      <td>PF</td>\n",
       "      <td>30</td>\n",
       "      <td>PHO</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>18.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>Ryan Anderson</td>\n",
       "      <td>PF</td>\n",
       "      <td>30</td>\n",
       "      <td>MIA</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>17</td>\n",
       "      <td>Ike Anigbogu</td>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "      <td>IND</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18</td>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>PF</td>\n",
       "      <td>24</td>\n",
       "      <td>MIL</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>32.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.729</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>12.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>27.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>19</td>\n",
       "      <td>Kostas Antetokounmpo</td>\n",
       "      <td>PF</td>\n",
       "      <td>21</td>\n",
       "      <td>DAL</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20</td>\n",
       "      <td>Carmelo Anthony</td>\n",
       "      <td>PF</td>\n",
       "      <td>34</td>\n",
       "      <td>HOU</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>29.4</td>\n",
       "      <td>4.9</td>\n",
       "      <td>12.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>21</td>\n",
       "      <td>OG Anunoby</td>\n",
       "      <td>SF</td>\n",
       "      <td>21</td>\n",
       "      <td>TOR</td>\n",
       "      <td>67</td>\n",
       "      <td>6</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.1</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>22</td>\n",
       "      <td>Ryan Arcidiacono</td>\n",
       "      <td>PG</td>\n",
       "      <td>24</td>\n",
       "      <td>CHI</td>\n",
       "      <td>81</td>\n",
       "      <td>32</td>\n",
       "      <td>24.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.1</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23</td>\n",
       "      <td>Trevor Ariza</td>\n",
       "      <td>SF</td>\n",
       "      <td>33</td>\n",
       "      <td>TOT</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>10.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23</td>\n",
       "      <td>Trevor Ariza</td>\n",
       "      <td>SF</td>\n",
       "      <td>33</td>\n",
       "      <td>PHO</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.837</td>\n",
       "      <td>0.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23</td>\n",
       "      <td>Trevor Ariza</td>\n",
       "      <td>SF</td>\n",
       "      <td>33</td>\n",
       "      <td>WAS</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>34.1</td>\n",
       "      <td>4.8</td>\n",
       "      <td>11.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>24</td>\n",
       "      <td>D.J. Augustin</td>\n",
       "      <td>PG</td>\n",
       "      <td>31</td>\n",
       "      <td>ORL</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>25</td>\n",
       "      <td>Deandre Ayton</td>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "      <td>PHO</td>\n",
       "      <td>71</td>\n",
       "      <td>70</td>\n",
       "      <td>30.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>12.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.746</td>\n",
       "      <td>3.1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>10.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.9</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>26</td>\n",
       "      <td>Dwayne Bacon</td>\n",
       "      <td>SG</td>\n",
       "      <td>23</td>\n",
       "      <td>CHO</td>\n",
       "      <td>43</td>\n",
       "      <td>13</td>\n",
       "      <td>17.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>509</td>\n",
       "      <td>Hassan Whiteside</td>\n",
       "      <td>C</td>\n",
       "      <td>29</td>\n",
       "      <td>MIA</td>\n",
       "      <td>72</td>\n",
       "      <td>53</td>\n",
       "      <td>23.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.449</td>\n",
       "      <td>3.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>510</td>\n",
       "      <td>Andrew Wiggins</td>\n",
       "      <td>SF</td>\n",
       "      <td>23</td>\n",
       "      <td>MIN</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>34.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>16.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.699</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>18.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>511</td>\n",
       "      <td>Alan Williams</td>\n",
       "      <td>PF</td>\n",
       "      <td>26</td>\n",
       "      <td>BRK</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>512</td>\n",
       "      <td>C.J. Williams</td>\n",
       "      <td>SG</td>\n",
       "      <td>28</td>\n",
       "      <td>MIN</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>513</td>\n",
       "      <td>Johnathan Williams</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>LAL</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.563</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>514</td>\n",
       "      <td>Kenrich Williams</td>\n",
       "      <td>SF</td>\n",
       "      <td>24</td>\n",
       "      <td>NOP</td>\n",
       "      <td>46</td>\n",
       "      <td>29</td>\n",
       "      <td>23.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.684</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.1</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>515</td>\n",
       "      <td>Lou Williams</td>\n",
       "      <td>SG</td>\n",
       "      <td>32</td>\n",
       "      <td>LAC</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>26.6</td>\n",
       "      <td>6.5</td>\n",
       "      <td>15.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>516</td>\n",
       "      <td>Marvin Williams</td>\n",
       "      <td>PF</td>\n",
       "      <td>32</td>\n",
       "      <td>CHO</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>28.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>517</td>\n",
       "      <td>Robert Williams</td>\n",
       "      <td>C</td>\n",
       "      <td>21</td>\n",
       "      <td>BOS</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>518</td>\n",
       "      <td>Troy Williams</td>\n",
       "      <td>SF</td>\n",
       "      <td>24</td>\n",
       "      <td>SAC</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>519</td>\n",
       "      <td>D.J. Wilson</td>\n",
       "      <td>PF</td>\n",
       "      <td>22</td>\n",
       "      <td>MIL</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>18.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>520</td>\n",
       "      <td>Justise Winslow</td>\n",
       "      <td>SF</td>\n",
       "      <td>22</td>\n",
       "      <td>MIA</td>\n",
       "      <td>66</td>\n",
       "      <td>52</td>\n",
       "      <td>29.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>11.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.628</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>521</td>\n",
       "      <td>Christian Wood</td>\n",
       "      <td>PF</td>\n",
       "      <td>23</td>\n",
       "      <td>TOT</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>521</td>\n",
       "      <td>Christian Wood</td>\n",
       "      <td>PF</td>\n",
       "      <td>23</td>\n",
       "      <td>MIL</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>521</td>\n",
       "      <td>Christian Wood</td>\n",
       "      <td>PF</td>\n",
       "      <td>23</td>\n",
       "      <td>NOP</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>23.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>11.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.756</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>522</td>\n",
       "      <td>Delon Wright</td>\n",
       "      <td>PG</td>\n",
       "      <td>26</td>\n",
       "      <td>TOT</td>\n",
       "      <td>75</td>\n",
       "      <td>13</td>\n",
       "      <td>22.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>522</td>\n",
       "      <td>Delon Wright</td>\n",
       "      <td>PG</td>\n",
       "      <td>26</td>\n",
       "      <td>TOR</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>18.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.869</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>522</td>\n",
       "      <td>Delon Wright</td>\n",
       "      <td>PG</td>\n",
       "      <td>26</td>\n",
       "      <td>MEM</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>30.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>10.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.742</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.9</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>523</td>\n",
       "      <td>Guerschon Yabusele</td>\n",
       "      <td>PF</td>\n",
       "      <td>23</td>\n",
       "      <td>BOS</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.682</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>524</td>\n",
       "      <td>Nick Young</td>\n",
       "      <td>SG</td>\n",
       "      <td>33</td>\n",
       "      <td>DEN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>525</td>\n",
       "      <td>Thaddeus Young</td>\n",
       "      <td>PF</td>\n",
       "      <td>30</td>\n",
       "      <td>IND</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>30.7</td>\n",
       "      <td>5.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.644</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>526</td>\n",
       "      <td>Trae Young</td>\n",
       "      <td>PG</td>\n",
       "      <td>20</td>\n",
       "      <td>ATL</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>30.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>527</td>\n",
       "      <td>Cody Zeller</td>\n",
       "      <td>C</td>\n",
       "      <td>26</td>\n",
       "      <td>CHO</td>\n",
       "      <td>49</td>\n",
       "      <td>47</td>\n",
       "      <td>25.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.787</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>528</td>\n",
       "      <td>Tyler Zeller</td>\n",
       "      <td>C</td>\n",
       "      <td>29</td>\n",
       "      <td>TOT</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.778</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>528</td>\n",
       "      <td>Tyler Zeller</td>\n",
       "      <td>C</td>\n",
       "      <td>29</td>\n",
       "      <td>ATL</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>528</td>\n",
       "      <td>Tyler Zeller</td>\n",
       "      <td>C</td>\n",
       "      <td>29</td>\n",
       "      <td>MEM</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>20.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.778</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>529</td>\n",
       "      <td>Ante Zizic</td>\n",
       "      <td>C</td>\n",
       "      <td>22</td>\n",
       "      <td>CLE</td>\n",
       "      <td>59</td>\n",
       "      <td>25</td>\n",
       "      <td>18.3</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.705</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>530</td>\n",
       "      <td>Ivica Zubac</td>\n",
       "      <td>C</td>\n",
       "      <td>21</td>\n",
       "      <td>TOT</td>\n",
       "      <td>59</td>\n",
       "      <td>37</td>\n",
       "      <td>17.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.802</td>\n",
       "      <td>1.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>530</td>\n",
       "      <td>Ivica Zubac</td>\n",
       "      <td>C</td>\n",
       "      <td>21</td>\n",
       "      <td>LAL</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>15.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.864</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>530</td>\n",
       "      <td>Ivica Zubac</td>\n",
       "      <td>C</td>\n",
       "      <td>21</td>\n",
       "      <td>LAC</td>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "      <td>20.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.733</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>708 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk                  Player Pos  Age   Tm   G  GS    MP    FG   FGA  ...  \\\n",
       "0      1            Alex Abrines  SG   25  OKC  31   2  19.0   1.8   5.1  ...   \n",
       "1      2              Quincy Acy  PF   28  PHO  10   0  12.3   0.4   1.8  ...   \n",
       "2      3            Jaylen Adams  PG   22  ATL  34   1  12.6   1.1   3.2  ...   \n",
       "3      4            Steven Adams   C   25  OKC  80  80  33.4   6.0  10.1  ...   \n",
       "4      5             Bam Adebayo   C   21  MIA  82  28  23.3   3.4   5.9  ...   \n",
       "5      6               Deng Adel  SF   21  CLE  19   3  10.2   0.6   1.9  ...   \n",
       "6      7  DeVaughn Akoon-Purcell  SG   25  DEN   7   0   3.1   0.4   1.4  ...   \n",
       "7      8       LaMarcus Aldridge   C   33  SAS  81  81  33.2   8.4  16.3  ...   \n",
       "8      9            Rawle Alkins  SG   21  CHI  10   1  12.0   1.3   3.9  ...   \n",
       "9     10           Grayson Allen  SG   23  UTA  38   2  10.9   1.8   4.7  ...   \n",
       "10    11           Jarrett Allen   C   20  BRK  80  80  26.2   4.2   7.1  ...   \n",
       "11    12            Kadeem Allen  SG   26  NYK  19   1  21.9   3.4   7.4  ...   \n",
       "12    13         Al-Farouq Aminu  PF   28  POR  81  81  28.3   3.2   7.3  ...   \n",
       "13    14         Justin Anderson  SF   25  ATL  48   4   9.6   1.3   3.3  ...   \n",
       "14    15           Kyle Anderson  SF   25  MEM  43  40  29.8   3.5   6.4  ...   \n",
       "15    16           Ryan Anderson  PF   30  TOT  25   8  12.9   0.8   2.8  ...   \n",
       "16    16           Ryan Anderson  PF   30  PHO  15   8  18.5   1.3   4.0  ...   \n",
       "17    16           Ryan Anderson  PF   30  MIA  10   0   4.4   0.2   0.9  ...   \n",
       "18    17            Ike Anigbogu   C   20  IND   3   0   2.0   0.0   1.0  ...   \n",
       "19    18   Giannis Antetokounmpo  PF   24  MIL  72  72  32.8  10.0  17.3  ...   \n",
       "20    19    Kostas Antetokounmpo  PF   21  DAL   2   0   5.5   0.0   1.5  ...   \n",
       "21    20         Carmelo Anthony  PF   34  HOU  10   2  29.4   4.9  12.1  ...   \n",
       "23    21              OG Anunoby  SF   21  TOR  67   6  20.2   2.7   6.0  ...   \n",
       "24    22        Ryan Arcidiacono  PG   24  CHI  81  32  24.2   2.3   5.2  ...   \n",
       "25    23            Trevor Ariza  SF   33  TOT  69  69  34.0   4.3  10.7  ...   \n",
       "26    23            Trevor Ariza  SF   33  PHO  26  26  34.0   3.3   8.7  ...   \n",
       "27    23            Trevor Ariza  SF   33  WAS  43  43  34.1   4.8  11.8  ...   \n",
       "28    24           D.J. Augustin  PG   31  ORL  81  81  28.0   3.9   8.4  ...   \n",
       "29    25           Deandre Ayton   C   20  PHO  71  70  30.7   7.2  12.3  ...   \n",
       "30    26            Dwayne Bacon  SG   23  CHO  43  13  17.7   2.8   6.0  ...   \n",
       "..   ...                     ...  ..  ...  ...  ..  ..   ...   ...   ...  ...   \n",
       "703  509        Hassan Whiteside   C   29  MIA  72  53  23.3   5.4   9.4  ...   \n",
       "704  510          Andrew Wiggins  SF   23  MIN  73  73  34.8   6.8  16.6  ...   \n",
       "705  511           Alan Williams  PF   26  BRK   5   0   5.2   1.6   2.6  ...   \n",
       "706  512           C.J. Williams  SG   28  MIN  15   0   8.5   1.1   2.3  ...   \n",
       "707  513      Johnathan Williams   C   23  LAL  24   0  15.5   2.7   4.6  ...   \n",
       "708  514        Kenrich Williams  SF   24  NOP  46  29  23.5   2.3   6.1  ...   \n",
       "709  515            Lou Williams  SG   32  LAC  75   1  26.6   6.5  15.2  ...   \n",
       "710  516         Marvin Williams  PF   32  CHO  75  75  28.4   3.7   8.7  ...   \n",
       "711  517         Robert Williams   C   21  BOS  32   2   8.8   1.1   1.6  ...   \n",
       "712  518           Troy Williams  SF   24  SAC  21   0  14.9   2.1   4.7  ...   \n",
       "713  519             D.J. Wilson  PF   22  MIL  48   3  18.4   2.2   5.2  ...   \n",
       "714  520         Justise Winslow  SF   22  MIA  66  52  29.7   4.9  11.3  ...   \n",
       "716  521          Christian Wood  PF   23  TOT  21   2  12.0   2.9   5.6  ...   \n",
       "717  521          Christian Wood  PF   23  MIL  13   0   4.8   0.9   1.9  ...   \n",
       "718  521          Christian Wood  PF   23  NOP   8   2  23.6   6.1  11.5  ...   \n",
       "719  522            Delon Wright  PG   26  TOT  75  13  22.7   3.2   7.4  ...   \n",
       "720  522            Delon Wright  PG   26  TOR  49   2  18.3   2.6   6.0  ...   \n",
       "721  522            Delon Wright  PG   26  MEM  26  11  30.8   4.4  10.2  ...   \n",
       "722  523      Guerschon Yabusele  PF   23  BOS  41   1   6.1   0.9   1.9  ...   \n",
       "723  524              Nick Young  SG   33  DEN   4   0   9.3   0.8   2.3  ...   \n",
       "724  525          Thaddeus Young  PF   30  IND  81  81  30.7   5.5  10.4  ...   \n",
       "725  526              Trae Young  PG   20  ATL  81  81  30.9   6.5  15.5  ...   \n",
       "726  527             Cody Zeller   C   26  CHO  49  47  25.4   3.9   7.0  ...   \n",
       "727  528            Tyler Zeller   C   29  TOT   6   1  15.5   2.7   5.0  ...   \n",
       "728  528            Tyler Zeller   C   29  ATL   2   0   5.5   0.0   1.0  ...   \n",
       "729  528            Tyler Zeller   C   29  MEM   4   1  20.5   4.0   7.0  ...   \n",
       "730  529              Ante Zizic   C   22  CLE  59  25  18.3   3.1   5.6  ...   \n",
       "731  530             Ivica Zubac   C   21  TOT  59  37  17.6   3.6   6.4  ...   \n",
       "732  530             Ivica Zubac   C   21  LAL  33  12  15.6   3.4   5.8  ...   \n",
       "733  530             Ivica Zubac   C   21  LAC  26  25  20.2   3.8   7.2  ...   \n",
       "\n",
       "       FT%  ORB   DRB   TRB  AST  STL  BLK  TOV   PF   PTS  \n",
       "0    0.923  0.2   1.4   1.5  0.6  0.5  0.2  0.5  1.7   5.3  \n",
       "1    0.700  0.3   2.2   2.5  0.8  0.1  0.4  0.4  2.4   1.7  \n",
       "2    0.778  0.3   1.4   1.8  1.9  0.4  0.1  0.8  1.3   3.2  \n",
       "3    0.500  4.9   4.6   9.5  1.6  1.5  1.0  1.7  2.6  13.9  \n",
       "4    0.735  2.0   5.3   7.3  2.2  0.9  0.8  1.5  2.5   8.9  \n",
       "5    1.000  0.2   0.8   1.0  0.3  0.1  0.2  0.3  0.7   1.7  \n",
       "6    0.500  0.1   0.4   0.6  0.9  0.3  0.0  0.3  0.6   1.0  \n",
       "7    0.847  3.1   6.1   9.2  2.4  0.5  1.3  1.8  2.2  21.3  \n",
       "8    0.667  1.1   1.5   2.6  1.3  0.1  0.0  0.8  0.7   3.7  \n",
       "9    0.750  0.1   0.5   0.6  0.7  0.2  0.2  0.9  1.2   5.6  \n",
       "10   0.709  2.4   6.0   8.4  1.4  0.5  1.5  1.3  2.3  10.9  \n",
       "11   0.778  0.4   2.3   2.7  4.0  0.8  0.2  1.4  2.4   9.9  \n",
       "12   0.867  1.4   6.1   7.5  1.3  0.8  0.4  0.9  1.8   9.4  \n",
       "13   0.743  0.5   1.3   1.8  0.5  0.5  0.3  0.5  1.0   3.7  \n",
       "14   0.578  1.1   4.7   5.8  3.0  1.3  0.9  1.3  2.6   8.0  \n",
       "15   0.750  0.7   1.4   2.2  0.8  0.2  0.0  0.6  1.0   2.5  \n",
       "16   0.786  0.9   2.1   3.0  1.1  0.2  0.1  0.8  1.5   3.7  \n",
       "17   0.500  0.4   0.5   0.9  0.2  0.1  0.0  0.2  0.2   0.7  \n",
       "18     NaN  0.3   0.7   1.0  0.3  0.0  0.3  0.3  0.0   0.0  \n",
       "19   0.729  2.2  10.3  12.5  5.9  1.3  1.5  3.7  3.2  27.7  \n",
       "20   0.500  0.0   0.5   0.5  0.0  1.0  0.0  0.5  0.0   1.0  \n",
       "21   0.682  0.9   4.5   5.4  0.5  0.4  0.7  0.8  3.2  13.4  \n",
       "23   0.581  0.9   2.1   2.9  0.7  0.7  0.3  0.8  2.1   7.0  \n",
       "24   0.873  0.3   2.4   2.7  3.3  0.8  0.0  0.8  2.1   6.7  \n",
       "25   0.793  0.7   4.7   5.4  3.7  1.3  0.3  1.5  1.9  12.5  \n",
       "26   0.837  0.6   5.0   5.6  3.3  1.5  0.3  1.5  1.7   9.9  \n",
       "27   0.777  0.8   4.5   5.3  3.8  1.2  0.3  1.6  2.0  14.1  \n",
       "28   0.866  0.5   2.0   2.5  5.3  0.6  0.0  1.6  1.4  11.7  \n",
       "29   0.746  3.1   7.1  10.3  1.8  0.9  0.9  1.8  2.9  16.3  \n",
       "30   0.739  0.2   1.9   2.1  1.1  0.3  0.1  0.4  1.7   7.3  \n",
       "..     ...  ...   ...   ...  ...  ...  ...  ...  ...   ...  \n",
       "703  0.449  3.6   7.8  11.3  0.8  0.6  1.9  1.3  2.7  12.3  \n",
       "704  0.699  1.1   3.7   4.8  2.5  1.0  0.7  1.9  2.1  18.1  \n",
       "705  0.500  0.8   3.0   3.8  0.6  0.2  0.0  0.2  0.4   3.6  \n",
       "706  0.000  0.1   0.5   0.5  0.8  0.4  0.0  0.3  1.1   2.6  \n",
       "707  0.563  2.0   2.1   4.1  0.5  0.3  0.3  0.7  2.6   6.5  \n",
       "708  0.684  1.2   3.6   4.8  1.8  1.0  0.4  0.8  2.1   6.1  \n",
       "709  0.876  0.5   2.4   3.0  5.4  0.8  0.1  2.4  1.1  20.0  \n",
       "710  0.767  1.0   4.4   5.4  1.2  0.9  0.8  0.6  2.1  10.1  \n",
       "711  0.600  0.8   1.7   2.5  0.2  0.3  1.3  0.3  1.1   2.5  \n",
       "712  0.600  0.6   2.2   2.8  0.5  0.5  0.4  0.4  1.8   5.3  \n",
       "713  0.553  0.9   3.7   4.6  1.1  0.4  0.4  0.7  1.8   5.8  \n",
       "714  0.628  1.0   4.4   5.4  4.3  1.1  0.3  2.2  2.7  12.6  \n",
       "716  0.732  0.8   3.1   4.0  0.4  0.3  0.5  0.8  0.8   8.2  \n",
       "717  0.667  0.3   1.2   1.5  0.2  0.0  0.0  0.2  0.2   2.8  \n",
       "718  0.756  1.6   6.3   7.9  0.8  0.9  1.3  1.8  1.8  16.9  \n",
       "719  0.793  0.9   2.6   3.5  3.3  1.2  0.4  1.0  1.4   8.7  \n",
       "720  0.869  0.8   1.8   2.6  2.2  0.9  0.3  0.8  1.1   6.9  \n",
       "721  0.742  1.1   4.3   5.4  5.3  1.6  0.6  1.5  1.9  12.2  \n",
       "722  0.682  0.6   0.7   1.3  0.4  0.2  0.2  0.4  0.8   2.3  \n",
       "723    NaN  0.0   0.3   0.3  0.5  0.0  0.3  0.5  1.0   2.3  \n",
       "724  0.644  2.4   4.1   6.5  2.5  1.5  0.4  1.5  2.4  12.6  \n",
       "725  0.829  0.8   2.9   3.7  8.1  0.9  0.2  3.8  1.7  19.1  \n",
       "726  0.787  2.2   4.6   6.8  2.1  0.8  0.8  1.3  3.3  10.1  \n",
       "727  0.778  1.8   2.2   4.0  0.7  0.2  0.5  0.7  3.3   7.7  \n",
       "728    NaN  1.0   2.0   3.0  0.5  0.0  0.0  0.0  2.0   0.0  \n",
       "729  0.778  2.3   2.3   4.5  0.8  0.3  0.8  1.0  4.0  11.5  \n",
       "730  0.705  1.8   3.6   5.4  0.9  0.2  0.4  1.0  1.9   7.8  \n",
       "731  0.802  1.9   4.2   6.1  1.1  0.2  0.9  1.2  2.3   8.9  \n",
       "732  0.864  1.6   3.3   4.9  0.8  0.1  0.8  1.0  2.2   8.5  \n",
       "733  0.733  2.3   5.3   7.7  1.5  0.4  0.9  1.4  2.5   9.4  \n",
       "\n",
       "[708 rows x 30 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_stats[2019]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see top 30 Points Per Game leader boards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_stats[2019].sort_values(by=['PTS'], inplace=True, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>206</td>\n",
       "      <td>James Harden</td>\n",
       "      <td>PG</td>\n",
       "      <td>29</td>\n",
       "      <td>HOU</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>36.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>24.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5.8</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>36.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>183</td>\n",
       "      <td>Paul George</td>\n",
       "      <td>SF</td>\n",
       "      <td>28</td>\n",
       "      <td>OKC</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>36.9</td>\n",
       "      <td>9.2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.839</td>\n",
       "      <td>1.4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>8.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18</td>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>PF</td>\n",
       "      <td>24</td>\n",
       "      <td>MIL</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>32.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.729</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>12.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>27.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>155</td>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>C</td>\n",
       "      <td>24</td>\n",
       "      <td>PHI</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>33.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.804</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.1</td>\n",
       "      <td>13.6</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>258</td>\n",
       "      <td>LeBron James</td>\n",
       "      <td>SF</td>\n",
       "      <td>34</td>\n",
       "      <td>LAL</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>35.2</td>\n",
       "      <td>10.1</td>\n",
       "      <td>19.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.665</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>124</td>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>PG</td>\n",
       "      <td>30</td>\n",
       "      <td>GSW</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>33.8</td>\n",
       "      <td>9.2</td>\n",
       "      <td>19.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>27.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>61</td>\n",
       "      <td>Devin Booker</td>\n",
       "      <td>SG</td>\n",
       "      <td>22</td>\n",
       "      <td>PHO</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>301</td>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>SF</td>\n",
       "      <td>27</td>\n",
       "      <td>TOR</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>34.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>18.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.854</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>150</td>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>PF</td>\n",
       "      <td>30</td>\n",
       "      <td>GSW</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>34.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>17.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>126</td>\n",
       "      <td>Anthony Davis</td>\n",
       "      <td>C</td>\n",
       "      <td>25</td>\n",
       "      <td>NOP</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>18.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.794</td>\n",
       "      <td>3.1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>305</td>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>POR</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>35.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>19.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>40</td>\n",
       "      <td>Bradley Beal</td>\n",
       "      <td>SG</td>\n",
       "      <td>25</td>\n",
       "      <td>WAS</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>36.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>19.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.808</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>496</td>\n",
       "      <td>Kemba Walker</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>CHO</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>34.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>20.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>202</td>\n",
       "      <td>Blake Griffin</td>\n",
       "      <td>PF</td>\n",
       "      <td>29</td>\n",
       "      <td>DET</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>17.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.753</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>480</td>\n",
       "      <td>Karl-Anthony Towns</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>MIN</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>33.1</td>\n",
       "      <td>8.8</td>\n",
       "      <td>17.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.836</td>\n",
       "      <td>3.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>249</td>\n",
       "      <td>Kyrie Irving</td>\n",
       "      <td>PG</td>\n",
       "      <td>26</td>\n",
       "      <td>BOS</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.873</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>354</td>\n",
       "      <td>Donovan Mitchell</td>\n",
       "      <td>SG</td>\n",
       "      <td>22</td>\n",
       "      <td>UTA</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>33.7</td>\n",
       "      <td>8.6</td>\n",
       "      <td>19.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>294</td>\n",
       "      <td>Zach LaVine</td>\n",
       "      <td>SG</td>\n",
       "      <td>23</td>\n",
       "      <td>CHI</td>\n",
       "      <td>63</td>\n",
       "      <td>62</td>\n",
       "      <td>34.5</td>\n",
       "      <td>8.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>506</td>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>PG</td>\n",
       "      <td>30</td>\n",
       "      <td>OKC</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>36.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>20.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.656</td>\n",
       "      <td>1.5</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>476</td>\n",
       "      <td>Klay Thompson</td>\n",
       "      <td>SG</td>\n",
       "      <td>28</td>\n",
       "      <td>GSW</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>416</td>\n",
       "      <td>Julius Randle</td>\n",
       "      <td>PF</td>\n",
       "      <td>24</td>\n",
       "      <td>NOP</td>\n",
       "      <td>73</td>\n",
       "      <td>49</td>\n",
       "      <td>30.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>14.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.731</td>\n",
       "      <td>2.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>84</td>\n",
       "      <td>Jimmy Butler</td>\n",
       "      <td>SG</td>\n",
       "      <td>29</td>\n",
       "      <td>MIN</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>36.1</td>\n",
       "      <td>7.4</td>\n",
       "      <td>15.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.787</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>LaMarcus Aldridge</td>\n",
       "      <td>C</td>\n",
       "      <td>33</td>\n",
       "      <td>SAS</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>33.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>16.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.847</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>230</td>\n",
       "      <td>Jrue Holiday</td>\n",
       "      <td>SG</td>\n",
       "      <td>28</td>\n",
       "      <td>NOP</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>35.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.768</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>142</td>\n",
       "      <td>Luka Doncic</td>\n",
       "      <td>SG</td>\n",
       "      <td>19</td>\n",
       "      <td>DAL</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>32.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.713</td>\n",
       "      <td>1.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>135</td>\n",
       "      <td>DeMar DeRozan</td>\n",
       "      <td>SG</td>\n",
       "      <td>29</td>\n",
       "      <td>SAS</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>34.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>17.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>112</td>\n",
       "      <td>Mike Conley</td>\n",
       "      <td>PG</td>\n",
       "      <td>31</td>\n",
       "      <td>MEM</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>33.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>433</td>\n",
       "      <td>D'Angelo Russell</td>\n",
       "      <td>PG</td>\n",
       "      <td>22</td>\n",
       "      <td>BRK</td>\n",
       "      <td>81</td>\n",
       "      <td>81</td>\n",
       "      <td>30.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>334</td>\n",
       "      <td>CJ McCollum</td>\n",
       "      <td>SG</td>\n",
       "      <td>27</td>\n",
       "      <td>POR</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>33.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>17.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>212</td>\n",
       "      <td>Tobias Harris</td>\n",
       "      <td>PF</td>\n",
       "      <td>26</td>\n",
       "      <td>LAC</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>34.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>15.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk                 Player Pos  Age   Tm   G  GS    MP    FG   FGA  ...  \\\n",
       "277  206           James Harden  PG   29  HOU  78  78  36.8  10.8  24.5  ...   \n",
       "249  183            Paul George  SF   28  OKC  77  77  36.9   9.2  21.0  ...   \n",
       "19    18  Giannis Antetokounmpo  PF   24  MIL  72  72  32.8  10.0  17.3  ...   \n",
       "207  155            Joel Embiid   C   24  PHI  64  64  33.7   9.1  18.7  ...   \n",
       "344  258           LeBron James  SF   34  LAL  55  55  35.2  10.1  19.9  ...   \n",
       "165  124          Stephen Curry  PG   30  GSW  69  69  33.8   9.2  19.4  ...   \n",
       "71    61           Devin Booker  SG   22  PHO  64  64  35.0   9.2  19.6  ...   \n",
       "412  301          Kawhi Leonard  SF   27  TOR  60  60  34.0   9.3  18.8  ...   \n",
       "198  150           Kevin Durant  PF   30  GSW  78  78  34.6   9.2  17.7  ...   \n",
       "167  126          Anthony Davis   C   25  NOP  56  56  33.0   9.5  18.3  ...   \n",
       "416  305         Damian Lillard  PG   28  POR  80  80  35.5   8.5  19.2  ...   \n",
       "48    40           Bradley Beal  SG   25  WAS  82  82  36.9   9.3  19.6  ...   \n",
       "689  496           Kemba Walker  PG   28  CHO  82  82  34.9   8.9  20.5  ...   \n",
       "271  202          Blake Griffin  PF   29  DET  75  75  35.0   8.3  17.9  ...   \n",
       "670  480     Karl-Anthony Towns   C   23  MIN  77  77  33.1   8.8  17.1  ...   \n",
       "333  249           Kyrie Irving  PG   26  BOS  67  67  33.0   9.0  18.5  ...   \n",
       "486  354       Donovan Mitchell  SG   22  UTA  77  77  33.7   8.6  19.9  ...   \n",
       "402  294            Zach LaVine  SG   23  CHI  63  62  34.5   8.4  18.0  ...   \n",
       "700  506      Russell Westbrook  PG   30  OKC  73  73  36.0   8.6  20.2  ...   \n",
       "666  476          Klay Thompson  SG   28  GSW  78  78  34.0   8.4  18.0  ...   \n",
       "574  416          Julius Randle  PF   24  NOP  73  49  30.6   7.8  14.9  ...   \n",
       "107   84           Jimmy Butler  SG   29  MIN  10  10  36.1   7.4  15.7  ...   \n",
       "7      8      LaMarcus Aldridge   C   33  SAS  81  81  33.2   8.4  16.3  ...   \n",
       "309  230           Jrue Holiday  SG   28  NOP  67  67  35.9   8.2  17.3  ...   \n",
       "188  142            Luka Doncic  SG   19  DAL  72  72  32.2   7.0  16.5  ...   \n",
       "180  135          DeMar DeRozan  SG   29  SAS  77  77  34.9   8.2  17.1  ...   \n",
       "148  112            Mike Conley  PG   31  MEM  70  70  33.5   7.0  16.0  ...   \n",
       "594  433       D'Angelo Russell  PG   22  BRK  81  81  30.2   8.1  18.7  ...   \n",
       "461  334            CJ McCollum  SG   27  POR  70  70  33.9   8.2  17.8  ...   \n",
       "284  212          Tobias Harris  PF   26  LAC  55  55  34.6   7.7  15.5  ...   \n",
       "\n",
       "       FT%  ORB   DRB   TRB   AST  STL  BLK  TOV   PF   PTS  \n",
       "277  0.879  0.8   5.8   6.6   7.5  2.0  0.7  5.0  3.1  36.1  \n",
       "249  0.839  1.4   6.8   8.2   4.1  2.2  0.4  2.7  2.8  28.0  \n",
       "19   0.729  2.2  10.3  12.5   5.9  1.3  1.5  3.7  3.2  27.7  \n",
       "207  0.804  2.5  11.1  13.6   3.7  0.7  1.9  3.5  3.3  27.5  \n",
       "344  0.665  1.0   7.4   8.5   8.3  1.3  0.6  3.6  1.7  27.4  \n",
       "165  0.916  0.7   4.7   5.3   5.2  1.3  0.4  2.8  2.4  27.3  \n",
       "71   0.866  0.6   3.5   4.1   6.8  0.9  0.2  4.1  3.1  26.6  \n",
       "412  0.854  1.3   6.0   7.3   3.3  1.8  0.4  2.0  1.5  26.6  \n",
       "198  0.885  0.4   5.9   6.4   5.9  0.7  1.1  2.9  2.0  26.0  \n",
       "167  0.794  3.1   8.9  12.0   3.9  1.6  2.4  2.0  2.4  25.9  \n",
       "416  0.912  0.9   3.8   4.6   6.9  1.1  0.4  2.7  1.9  25.8  \n",
       "48   0.808  1.1   3.9   5.0   5.5  1.5  0.7  2.7  2.8  25.6  \n",
       "689  0.844  0.6   3.8   4.4   5.9  1.2  0.4  2.6  1.6  25.6  \n",
       "271  0.753  1.3   6.2   7.5   5.4  0.7  0.4  3.4  2.7  24.5  \n",
       "670  0.836  3.4   9.0  12.4   3.4  0.9  1.6  3.1  3.8  24.4  \n",
       "333  0.873  1.1   3.9   5.0   6.9  1.5  0.5  2.6  2.5  23.8  \n",
       "486  0.806  0.8   3.3   4.1   4.2  1.4  0.4  2.8  2.7  23.8  \n",
       "402  0.832  0.6   4.0   4.7   4.5  1.0  0.4  3.4  2.2  23.7  \n",
       "700  0.656  1.5   9.6  11.1  10.7  1.9  0.5  4.5  3.4  22.9  \n",
       "666  0.816  0.5   3.4   3.8   2.4  1.1  0.6  1.5  2.0  21.5  \n",
       "574  0.731  2.2   6.5   8.7   3.1  0.7  0.6  2.8  3.4  21.4  \n",
       "107  0.787  1.6   3.6   5.2   4.3  2.4  1.0  1.4  1.8  21.3  \n",
       "7    0.847  3.1   6.1   9.2   2.4  0.5  1.3  1.8  2.2  21.3  \n",
       "309  0.768  1.1   3.9   5.0   7.7  1.6  0.8  3.1  2.2  21.2  \n",
       "188  0.713  1.2   6.6   7.8   6.0  1.1  0.3  3.4  1.9  21.2  \n",
       "180  0.830  0.7   5.3   6.0   6.2  1.1  0.5  2.6  2.3  21.2  \n",
       "148  0.845  0.6   2.8   3.4   6.4  1.3  0.3  1.9  1.8  21.1  \n",
       "594  0.780  0.7   3.2   3.9   7.0  1.2  0.2  3.1  1.7  21.1  \n",
       "461  0.828  0.9   3.1   4.0   3.0  0.8  0.4  1.5  2.5  21.0  \n",
       "284  0.877  0.7   7.2   7.9   2.7  0.7  0.4  2.0  2.2  20.9  \n",
       "\n",
       "[30 rows x 30 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_stats[2019].head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MVP Voting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get NBA normal season most valuable player voting results from Season 2000 to Season 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_num_list = ['Age', 'First', 'Pts Won', 'Pts Max', 'Share', 'G', 'MP', 'PTS', 'TRB', 'AST', 'STL', 'BLK', 'FG%', '3P%', 'FT%', 'WS', 'WS/48']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = range(2000,2019)\n",
    "MVP_voting = {}\n",
    "for year in years:\n",
    "    url = 'https://www.basketball-reference.com/awards/awards_'+str(year)+'.html'\n",
    "    html = requests.get(url).content\n",
    "    df_list = pd.read_html(html)\n",
    "    df = df_list[0]\n",
    "    df.columns = df.columns.droplevel()\n",
    "    df[to_num_list] = df[to_num_list].apply(pd.to_numeric)\n",
    "    MVP_voting[year] = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "MVP_votings = pd.DataFrame()\n",
    "for year in range(2000,2019):\n",
    "    MVP_votings = MVP_votings.append(MVP_voting[year], ignore_index=True, sort=False)\n",
    "MVP_votings.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "MVP_votings['Rank'] = MVP_votings['Rank'].map(lambda x: int(str(x[:-1])) if str(x).endswith('T') else int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275, 20)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MVP_votings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Glossary:\n",
    "    Age -- Age of Player at the start of February 1st of that season.  Tm -- Team\n",
    "    Voting  \n",
    "    First -- First Place Votes\n",
    "    Totals\n",
    "    G -- Games  Per Game  MP -- Minutes Played Per Game  PTS -- Points Per Game  TRB -- Total Rebounds Per Game\n",
    "    AST -- Assists Per Game  STL -- Steals Per Game  BLK -- Blocks Per Game\n",
    "    Shooting\n",
    "    FG% -- Field Goal Percentage  3P% -- 3-Point Field Goal Percentage  FT% -- Free Throw Percentage\n",
    "    Advanced\n",
    "    WS -- Win Shares  An estimate of the number of wins contributed by a player.\n",
    "    WS/48 -- Win Shares Per 48 Minutes  An estimate of the number of wins contributed by a player per 48 minutes (league average is approximately .100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Player</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>First</th>\n",
       "      <th>Pts Won</th>\n",
       "      <th>Pts Max</th>\n",
       "      <th>Share</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>PTS</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FT%</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/48</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Shaquille O'Neal</td>\n",
       "      <td>27</td>\n",
       "      <td>LAL</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>0.998</td>\n",
       "      <td>79</td>\n",
       "      <td>40.0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>13.6</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.524</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kevin Garnett</td>\n",
       "      <td>23</td>\n",
       "      <td>MIN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>0.337</td>\n",
       "      <td>81</td>\n",
       "      <td>40.0</td>\n",
       "      <td>22.9</td>\n",
       "      <td>11.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.765</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Alonzo Mourning</td>\n",
       "      <td>29</td>\n",
       "      <td>MIA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>0.303</td>\n",
       "      <td>79</td>\n",
       "      <td>34.8</td>\n",
       "      <td>21.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.711</td>\n",
       "      <td>12.9</td>\n",
       "      <td>0.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Karl Malone</td>\n",
       "      <td>36</td>\n",
       "      <td>UTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>0.258</td>\n",
       "      <td>82</td>\n",
       "      <td>35.9</td>\n",
       "      <td>25.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.509</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.797</td>\n",
       "      <td>15.3</td>\n",
       "      <td>0.249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Tim Duncan</td>\n",
       "      <td>23</td>\n",
       "      <td>SAS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>0.205</td>\n",
       "      <td>74</td>\n",
       "      <td>38.9</td>\n",
       "      <td>23.2</td>\n",
       "      <td>12.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.761</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Gary Payton</td>\n",
       "      <td>31</td>\n",
       "      <td>SEA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>0.149</td>\n",
       "      <td>82</td>\n",
       "      <td>41.8</td>\n",
       "      <td>24.2</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.735</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Allen Iverson</td>\n",
       "      <td>24</td>\n",
       "      <td>PHI</td>\n",
       "      <td>1.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>0.109</td>\n",
       "      <td>70</td>\n",
       "      <td>40.8</td>\n",
       "      <td>28.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.713</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Grant Hill</td>\n",
       "      <td>27</td>\n",
       "      <td>DET</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>0.093</td>\n",
       "      <td>74</td>\n",
       "      <td>37.5</td>\n",
       "      <td>25.8</td>\n",
       "      <td>6.6</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.795</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Chris Webber</td>\n",
       "      <td>26</td>\n",
       "      <td>SAC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>0.079</td>\n",
       "      <td>75</td>\n",
       "      <td>38.4</td>\n",
       "      <td>24.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.751</td>\n",
       "      <td>10.7</td>\n",
       "      <td>0.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Vince Carter</td>\n",
       "      <td>23</td>\n",
       "      <td>TOR</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>0.042</td>\n",
       "      <td>82</td>\n",
       "      <td>38.1</td>\n",
       "      <td>25.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.791</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank            Player  Age   Tm  First  Pts Won  Pts Max  Share   G    MP  \\\n",
       "0     1  Shaquille O'Neal   27  LAL  120.0   1207.0     1210  0.998  79  40.0   \n",
       "1     2     Kevin Garnett   23  MIN    0.0    408.0     1210  0.337  81  40.0   \n",
       "2     3   Alonzo Mourning   29  MIA    0.0    367.0     1210  0.303  79  34.8   \n",
       "3     4       Karl Malone   36  UTA    0.0    312.0     1210  0.258  82  35.9   \n",
       "4     5        Tim Duncan   23  SAS    0.0    248.0     1210  0.205  74  38.9   \n",
       "5     6       Gary Payton   31  SEA    0.0    180.0     1210  0.149  82  41.8   \n",
       "6     7     Allen Iverson   24  PHI    1.0    132.0     1210  0.109  70  40.8   \n",
       "7     8        Grant Hill   27  DET    0.0    113.0     1210  0.093  74  37.5   \n",
       "8     9      Chris Webber   26  SAC    0.0     96.0     1210  0.079  75  38.4   \n",
       "9    10      Vince Carter   23  TOR    0.0     51.0     1210  0.042  82  38.1   \n",
       "\n",
       "    PTS   TRB  AST  STL  BLK    FG%    3P%    FT%    WS  WS/48  \n",
       "0  29.7  13.6  3.8  0.5  3.0  0.574  0.000  0.524  18.6  0.283  \n",
       "1  22.9  11.8  5.0  1.5  1.6  0.497  0.370  0.765  11.6  0.172  \n",
       "2  21.7   9.5  1.6  0.5  3.7  0.551  0.000  0.711  12.9  0.226  \n",
       "3  25.5   9.5  3.7  1.0  0.9  0.509  0.250  0.797  15.3  0.249  \n",
       "4  23.2  12.4  3.2  0.9  2.2  0.490  0.091  0.761  13.0  0.218  \n",
       "5  24.2   6.5  8.9  1.9  0.2  0.448  0.340  0.735  13.9  0.195  \n",
       "6  28.4   3.8  4.7  2.1  0.1  0.421  0.341  0.713   6.9  0.116  \n",
       "7  25.8   6.6  5.2  1.4  0.6  0.489  0.347  0.795  10.7  0.185  \n",
       "8  24.5  10.5  4.6  1.6  1.7  0.483  0.284  0.751  10.7  0.179  \n",
       "9  25.7   5.8  3.9  1.3  1.1  0.465  0.403  0.791  11.8  0.182  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MVP_votings.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot hist of every years' MVP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "MVP = MVP_votings[MVP_votings['Rank']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12fac4b70>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MVP.Player.value_counts().plot.bar(figsize=(15.0,5.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From plot above, we can see Lebron James got 4 times normal MVP recent 19 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We drop columns which records the journalists voting results, and use other columns which records players' performance to predict their ranking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = MVP_votings.drop(labels=['Player','Rank', 'Tm', 'First', 'Pts Won', 'Pts Max', 'Share'], axis= 1)\n",
    "y = MVP_votings['Rank']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'G',\n",
       " 'MP',\n",
       " 'PTS',\n",
       " 'TRB',\n",
       " 'AST',\n",
       " 'STL',\n",
       " 'BLK',\n",
       " 'FG%',\n",
       " '3P%',\n",
       " 'FT%',\n",
       " 'WS',\n",
       " 'WS/48']"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = 2\n",
    "train_X, test_X, train_y, test_y =  train_test_split(X, y, test_size=.2, random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma = np.linspace(1e-2,1e-4, 2)\n",
    "C= np.linspace(1, 1000, 2)\n",
    "hyper_parameters = [{'kernel': ['rbf'], 'gamma': gamma,\n",
    "                     'C': C},\n",
    "                    {'kernel': ['linear'], 'C': C}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(SVC(), hyper_parameters, scoring='accuracy',cv=5,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid=[{'kernel': ['rbf'], 'gamma': array([0.01  , 0.0001]), 'C': array([   1., 1000.])}, {'kernel': ['linear'], 'C': array([   1., 1000.])}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14545454545454545"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(test_X,test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean accuracy is 0.145, not good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pred = MVP_voting[2008].drop(labels=['Player','Rank', 'Tm', 'First', 'Pts Won', 'Pts Max','Share'], axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  3,  1,  4,  6,  7, 15,  9, 10,  4,  9,  9, 14, 14, 14, 14])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(x_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Player</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>First</th>\n",
       "      <th>Pts Won</th>\n",
       "      <th>Pts Max</th>\n",
       "      <th>Share</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>PTS</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FT%</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/48</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>29</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.873</td>\n",
       "      <td>82</td>\n",
       "      <td>38.9</td>\n",
       "      <td>28.3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.840</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>22</td>\n",
       "      <td>NOH</td>\n",
       "      <td>28.0</td>\n",
       "      <td>894.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.710</td>\n",
       "      <td>80</td>\n",
       "      <td>37.6</td>\n",
       "      <td>21.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.851</td>\n",
       "      <td>17.8</td>\n",
       "      <td>0.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Kevin Garnett</td>\n",
       "      <td>31</td>\n",
       "      <td>BOS</td>\n",
       "      <td>15.0</td>\n",
       "      <td>670.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.532</td>\n",
       "      <td>71</td>\n",
       "      <td>32.8</td>\n",
       "      <td>18.8</td>\n",
       "      <td>9.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.801</td>\n",
       "      <td>12.9</td>\n",
       "      <td>0.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>LeBron James</td>\n",
       "      <td>23</td>\n",
       "      <td>CLE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.348</td>\n",
       "      <td>75</td>\n",
       "      <td>40.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.315</td>\n",
       "      <td>0.712</td>\n",
       "      <td>15.2</td>\n",
       "      <td>0.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Dwight Howard</td>\n",
       "      <td>22</td>\n",
       "      <td>ORL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.048</td>\n",
       "      <td>82</td>\n",
       "      <td>37.7</td>\n",
       "      <td>20.7</td>\n",
       "      <td>14.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.590</td>\n",
       "      <td>12.9</td>\n",
       "      <td>0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Amar'e Stoudemire</td>\n",
       "      <td>25</td>\n",
       "      <td>PHO</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.021</td>\n",
       "      <td>79</td>\n",
       "      <td>33.9</td>\n",
       "      <td>25.2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.805</td>\n",
       "      <td>14.6</td>\n",
       "      <td>0.262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Tim Duncan</td>\n",
       "      <td>31</td>\n",
       "      <td>SAS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.020</td>\n",
       "      <td>78</td>\n",
       "      <td>34.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.730</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Tracy McGrady</td>\n",
       "      <td>28</td>\n",
       "      <td>HOU</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.015</td>\n",
       "      <td>66</td>\n",
       "      <td>37.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.419</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.684</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Steve Nash</td>\n",
       "      <td>33</td>\n",
       "      <td>PHO</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.014</td>\n",
       "      <td>81</td>\n",
       "      <td>34.3</td>\n",
       "      <td>16.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.906</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Manu Ginobili</td>\n",
       "      <td>30</td>\n",
       "      <td>SAS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.007</td>\n",
       "      <td>74</td>\n",
       "      <td>31.1</td>\n",
       "      <td>19.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.860</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Dirk Nowitzki</td>\n",
       "      <td>29</td>\n",
       "      <td>DAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.004</td>\n",
       "      <td>77</td>\n",
       "      <td>36.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.879</td>\n",
       "      <td>12.9</td>\n",
       "      <td>0.223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Deron Williams</td>\n",
       "      <td>23</td>\n",
       "      <td>UTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.003</td>\n",
       "      <td>82</td>\n",
       "      <td>37.3</td>\n",
       "      <td>18.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.803</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Carmelo Anthony</td>\n",
       "      <td>23</td>\n",
       "      <td>DEN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.002</td>\n",
       "      <td>77</td>\n",
       "      <td>36.4</td>\n",
       "      <td>25.7</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.492</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.786</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14T</td>\n",
       "      <td>Carlos Boozer</td>\n",
       "      <td>26</td>\n",
       "      <td>UTA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.001</td>\n",
       "      <td>81</td>\n",
       "      <td>34.9</td>\n",
       "      <td>21.1</td>\n",
       "      <td>10.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.738</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14T</td>\n",
       "      <td>Antawn Jamison</td>\n",
       "      <td>31</td>\n",
       "      <td>WAS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.001</td>\n",
       "      <td>79</td>\n",
       "      <td>38.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>10.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.760</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14T</td>\n",
       "      <td>Paul Pierce</td>\n",
       "      <td>30</td>\n",
       "      <td>BOS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.001</td>\n",
       "      <td>80</td>\n",
       "      <td>35.9</td>\n",
       "      <td>19.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.843</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14T</td>\n",
       "      <td>Rasheed Wallace</td>\n",
       "      <td>33</td>\n",
       "      <td>DET</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.001</td>\n",
       "      <td>77</td>\n",
       "      <td>30.5</td>\n",
       "      <td>12.7</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.767</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank             Player  Age   Tm  First  Pts Won  Pts Max  Share   G  \\\n",
       "0     1        Kobe Bryant   29  LAL   82.0   1100.0     1260  0.873  82   \n",
       "1     2         Chris Paul   22  NOH   28.0    894.0     1260  0.710  80   \n",
       "2     3      Kevin Garnett   31  BOS   15.0    670.0     1260  0.532  71   \n",
       "3     4       LeBron James   23  CLE    1.0    438.0     1260  0.348  75   \n",
       "4     5      Dwight Howard   22  ORL    0.0     60.0     1260  0.048  82   \n",
       "5     6  Amar'e Stoudemire   25  PHO    0.0     27.0     1260  0.021  79   \n",
       "6     7         Tim Duncan   31  SAS    0.0     25.0     1260  0.020  78   \n",
       "7     8      Tracy McGrady   28  HOU    0.0     19.0     1260  0.015  66   \n",
       "8     9         Steve Nash   33  PHO    0.0     18.0     1260  0.014  81   \n",
       "9    10      Manu Ginobili   30  SAS    0.0      9.0     1260  0.007  74   \n",
       "10   11      Dirk Nowitzki   29  DAL    0.0      5.0     1260  0.004  77   \n",
       "11   12     Deron Williams   23  UTA    0.0      4.0     1260  0.003  82   \n",
       "12   13    Carmelo Anthony   23  DEN    0.0      3.0     1260  0.002  77   \n",
       "13  14T      Carlos Boozer   26  UTA    0.0      1.0     1260  0.001  81   \n",
       "14  14T     Antawn Jamison   31  WAS    0.0      1.0     1260  0.001  79   \n",
       "15  14T        Paul Pierce   30  BOS    0.0      1.0     1260  0.001  80   \n",
       "16  14T    Rasheed Wallace   33  DET    0.0      1.0     1260  0.001  77   \n",
       "\n",
       "      MP   PTS   TRB   AST  STL  BLK    FG%    3P%    FT%    WS  WS/48  \n",
       "0   38.9  28.3   6.3   5.4  1.8  0.5  0.459  0.361  0.840  13.8  0.208  \n",
       "1   37.6  21.1   4.0  11.6  2.7  0.1  0.488  0.369  0.851  17.8  0.284  \n",
       "2   32.8  18.8   9.2   3.4  1.4  1.3  0.539  0.000  0.801  12.9  0.265  \n",
       "3   40.4  30.0   7.9   7.2  1.8  1.1  0.484  0.315  0.712  15.2  0.242  \n",
       "4   37.7  20.7  14.2   1.3  0.9  2.1  0.599  0.000  0.590  12.9  0.200  \n",
       "5   33.9  25.2   9.1   1.5  0.8  2.1  0.590  0.161  0.805  14.6  0.262  \n",
       "6   34.0  19.3  11.3   2.8  0.7  1.9  0.497  0.000  0.730  11.1  0.201  \n",
       "7   37.0  21.6   5.1   5.9  1.0  0.5  0.419  0.292  0.684   5.8  0.115  \n",
       "8   34.3  16.9   3.5  11.1  0.7  0.1  0.504  0.470  0.906  10.5  0.181  \n",
       "9   31.1  19.5   4.8   4.5  1.5  0.4  0.460  0.401  0.860  11.1  0.232  \n",
       "10  36.0  23.6   8.6   3.5  0.7  0.9  0.479  0.359  0.879  12.9  0.223  \n",
       "11  37.3  18.8   3.0  10.5  1.1  0.3  0.507  0.395  0.803  11.3  0.177  \n",
       "12  36.4  25.7   7.4   3.4  1.3  0.5  0.492  0.354  0.786   8.2  0.140  \n",
       "13  34.9  21.1  10.4   2.9  1.2  0.5  0.547  0.000  0.738  10.2  0.173  \n",
       "14  38.7  21.4  10.2   1.5  1.3  0.4  0.436  0.339  0.760   9.2  0.144  \n",
       "15  35.9  19.6   5.1   4.5  1.3  0.5  0.464  0.392  0.843  12.4  0.207  \n",
       "16  30.5  12.7   6.6   1.8  1.2  1.7  0.432  0.356  0.767   7.6  0.155  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MVP_voting[2008]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's predict who will win Season 2019 normal MVP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, get all information we need to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.basketball-reference.com/friv/mvp.html'\n",
    "html = requests.get(url).content\n",
    "df_list = pd.read_html(html)\n",
    "df = df_list[0]\n",
    "\n",
    "drop_list = []\n",
    "for i in df.columns.tolist():\n",
    "    if i not in MVP_voting[2009].columns.tolist():\n",
    "        drop_list.append(i)\n",
    "\n",
    "df.drop(labels=drop_list, axis = 1, inplace=True)\n",
    "df['Age'] = [24, 29, 24, 27, 30, 25, 28, 31, 29, 30]\n",
    "df['WS'] = [14.4, 15.2, 11.8, 9.5, 11.5, 8.7, 12.1, 9.7, 11.9, 6.8]\n",
    "df['WS/48'] = [0.292, 0.254, 0.226, 0.224, 0.204, 0.194, 0.205, 0.199, 0.201, 0.124]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resort cloumns like the training format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "MVP_voting[2019] = df[['Player', 'Tm', 'Age', 'G', 'MP', 'PTS', 'TRB', 'AST', 'STL', 'BLK', 'FG%', '3P%', 'FT%', 'WS', 'WS/48']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using model we generated to predict the ranking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_2019 = MVP_voting[2019].drop(labels=['Player', 'Tm'], axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  1,  9, 10,  9,  4,  5,  9,  4,  2])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(predict_2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>PTS</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FT%</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/48</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>MIL</td>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "      <td>32.8</td>\n",
       "      <td>27.7</td>\n",
       "      <td>12.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.729</td>\n",
       "      <td>14.4</td>\n",
       "      <td>0.292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>HOU</td>\n",
       "      <td>29</td>\n",
       "      <td>78</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.1</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.368</td>\n",
       "      <td>0.879</td>\n",
       "      <td>15.2</td>\n",
       "      <td>0.254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nikola Jokic</td>\n",
       "      <td>DEN</td>\n",
       "      <td>24</td>\n",
       "      <td>80</td>\n",
       "      <td>31.3</td>\n",
       "      <td>20.1</td>\n",
       "      <td>10.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.821</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>TOR</td>\n",
       "      <td>27</td>\n",
       "      <td>60</td>\n",
       "      <td>34.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.854</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>GSW</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "      <td>34.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.885</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>PHI</td>\n",
       "      <td>25</td>\n",
       "      <td>64</td>\n",
       "      <td>33.7</td>\n",
       "      <td>27.5</td>\n",
       "      <td>13.6</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.804</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>POR</td>\n",
       "      <td>28</td>\n",
       "      <td>80</td>\n",
       "      <td>35.5</td>\n",
       "      <td>25.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.444</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.912</td>\n",
       "      <td>12.1</td>\n",
       "      <td>0.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>GSW</td>\n",
       "      <td>31</td>\n",
       "      <td>69</td>\n",
       "      <td>33.8</td>\n",
       "      <td>27.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.437</td>\n",
       "      <td>0.916</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Paul George</td>\n",
       "      <td>OKC</td>\n",
       "      <td>29</td>\n",
       "      <td>77</td>\n",
       "      <td>36.9</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.438</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.839</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0.201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>OKC</td>\n",
       "      <td>30</td>\n",
       "      <td>73</td>\n",
       "      <td>36.0</td>\n",
       "      <td>22.9</td>\n",
       "      <td>11.1</td>\n",
       "      <td>10.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.290</td>\n",
       "      <td>0.656</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Player   Tm  Age   G    MP   PTS   TRB   AST  STL  BLK  \\\n",
       "0  Giannis Antetokounmpo  MIL   24  72  32.8  27.7  12.5   5.9  1.3  1.5   \n",
       "1           James Harden  HOU   29  78  36.8  36.1   6.6   7.5  2.0  0.7   \n",
       "2           Nikola Jokic  DEN   24  80  31.3  20.1  10.8   7.3  1.4  0.7   \n",
       "3          Kawhi Leonard  TOR   27  60  34.0  26.6   7.3   3.3  1.8  0.4   \n",
       "4           Kevin Durant  GSW   30  78  34.6  26.0   6.4   5.9  0.7  1.1   \n",
       "5            Joel Embiid  PHI   25  64  33.7  27.5  13.6   3.7  0.7  1.9   \n",
       "6         Damian Lillard  POR   28  80  35.5  25.8   4.6   6.9  1.1  0.4   \n",
       "7          Stephen Curry  GSW   31  69  33.8  27.3   5.3   5.2  1.3  0.4   \n",
       "8            Paul George  OKC   29  77  36.9  28.0   8.2   4.1  2.2  0.4   \n",
       "9      Russell Westbrook  OKC   30  73  36.0  22.9  11.1  10.7  1.9  0.5   \n",
       "\n",
       "     FG%    3P%    FT%    WS  WS/48  \n",
       "0  0.578  0.256  0.729  14.4  0.292  \n",
       "1  0.442  0.368  0.879  15.2  0.254  \n",
       "2  0.511  0.307  0.821  11.8  0.226  \n",
       "3  0.496  0.371  0.854   9.5  0.224  \n",
       "4  0.521  0.353  0.885  11.5  0.204  \n",
       "5  0.484  0.300  0.804   8.7  0.194  \n",
       "6  0.444  0.369  0.912  12.1  0.205  \n",
       "7  0.472  0.437  0.916   9.7  0.199  \n",
       "8  0.438  0.386  0.839  11.9  0.201  \n",
       "9  0.428  0.290  0.656   6.8  0.124  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MVP_voting[2019]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the predict result we can see, James Harden may win this year's normal MVP."
   ]
  }
 ],
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