Website_Predictions.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ho7fVx11dom7"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "nnJLZJ9geBIR"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd # 1.1\n",
        "from sklearn.preprocessing import StandardScaler # 1.2\n",
        "from sklearn.model_selection import train_test_split # 1.4\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "__ixv25tcLKC"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# FOR SVR Baseline model\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "MdW4F84wkXuQ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lzk-MC-UdrsZ"
      },
      "source": [
        "# 1. Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-_0wKafeMMV"
      },
      "source": [
        "## 1.1 Get Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7GAbzMh_Cfg"
      },
      "source": [
        "### Player Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "h_-NVKFzu6YP",
        "outputId": "4f841da5-5a20-4af0-ae22-6b8d2f2e550f"
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      "outputs": [
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            "text/plain": [
              "  web-scraper-order                        web-scraper-start-url  newpage  \\\n",
              "0      1710822472-1  https://www.nba.com/stats/players/boxscores      NaN   \n",
              "1      1710822472-2  https://www.nba.com/stats/players/boxscores      NaN   \n",
              "2      1710822472-3  https://www.nba.com/stats/players/boxscores      NaN   \n",
              "3      1710822472-4  https://www.nba.com/stats/players/boxscores      NaN   \n",
              "4      1710822472-5  https://www.nba.com/stats/players/boxscores      NaN   \n",
              "\n",
              "  team      mathcup    gamedate winloss  minuters  pts  fgm  ...  dreb reb  \\\n",
              "0  ATL    ATL @ LAL  03/18/2024     NaN        13    8    4  ...     4   8   \n",
              "1  PHI  PHI vs. MIA  03/18/2024       W        19    8    3  ...     3   4   \n",
              "2  MIA    MIA @ PHI  03/18/2024       L        16   11    4  ...     1   2   \n",
              "3  MIA    MIA @ PHI  03/18/2024       L        32   20    8  ...    10  13   \n",
              "4  PHI  PHI vs. MIA  03/18/2024       W        24   12    4  ...     1   2   \n",
              "\n",
              "   ast  stl blk  tov  pfo plusminus   fp1         player  \n",
              "0    0    1   1    1    1        -7  22.6   Clint Capela  \n",
              "1    3    2   2    1    2        -1  28.3       Mo Bamba  \n",
              "2    1    1   1    0    1         1  20.9  Thomas Bryant  \n",
              "3    6    0   0    4    1       -19  40.6    Bam Adebayo  \n",
              "4    1    0   1    1    1        11  17.9    Buddy Hield  \n",
              "\n",
              "[5 rows x 29 columns]"
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              "      <th>web-scraper-order</th>\n",
              "      <th>web-scraper-start-url</th>\n",
              "      <th>newpage</th>\n",
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              "      <th>fgm</th>\n",
              "      <th>...</th>\n",
              "      <th>dreb</th>\n",
              "      <th>reb</th>\n",
              "      <th>ast</th>\n",
              "      <th>stl</th>\n",
              "      <th>blk</th>\n",
              "      <th>tov</th>\n",
              "      <th>pfo</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>1710822472-1</td>\n",
              "      <td>https://www.nba.com/stats/players/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ATL</td>\n",
              "      <td>ATL @ LAL</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-7</td>\n",
              "      <td>22.6</td>\n",
              "      <td>Clint Capela</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1710822472-2</td>\n",
              "      <td>https://www.nba.com/stats/players/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PHI</td>\n",
              "      <td>PHI vs. MIA</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>19</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
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              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>28.3</td>\n",
              "      <td>Mo Bamba</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1710822472-3</td>\n",
              "      <td>https://www.nba.com/stats/players/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MIA</td>\n",
              "      <td>MIA @ PHI</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20.9</td>\n",
              "      <td>Thomas Bryant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1710822472-4</td>\n",
              "      <td>https://www.nba.com/stats/players/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MIA</td>\n",
              "      <td>MIA @ PHI</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>32</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>-19</td>\n",
              "      <td>40.6</td>\n",
              "      <td>Bam Adebayo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1710822472-5</td>\n",
              "      <td>https://www.nba.com/stats/players/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PHI</td>\n",
              "      <td>PHI vs. MIA</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>24</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>17.9</td>\n",
              "      <td>Buddy Hield</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5 rows × 29 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "player_df"
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          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "player_data_url = 'https://raw.githubusercontent.com/BerkeAltiparmak/SportSense.AI/main/data/nba_players.csv'\n",
        "player_df = pd.read_csv(player_data_url)\n",
        "player_df.head()"
      ]
    },
    {
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      "execution_count": 15,
      "metadata": {
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        "id": "qUSk6owfva-n",
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          "data": {
            "text/plain": [
              "Index(['web-scraper-order', 'web-scraper-start-url', 'newpage', 'team',\n",
              "       'mathcup', 'gamedate', 'winloss', 'minuters', 'pts', 'fgm', 'fga',\n",
              "       'fgpercent', '3pm', '3pa', '3ppercent', 'ftm', 'fta', 'ftpercent',\n",
              "       'oreb', 'dreb', 'reb', 'ast', 'stl', 'blk', 'tov', 'pfo', 'plusminus',\n",
              "       'fp1', 'player'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "player_df.columns"
      ]
    },
    {
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      "execution_count": 16,
      "metadata": {
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          "name": "stderr",
          "text": [
            "<ipython-input-16-2dcc550019b0>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  player_df['matchup'] = player_df['mathcup']\n",
            "<ipython-input-16-2dcc550019b0>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  player_df.drop(columns=['mathcup'], inplace=True)\n"
          ]
        }
      ],
      "source": [
        "# remove unnecessary columns\n",
        "columns_to_remove = ['web-scraper-order', 'web-scraper-start-url', 'newpage', 'fp1']\n",
        "player_df.drop(columns=columns_to_remove, inplace=True)\n",
        "\n",
        "# only consider games before 2024-03-18, the time we got the data (there were incompelete games)\n",
        "player_df['gamedate'] = pd.to_datetime(player_df['gamedate'])\n",
        "player_df = player_df[player_df['gamedate'] != '2024-03-18']\n",
        "\n",
        "# NBA made a typo so we gotta fix that\n",
        "player_df['matchup'] = player_df['mathcup']\n",
        "player_df.drop(columns=['mathcup'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "FB7YDWmS1T15"
      },
      "outputs": [],
      "source": [
        "def calculate_fantasy_points(row):\n",
        "    # Points scored\n",
        "    points_scored = row['pts'] * 0.5\n",
        "    # Rebounds, Assists, Steals, and Blocks\n",
        "    rebound_points = (row['oreb'] + row['dreb']) * 1\n",
        "    assist_points = row['ast'] * 1\n",
        "    steal_points = row['stl'] * 2\n",
        "    block_points = row['blk'] * 2\n",
        "    # Turnovers\n",
        "    turnover_points = row['tov'] * -1\n",
        "    # Three-points made\n",
        "    three_points = row['3pm'] * 0.5\n",
        "    # Double-Double and Triple-Double\n",
        "    double_digits = sum(i >= 10 for i in [row['pts'], row['oreb'] + row['dreb'], row['ast'], row['stl'], row['blk']])\n",
        "    double_double_points = 1.0 if double_digits >= 2 else 0\n",
        "    triple_double_points = 2.0 if double_digits >= 3 else 0\n",
        "    # 40+ and 50+ bonuses\n",
        "    fourty_pt_bonus = 2.0 * (row['pts'] >= 40)\n",
        "    fifty_pt_bonus = 2.0 * (row['pts'] >= 50)\n",
        "    # Total fantasy points\n",
        "    total_points = (points_scored + rebound_points + assist_points +\n",
        "                    steal_points + block_points + turnover_points + three_points +\n",
        "                    double_double_points + triple_double_points +\n",
        "                    fourty_pt_bonus + fifty_pt_bonus)\n",
        "    return total_points"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "8x3z6TWG-hm9"
      },
      "outputs": [],
      "source": [
        "def get_opp_team(matchup):\n",
        "    if '@' in matchup:\n",
        "        return matchup.split(' @ ')[1]\n",
        "    elif 'vs.' in matchup:\n",
        "        return matchup.split(' vs. ')[1]\n",
        "    else:\n",
        "        return None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "6WQHyz79DilE"
      },
      "outputs": [],
      "source": [
        "# Helper function to determine home and away teams\n",
        "def extract_teams(matchup):\n",
        "    teams_a = matchup.split(' @ ')\n",
        "    teams_h = matchup.split(' vs. ')\n",
        "    if '@' in matchup:\n",
        "        return teams_a[1], teams_a[0]\n",
        "    else:\n",
        "        return teams_h[0], teams_h[1]\n",
        "\n",
        "# Function to create game_id\n",
        "def create_game_id(row):\n",
        "    home_team, away_team = extract_teams(row['matchup'])\n",
        "    game_date = row['gamedate'].strftime('%Y-%m-%d')\n",
        "    return f\"{home_team}.{away_team}.{game_date}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "7p8hNFhbyhjy",
        "outputId": "2c2f9da3-d857-4f7f-c05c-b3e7b451cb5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-211a0410e864>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  player_df['is_home'] = player_df['matchup'].apply(lambda x: 1 if 'vs.' in x else 0) # 1 if home game, 0 if away\n",
            "<ipython-input-20-211a0410e864>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  player_df['winloss'] = player_df['winloss'].apply(lambda x: 1 if 'W' in x else 0) # 1 if W, 0 if L\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    team   gamedate  winloss  minuters  pts  fgm  fga fgpercent  3pm  3pa  \\\n",
              "152  BOS 2024-03-17        1        35   14    6   15      40.0    2    5   \n",
              "153  BOS 2024-03-17        1        23    9    3    7      42.9    3    6   \n",
              "154  BOS 2024-03-17        1        20   14    5    9      55.6    0    0   \n",
              "155  WAS 2024-03-17        0        22    8    4    7      57.1    0    2   \n",
              "156  WAS 2024-03-17        0        15    0    0    1       0.0    0    1   \n",
              "\n",
              "     ... tov  pfo  plusminus            player      matchup  is_home    fp  \\\n",
              "152  ...   1    0         16  Payton Pritchard    BOS @ WAS        0  28.0   \n",
              "153  ...   0    0          7    Svi Mykhailiuk    BOS @ WAS        0  13.0   \n",
              "154  ...   1    1          9       Luke Kornet    BOS @ WAS        0  17.0   \n",
              "155  ...   0    3        -17      Johnny Davis  WAS vs. BOS        1  13.0   \n",
              "156  ...   1    2        -14    Eugene Omoruyi  WAS vs. BOS        1   3.0   \n",
              "\n",
              "     opp_team             game_id  days_since  \n",
              "152       WAS  WAS.BOS.2024-03-17         145  \n",
              "153       WAS  WAS.BOS.2024-03-17         145  \n",
              "154       WAS  WAS.BOS.2024-03-17         145  \n",
              "155       BOS  WAS.BOS.2024-03-17         145  \n",
              "156       BOS  WAS.BOS.2024-03-17         145  \n",
              "\n",
              "[5 rows x 30 columns]"
            ],
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              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>team</th>\n",
              "      <th>gamedate</th>\n",
              "      <th>winloss</th>\n",
              "      <th>minuters</th>\n",
              "      <th>pts</th>\n",
              "      <th>fgm</th>\n",
              "      <th>fga</th>\n",
              "      <th>fgpercent</th>\n",
              "      <th>3pm</th>\n",
              "      <th>3pa</th>\n",
              "      <th>...</th>\n",
              "      <th>tov</th>\n",
              "      <th>pfo</th>\n",
              "      <th>plusminus</th>\n",
              "      <th>player</th>\n",
              "      <th>matchup</th>\n",
              "      <th>is_home</th>\n",
              "      <th>fp</th>\n",
              "      <th>opp_team</th>\n",
              "      <th>game_id</th>\n",
              "      <th>days_since</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>40.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>Payton Pritchard</td>\n",
              "      <td>BOS @ WAS</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>WAS</td>\n",
              "      <td>WAS.BOS.2024-03-17</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>42.9</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>Svi Mykhailiuk</td>\n",
              "      <td>BOS @ WAS</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>WAS</td>\n",
              "      <td>WAS.BOS.2024-03-17</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>55.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>Luke Kornet</td>\n",
              "      <td>BOS @ WAS</td>\n",
              "      <td>0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>WAS</td>\n",
              "      <td>WAS.BOS.2024-03-17</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>57.1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>-17</td>\n",
              "      <td>Johnny Davis</td>\n",
              "      <td>WAS vs. BOS</td>\n",
              "      <td>1</td>\n",
              "      <td>13.0</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS.BOS.2024-03-17</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-14</td>\n",
              "      <td>Eugene Omoruyi</td>\n",
              "      <td>WAS vs. BOS</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS.BOS.2024-03-17</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-79c78b56-92f4-4ff2-9753-57a28094e20f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "player_df"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "player_df['is_home'] = player_df['matchup'].apply(lambda x: 1 if 'vs.' in x else 0) # 1 if home game, 0 if away\n",
        "player_df['winloss'] = player_df['winloss'].apply(lambda x: 1 if 'W' in x else 0) # 1 if W, 0 if L\n",
        "player_df['fp'] = player_df.apply(calculate_fantasy_points, axis=1) # calculate fantasy points\n",
        "player_df['opp_team'] = player_df['matchup'].apply(get_opp_team) # extract the opponent team name\n",
        "player_df['game_id'] = player_df.apply(create_game_id, axis=1) # calculate the game id\n",
        "\n",
        "# calculate how many days has passed since the start of the season till this game\n",
        "season_start_date = pd.Timestamp('2023-10-24')\n",
        "player_df['days_since'] = (player_df['gamedate'] - season_start_date).dt.days\n",
        "\n",
        "player_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqSatUWH_uZ6",
        "outputId": "b53105ff-79f7-4f93-a9ad-6d2942182c2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['team', 'gamedate', 'winloss', 'minuters', 'pts', 'fgm', 'fga',\n",
              "       'fgpercent', '3pm', '3pa', '3ppercent', 'ftm', 'fta', 'ftpercent',\n",
              "       'oreb', 'dreb', 'reb', 'ast', 'stl', 'blk', 'tov', 'pfo', 'plusminus',\n",
              "       'player', 'matchup', 'is_home', 'fp', 'opp_team', 'game_id',\n",
              "       'days_since'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "player_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jD-JDd2_IAL"
      },
      "source": [
        "### Team Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "emaHdwTM_Njm"
      },
      "outputs": [],
      "source": [
        "team_data_url = 'https://raw.githubusercontent.com/BerkeAltiparmak/SportSense.AI/main/data/teamscores.csv'\n",
        "team_df = pd.read_csv(team_data_url)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "eZMUMqM4_ZMl",
        "outputId": "4d4e82c6-cd6a-4903-fd73-07ba2f35ee93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  web-scraper-order                      web-scraper-start-url  newpage team  \\\n",
              "0      1710880555-1  https://www.nba.com/stats/teams/boxscores      NaN  CLE   \n",
              "1      1710880555-2  https://www.nba.com/stats/teams/boxscores      NaN  IND   \n",
              "2      1710880555-3  https://www.nba.com/stats/teams/boxscores      NaN  DET   \n",
              "3      1710880555-4  https://www.nba.com/stats/teams/boxscores      NaN  BOS   \n",
              "4      1710880555-5  https://www.nba.com/stats/teams/boxscores      NaN  PHI   \n",
              "\n",
              "       matchup        date winloss  min  pts  fgm  ...  ftpercent  oreb  dreb  \\\n",
              "0    CLE @ IND  03/18/2024       W  240  108   38  ...       71.4     9    30   \n",
              "1  IND vs. CLE  03/18/2024       L  240  103   42  ...       62.5    14    31   \n",
              "2    DET @ BOS  03/18/2024       L  240   94   36  ...       86.7     8    35   \n",
              "3  BOS vs. DET  03/18/2024       W  240  119   43  ...       84.6    15    38   \n",
              "4  PHI vs. MIA  03/18/2024       W  240   98   37  ...       91.7     7    39   \n",
              "\n",
              "   reb  ast  stl  blk  tov  pfidk  plusminus  \n",
              "0   39   27    8    4   14     18          5  \n",
              "1   45   28    8    3   11     24         -5  \n",
              "2   43   21    6    3   13     15        -25  \n",
              "3   53   30    8    8   11      9         25  \n",
              "4   46   23    7   12   12     16          7  \n",
              "\n",
              "[5 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-91138a73-23dc-490d-b9e3-a4cb9cd6c3ef\" class=\"colab-df-container\">\n",
              "    <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>web-scraper-order</th>\n",
              "      <th>web-scraper-start-url</th>\n",
              "      <th>newpage</th>\n",
              "      <th>team</th>\n",
              "      <th>matchup</th>\n",
              "      <th>date</th>\n",
              "      <th>winloss</th>\n",
              "      <th>min</th>\n",
              "      <th>pts</th>\n",
              "      <th>fgm</th>\n",
              "      <th>...</th>\n",
              "      <th>ftpercent</th>\n",
              "      <th>oreb</th>\n",
              "      <th>dreb</th>\n",
              "      <th>reb</th>\n",
              "      <th>ast</th>\n",
              "      <th>stl</th>\n",
              "      <th>blk</th>\n",
              "      <th>tov</th>\n",
              "      <th>pfidk</th>\n",
              "      <th>plusminus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1710880555-1</td>\n",
              "      <td>https://www.nba.com/stats/teams/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CLE</td>\n",
              "      <td>CLE @ IND</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>240</td>\n",
              "      <td>108</td>\n",
              "      <td>38</td>\n",
              "      <td>...</td>\n",
              "      <td>71.4</td>\n",
              "      <td>9</td>\n",
              "      <td>30</td>\n",
              "      <td>39</td>\n",
              "      <td>27</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1710880555-2</td>\n",
              "      <td>https://www.nba.com/stats/teams/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IND</td>\n",
              "      <td>IND vs. CLE</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>240</td>\n",
              "      <td>103</td>\n",
              "      <td>42</td>\n",
              "      <td>...</td>\n",
              "      <td>62.5</td>\n",
              "      <td>14</td>\n",
              "      <td>31</td>\n",
              "      <td>45</td>\n",
              "      <td>28</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>24</td>\n",
              "      <td>-5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1710880555-3</td>\n",
              "      <td>https://www.nba.com/stats/teams/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DET</td>\n",
              "      <td>DET @ BOS</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>240</td>\n",
              "      <td>94</td>\n",
              "      <td>36</td>\n",
              "      <td>...</td>\n",
              "      <td>86.7</td>\n",
              "      <td>8</td>\n",
              "      <td>35</td>\n",
              "      <td>43</td>\n",
              "      <td>21</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1710880555-4</td>\n",
              "      <td>https://www.nba.com/stats/teams/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BOS</td>\n",
              "      <td>BOS vs. DET</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>240</td>\n",
              "      <td>119</td>\n",
              "      <td>43</td>\n",
              "      <td>...</td>\n",
              "      <td>84.6</td>\n",
              "      <td>15</td>\n",
              "      <td>38</td>\n",
              "      <td>53</td>\n",
              "      <td>30</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1710880555-5</td>\n",
              "      <td>https://www.nba.com/stats/teams/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PHI</td>\n",
              "      <td>PHI vs. MIA</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>240</td>\n",
              "      <td>98</td>\n",
              "      <td>37</td>\n",
              "      <td>...</td>\n",
              "      <td>91.7</td>\n",
              "      <td>7</td>\n",
              "      <td>39</td>\n",
              "      <td>46</td>\n",
              "      <td>23</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>16</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91138a73-23dc-490d-b9e3-a4cb9cd6c3ef')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      margin-bottom: 4px;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-91138a73-23dc-490d-b9e3-a4cb9cd6c3ef button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-91138a73-23dc-490d-b9e3-a4cb9cd6c3ef');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0be9fd67-3468-4a53-bf71-245a0e604022 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "team_df"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "team_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyXPc7cA_bgl",
        "outputId": "7a327cc1-1908-486f-e7cd-94d210dff674"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['web-scraper-order', 'web-scraper-start-url', 'newpage', 'team',\n",
              "       'matchup', 'date', 'winloss', 'min', 'pts', 'fgm', 'fga', 'fgpercent',\n",
              "       '3pm', '3pa', '3ppercent', 'ftm', 'fta', 'ftpercent', 'oreb', 'dreb',\n",
              "       'reb', 'ast', 'stl', 'blk', 'tov', 'pfidk', 'plusminus'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "team_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "ak_AizVQ_85D"
      },
      "outputs": [],
      "source": [
        "# remove unnecessary columns\n",
        "columns_to_remove = ['web-scraper-order', 'web-scraper-start-url', 'newpage']\n",
        "team_df.drop(columns=columns_to_remove, inplace=True)\n",
        "\n",
        "# only consider games before 2024-03-18, the time we got the data (there were incompelete games)\n",
        "team_df['gamedate'] = pd.to_datetime(team_df['date'])\n",
        "team_df = team_df[team_df['gamedate'] != '2024-03-18']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "cQbVdRnQAjRc",
        "outputId": "1ae1d409-f8a6-4f91-dbdc-c01a13bfda51"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-a5d1a32ab59e>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  team_df['is_home'] = team_df['matchup'].apply(lambda x: 1 if 'vs.' in x else 0)\n",
            "<ipython-input-26-a5d1a32ab59e>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  team_df['opp_team'] = team_df['matchup'].apply(get_opp_team)\n",
            "<ipython-input-26-a5d1a32ab59e>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  team_df['game_id'] = team_df.apply(create_game_id, axis=1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   team      matchup        date winloss  min  pts  fgm  fga  fgpercent  3pm  \\\n",
              "16  LAC  LAC vs. ATL  03/17/2024       L  240   93   28   74       37.8   11   \n",
              "17  ATL    ATL @ LAC  03/17/2024       W  240  110   41   84       48.8   17   \n",
              "18  BKN    BKN @ SAS  03/17/2024       L  265  115   47  102       46.1    8   \n",
              "19  SAS  SAS vs. BKN  03/17/2024       W  265  122   46   99       46.5    9   \n",
              "20  ORL  ORL vs. TOR  03/17/2024       W  240  111   43   85       50.6   11   \n",
              "\n",
              "    ...  ast  stl  blk  tov  pfidk  plusminus   gamedate  is_home  opp_team  \\\n",
              "16  ...   17   10    5   16     13        -17 2024-03-17        1       ATL   \n",
              "17  ...   32   10    7   16     22         17 2024-03-17        0       LAC   \n",
              "18  ...   29   13    5   12     20         -7 2024-03-17        0       SAS   \n",
              "19  ...   30    6   10   18     17          7 2024-03-17        1       BKN   \n",
              "20  ...   22    7    4   15     16         15 2024-03-17        1       TOR   \n",
              "\n",
              "               game_id  \n",
              "16  LAC.ATL.2024-03-17  \n",
              "17  LAC.ATL.2024-03-17  \n",
              "18  SAS.BKN.2024-03-17  \n",
              "19  SAS.BKN.2024-03-17  \n",
              "20  ORL.TOR.2024-03-17  \n",
              "\n",
              "[5 rows x 28 columns]"
            ],
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>team</th>\n",
              "      <th>matchup</th>\n",
              "      <th>date</th>\n",
              "      <th>winloss</th>\n",
              "      <th>min</th>\n",
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              "      <th>fgm</th>\n",
              "      <th>fga</th>\n",
              "      <th>fgpercent</th>\n",
              "      <th>3pm</th>\n",
              "      <th>...</th>\n",
              "      <th>ast</th>\n",
              "      <th>stl</th>\n",
              "      <th>blk</th>\n",
              "      <th>tov</th>\n",
              "      <th>pfidk</th>\n",
              "      <th>plusminus</th>\n",
              "      <th>gamedate</th>\n",
              "      <th>is_home</th>\n",
              "      <th>opp_team</th>\n",
              "      <th>game_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>LAC</td>\n",
              "      <td>LAC vs. ATL</td>\n",
              "      <td>03/17/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>240</td>\n",
              "      <td>93</td>\n",
              "      <td>28</td>\n",
              "      <td>74</td>\n",
              "      <td>37.8</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
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              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>-17</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>ATL</td>\n",
              "      <td>LAC.ATL.2024-03-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>ATL</td>\n",
              "      <td>ATL @ LAC</td>\n",
              "      <td>03/17/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>240</td>\n",
              "      <td>110</td>\n",
              "      <td>41</td>\n",
              "      <td>84</td>\n",
              "      <td>48.8</td>\n",
              "      <td>17</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>17</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>0</td>\n",
              "      <td>LAC</td>\n",
              "      <td>LAC.ATL.2024-03-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>BKN</td>\n",
              "      <td>BKN @ SAS</td>\n",
              "      <td>03/17/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>265</td>\n",
              "      <td>115</td>\n",
              "      <td>47</td>\n",
              "      <td>102</td>\n",
              "      <td>46.1</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>29</td>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>-7</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>0</td>\n",
              "      <td>SAS</td>\n",
              "      <td>SAS.BKN.2024-03-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>SAS</td>\n",
              "      <td>SAS vs. BKN</td>\n",
              "      <td>03/17/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>265</td>\n",
              "      <td>122</td>\n",
              "      <td>46</td>\n",
              "      <td>99</td>\n",
              "      <td>46.5</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>18</td>\n",
              "      <td>17</td>\n",
              "      <td>7</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>BKN</td>\n",
              "      <td>SAS.BKN.2024-03-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>ORL</td>\n",
              "      <td>ORL vs. TOR</td>\n",
              "      <td>03/17/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>240</td>\n",
              "      <td>111</td>\n",
              "      <td>43</td>\n",
              "      <td>85</td>\n",
              "      <td>50.6</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>22</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>15</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>TOR</td>\n",
              "      <td>ORL.TOR.2024-03-17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 28 columns</p>\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "team_df"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "team_df['is_home'] = team_df['matchup'].apply(lambda x: 1 if 'vs.' in x else 0)\n",
        "team_df['opp_team'] = team_df['matchup'].apply(get_opp_team)\n",
        "team_df['game_id'] = team_df.apply(create_game_id, axis=1)\n",
        "team_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7bJqVyuxHo21"
      },
      "source": [
        "### Merging"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "cToVxX64HHyZ"
      },
      "outputs": [],
      "source": [
        "own_team_df = team_df.copy()\n",
        "opp_team_df = team_df.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "iaYWRZUdH20J"
      },
      "outputs": [],
      "source": [
        "# renaming columns for the team data\n",
        "team_rename_columns = {\n",
        "    'pts': 'team_pts',\n",
        "    'fgm': 'team_fgm',\n",
        "    'fga': 'team_fga',\n",
        "    'fgpercent': 'team_fgpercent',\n",
        "    '3pm': 'team_3pm',\n",
        "    '3pa': 'team_3pa',\n",
        "    '3ppercent': 'team_3ppercent',\n",
        "    'ftm': 'team_ftm',\n",
        "    'fta': 'team_fta',\n",
        "    'ftpercent': 'team_ftpercent',\n",
        "    'oreb': 'team_oreb',\n",
        "    'dreb': 'team_dreb',\n",
        "    'reb': 'team_reb',\n",
        "    'ast': 'team_ast',\n",
        "    'stl': 'team_stl',\n",
        "    'blk': 'team_blk',\n",
        "    'tov': 'team_tov',\n",
        "    'pfidk': 'team_pfidk',\n",
        "    'plusminus': 'team_plusminus'\n",
        "}\n",
        "\n",
        "# renaming columns for the opponent team data\n",
        "opp_rename_columns = {\n",
        "    'pts': 'opp_pts',\n",
        "    'fgm': 'opp_fgm',\n",
        "    'fga': 'opp_fga',\n",
        "    'fgpercent': 'opp_fgpercent',\n",
        "    '3pm': 'opp_3pm',\n",
        "    '3pa': 'opp_3pa',\n",
        "    '3ppercent': 'opp_3ppercent',\n",
        "    'ftm': 'opp_ftm',\n",
        "    'fta': 'opp_fta',\n",
        "    'ftpercent': 'opp_ftpercent',\n",
        "    'oreb': 'opp_oreb',\n",
        "    'dreb': 'opp_dreb',\n",
        "    'reb': 'opp_reb',\n",
        "    'ast': 'opp_ast',\n",
        "    'stl': 'opp_stl',\n",
        "    'blk': 'opp_blk',\n",
        "    'tov': 'opp_tov',\n",
        "    'pfidk': 'opp_pfidk',\n",
        "    'plusminus': 'opp_plusminus'\n",
        "}\n",
        "\n",
        "own_team_df.rename(columns=team_rename_columns, inplace=True)\n",
        "opp_team_df.rename(columns=opp_rename_columns, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S1K3T_OyLAYd",
        "outputId": "ff0b5905-5b44-4131-d237-44c9dfc993fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['team', 'matchup', 'date', 'winloss', 'min', 'opp_pts', 'opp_fgm',\n",
              "       'opp_fga', 'opp_fgpercent', 'opp_3pm', 'opp_3pa', 'opp_3ppercent',\n",
              "       'opp_ftm', 'opp_fta', 'opp_ftpercent', 'opp_oreb', 'opp_dreb',\n",
              "       'opp_reb', 'opp_ast', 'opp_stl', 'opp_blk', 'opp_tov', 'opp_pfidk',\n",
              "       'opp_plusminus', 'gamedate', 'is_home', 'opp_team', 'game_id'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "opp_team_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "pxFkDlePI_nM"
      },
      "outputs": [],
      "source": [
        "# merging player data with own team data\n",
        "initial_merged_df = pd.merge(player_df, own_team_df, left_on=['game_id', 'team'], right_on=['game_id', 'team'], suffixes=('', '_drop'))\n",
        "# drop the columns from team_df that are redundant\n",
        "initial_merged_df.drop([col for col in initial_merged_df.columns if 'drop' in col], axis=1, inplace=True)\n",
        "\n",
        "# merging the last df with opponent team data\n",
        "df = pd.merge(initial_merged_df, opp_team_df, left_on=['game_id', 'opp_team'], right_on=['game_id', 'team'], suffixes=('', '_drop'))\n",
        "# drop the columns from opp_team_df that are redundant\n",
        "df.drop([col for col in df.columns if 'drop' in col], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "iG-w-RMA7DWx",
        "outputId": "fdf86cc6-3645-4328-e10f-fc95e75fe1dd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             player team opp_team   gamedate  days_since  winloss  minuters  \\\n",
              "0  Payton Pritchard  BOS      WAS 2024-03-17         145        1        35   \n",
              "1    Svi Mykhailiuk  BOS      WAS 2024-03-17         145        1        23   \n",
              "2       Luke Kornet  BOS      WAS 2024-03-17         145        1        20   \n",
              "3        Sam Hauser  BOS      WAS 2024-03-17         145        1        23   \n",
              "4     Neemias Queta  BOS      WAS 2024-03-17         145        1         8   \n",
              "\n",
              "   pts  fgm  fga  ... opp_oreb  opp_dreb  opp_reb opp_ast  opp_stl  opp_blk  \\\n",
              "0   14    6   15  ...       10        30       40      24        3        5   \n",
              "1    9    3    7  ...       10        30       40      24        3        5   \n",
              "2   14    5    9  ...       10        30       40      24        3        5   \n",
              "3   30   10   13  ...       10        30       40      24        3        5   \n",
              "4    0    0    0  ...       10        30       40      24        3        5   \n",
              "\n",
              "  opp_tov  opp_pfidk  opp_plusminus    fp  \n",
              "0      16         20            -26  28.0  \n",
              "1      16         20            -26  13.0  \n",
              "2      16         20            -26  17.0  \n",
              "3      16         20            -26  29.0  \n",
              "4      16         20            -26   1.0  \n",
              "\n",
              "[5 rows x 70 columns]"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player</th>\n",
              "      <th>team</th>\n",
              "      <th>opp_team</th>\n",
              "      <th>gamedate</th>\n",
              "      <th>days_since</th>\n",
              "      <th>winloss</th>\n",
              "      <th>minuters</th>\n",
              "      <th>pts</th>\n",
              "      <th>fgm</th>\n",
              "      <th>fga</th>\n",
              "      <th>...</th>\n",
              "      <th>opp_oreb</th>\n",
              "      <th>opp_dreb</th>\n",
              "      <th>opp_reb</th>\n",
              "      <th>opp_ast</th>\n",
              "      <th>opp_stl</th>\n",
              "      <th>opp_blk</th>\n",
              "      <th>opp_tov</th>\n",
              "      <th>opp_pfidk</th>\n",
              "      <th>opp_plusminus</th>\n",
              "      <th>fp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Payton Pritchard</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>30</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
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              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Svi Mykhailiuk</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>30</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Luke Kornet</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>30</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sam Hauser</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>...</td>\n",
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              "      <td>30</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Neemias Queta</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>24</td>\n",
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              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 70 columns</p>\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-52709f76-188d-4ac1-b3b7-5bef0ebcaa3a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# reorder the columns for visual purposes\n",
        "columns_order = ['player', 'team', 'opp_team', 'gamedate', 'days_since'] + [col for col in df.columns if col not in ['player', 'team', 'opp_team', 'gamedate', 'days_since', 'fp']] + ['fp']\n",
        "df = df[columns_order]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qxKM4CjKAbE",
        "outputId": "fac9a268-f743-4171-b17b-b1538826064c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['player', 'team', 'opp_team', 'gamedate', 'days_since', 'winloss',\n",
              "       'minuters', 'pts', 'fgm', 'fga', 'fgpercent', '3pm', '3pa', '3ppercent',\n",
              "       'ftm', 'fta', 'ftpercent', 'oreb', 'dreb', 'reb', 'ast', 'stl', 'blk',\n",
              "       'tov', 'pfo', 'plusminus', 'matchup', 'is_home', 'game_id', 'date',\n",
              "       'min', 'team_pts', 'team_fgm', 'team_fga', 'team_fgpercent', 'team_3pm',\n",
              "       'team_3pa', 'team_3ppercent', 'team_ftm', 'team_fta', 'team_ftpercent',\n",
              "       'team_oreb', 'team_dreb', 'team_reb', 'team_ast', 'team_stl',\n",
              "       'team_blk', 'team_tov', 'team_pfidk', 'team_plusminus', 'opp_pts',\n",
              "       'opp_fgm', 'opp_fga', 'opp_fgpercent', 'opp_3pm', 'opp_3pa',\n",
              "       'opp_3ppercent', 'opp_ftm', 'opp_fta', 'opp_ftpercent', 'opp_oreb',\n",
              "       'opp_dreb', 'opp_reb', 'opp_ast', 'opp_stl', 'opp_blk', 'opp_tov',\n",
              "       'opp_pfidk', 'opp_plusminus', 'fp'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ob3lrIAtdtXO"
      },
      "source": [
        "## 1.2 Z-Score Normalization / Standardization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "w0NPMa0sdyZ1",
        "outputId": "2d04c28a-6bbe-4954-f9fe-d22d1bfd7aa9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\n# Select the columns with player stats\\nstats_columns = ['points', 'rebounds', 'assists', 'steals', 'blocks']  # Make sure we got all the columns here\\n\\n# Initialize the scaler\\nscaler = StandardScaler()\\n\\n# Normalize the stats using Z-score normalization\\ndf[stats_columns] = scaler.fit_transform(df[stats_columns])\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "\"\"\"\n",
        "# Select the columns with player stats\n",
        "stats_columns = ['points', 'rebounds', 'assists', 'steals', 'blocks']  # Make sure we got all the columns here\n",
        "\n",
        "# Initialize the scaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Normalize the stats using Z-score normalization\n",
        "df[stats_columns] = scaler.fit_transform(df[stats_columns])\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3m-nQTec3aR"
      },
      "source": [
        "## 1.25: EXPERIMENTAL REMOVING OF MORE COLUMNS TO SIMPLIFY THE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "m9lTocQZc352"
      },
      "outputs": [],
      "source": [
        "# EXPERIMENTAL:\n",
        "more_columns_to_remove = \\\n",
        " ['opp_team', 'fgm', 'fga', 'fgpercent', '3pm', '3pa', '3ppercent', 'ftm',\n",
        "  'fta', 'ftpercent', 'pfo', 'plusminus', 'matchup', 'is_home', 'date', 'min',\n",
        "  'team_pts', 'team_fgm', 'team_fga', 'team_fgpercent', 'team_3pm', 'team_3pa',\n",
        "  'team_3ppercent', 'team_ftm', 'team_fta', 'team_ftpercent', 'team_oreb',\n",
        "  'team_dreb', 'team_reb', 'team_ast', 'team_stl', 'team_blk', 'team_tov',\n",
        "  'team_pfidk', 'team_plusminus', 'opp_pts', 'opp_fgm', 'opp_fga',\n",
        "  'opp_fgpercent', 'opp_3pm', 'opp_3pa', 'opp_3ppercent', 'opp_ftm', 'opp_fta',\n",
        "  'opp_ftpercent', 'opp_oreb', 'opp_dreb', 'opp_reb', 'opp_ast', 'opp_stl',\n",
        "  'opp_blk', 'opp_tov', 'opp_pfidk', 'opp_plusminus']\n",
        "# df.drop(columns=more_columns_to_remove, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvZASnxSdN1Q",
        "outputId": "fe52e3f5-d024-4552-89df-02bfa11c812a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['player', 'team', 'opp_team', 'gamedate', 'days_since', 'winloss',\n",
              "       'minuters', 'pts', 'fgm', 'fga', 'fgpercent', '3pm', '3pa', '3ppercent',\n",
              "       'ftm', 'fta', 'ftpercent', 'oreb', 'dreb', 'reb', 'ast', 'stl', 'blk',\n",
              "       'tov', 'pfo', 'plusminus', 'matchup', 'is_home', 'game_id', 'date',\n",
              "       'min', 'team_pts', 'team_fgm', 'team_fga', 'team_fgpercent', 'team_3pm',\n",
              "       'team_3pa', 'team_3ppercent', 'team_ftm', 'team_fta', 'team_ftpercent',\n",
              "       'team_oreb', 'team_dreb', 'team_reb', 'team_ast', 'team_stl',\n",
              "       'team_blk', 'team_tov', 'team_pfidk', 'team_plusminus', 'opp_pts',\n",
              "       'opp_fgm', 'opp_fga', 'opp_fgpercent', 'opp_3pm', 'opp_3pa',\n",
              "       'opp_3ppercent', 'opp_ftm', 'opp_fta', 'opp_ftpercent', 'opp_oreb',\n",
              "       'opp_dreb', 'opp_reb', 'opp_ast', 'opp_stl', 'opp_blk', 'opp_tov',\n",
              "       'opp_pfidk', 'opp_plusminus', 'fp'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a boolean mask\n",
        "mask = (df == '-')\n",
        "\n",
        "# Use `any` to check each column\n",
        "columns_with_dash = mask.any(axis=0)\n",
        "\n",
        "# Print the columns that contain '-'\n",
        "print(columns_with_dash[columns_with_dash].index.tolist())\n",
        "\n",
        "# Convert specific columns to strings\n",
        "df[['fgpercent', '3ppercent', 'ftpercent']] = df[['fgpercent', '3ppercent', 'ftpercent']].astype(str)\n",
        "\n",
        "# Replace '-' with '0'\n",
        "df[['fgpercent', '3ppercent', 'ftpercent']] = df[['fgpercent', '3ppercent', 'ftpercent']].replace('-', '0')\n",
        "\n",
        "# Convert these columns back to floats\n",
        "df[['fgpercent', '3ppercent', 'ftpercent']] = df[['fgpercent', '3ppercent', 'ftpercent']].astype(float)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_aZcyqavOKkf",
        "outputId": "99d27b54-52b5-4325-8517-b830e62ad43e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['fgpercent', '3ppercent', 'ftpercent']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3kuJsMZiTha"
      },
      "source": [
        "## 1.275: EXPERIMENTAL REMOVING OF PLAYERS THAT HAVENT PLAYED 10 GAMES AND THAT ARE NOT IN TOP 150"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8v98YDscKUV_"
      },
      "source": [
        "1.29. ADDING MORE FEATURES FOR DEBUGGING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "Wlc4a5S_I9-6"
      },
      "outputs": [],
      "source": [
        "players_list = list(df.groupby('player')['fp'].mean().reset_index().sort_values(by='fp', ascending=False)['player'])\n",
        "players_dict = {players_list[i]: i for i in range(len(players_list))}\n",
        "players_inv_dict = {i: players_list[i] for i in range(len(players_list))}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GTVskQl_LJnR",
        "outputId": "5d3df0ff-df8c-4b62-8c52-74ac8a043a23"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3017"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "int(str(df.iloc[132]['gamedate'])[-14:-12]) * 1000 + int(str(df.iloc[132]['gamedate'])[-11:-9])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "30RO0MWKKzQ4"
      },
      "outputs": [],
      "source": [
        "def intify_player(player):\n",
        "    return players_dict[player] / 1000\n",
        "\n",
        "def intify_gamedate(gamedate):\n",
        "    return (int(str(gamedate)[-14:-12]) * 1000 + int(str(gamedate)[-11:-9])) / 100000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "atlH9ZfCKv1k"
      },
      "outputs": [],
      "source": [
        "df['int_player'] = df['player'].apply(intify_player)\n",
        "df['int_gamedate'] = df['gamedate'].apply(intify_gamedate)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "3D3tWAoFMVKn",
        "outputId": "4a7071c2-d721-495b-e370-6405b055597a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             player team opp_team   gamedate  days_since  winloss  minuters  \\\n",
              "0  Payton Pritchard  BOS      WAS 2024-03-17         145        1        35   \n",
              "1    Svi Mykhailiuk  BOS      WAS 2024-03-17         145        1        23   \n",
              "2       Luke Kornet  BOS      WAS 2024-03-17         145        1        20   \n",
              "3        Sam Hauser  BOS      WAS 2024-03-17         145        1        23   \n",
              "4     Neemias Queta  BOS      WAS 2024-03-17         145        1         8   \n",
              "\n",
              "   pts  fgm  fga  ...  opp_reb  opp_ast  opp_stl  opp_blk  opp_tov  opp_pfidk  \\\n",
              "0   14    6   15  ...       40       24        3        5       16         20   \n",
              "1    9    3    7  ...       40       24        3        5       16         20   \n",
              "2   14    5    9  ...       40       24        3        5       16         20   \n",
              "3   30   10   13  ...       40       24        3        5       16         20   \n",
              "4    0    0    0  ...       40       24        3        5       16         20   \n",
              "\n",
              "   opp_plusminus    fp  int_player  int_gamedate  \n",
              "0            -26  28.0       0.230       0.03017  \n",
              "1            -26  13.0       0.455       0.03017  \n",
              "2            -26  17.0       0.263       0.03017  \n",
              "3            -26  29.0       0.240       0.03017  \n",
              "4            -26   1.0       0.295       0.03017  \n",
              "\n",
              "[5 rows x 72 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f7eae12c-f75f-4f88-bf07-3f431b21586a\" class=\"colab-df-container\">\n",
              "    <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>team</th>\n",
              "      <th>opp_team</th>\n",
              "      <th>gamedate</th>\n",
              "      <th>days_since</th>\n",
              "      <th>winloss</th>\n",
              "      <th>minuters</th>\n",
              "      <th>pts</th>\n",
              "      <th>fgm</th>\n",
              "      <th>fga</th>\n",
              "      <th>...</th>\n",
              "      <th>opp_reb</th>\n",
              "      <th>opp_ast</th>\n",
              "      <th>opp_stl</th>\n",
              "      <th>opp_blk</th>\n",
              "      <th>opp_tov</th>\n",
              "      <th>opp_pfidk</th>\n",
              "      <th>opp_plusminus</th>\n",
              "      <th>fp</th>\n",
              "      <th>int_player</th>\n",
              "      <th>int_gamedate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Payton Pritchard</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>...</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.230</td>\n",
              "      <td>0.03017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Svi Mykhailiuk</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.455</td>\n",
              "      <td>0.03017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Luke Kornet</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.263</td>\n",
              "      <td>0.03017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sam Hauser</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>...</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.240</td>\n",
              "      <td>0.03017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Neemias Queta</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.295</td>\n",
              "      <td>0.03017</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 72 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7eae12c-f75f-4f88-bf07-3f431b21586a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f7eae12c-f75f-4f88-bf07-3f431b21586a');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9AzA7_sdyp3"
      },
      "source": [
        "## 1.3 One-Hot Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "9WjkfPqod29d"
      },
      "outputs": [],
      "source": [
        "# one-hot encode the categorical columns\n",
        "df = pd.get_dummies(df, columns=['opp_team'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "V0PoMHUe8GLi"
      },
      "outputs": [],
      "source": [
        "## reorder the columns for visual purposes\n",
        "columns_order = ['player', 'team', 'gamedate', 'days_since'] + [col for col in df.columns if col not in ['player', 'team', 'gamedate', 'days_since', 'fp']] + ['fp']\n",
        "df = df[columns_order]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "-rFgNHIx-w6i",
        "outputId": "43a75de4-9247-484c-fb83-be0ef82f92ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             player team   gamedate  days_since  winloss  minuters  pts  fgm  \\\n",
              "0  Payton Pritchard  BOS 2024-03-17         145        1        35   14    6   \n",
              "1    Svi Mykhailiuk  BOS 2024-03-17         145        1        23    9    3   \n",
              "2       Luke Kornet  BOS 2024-03-17         145        1        20   14    5   \n",
              "3        Sam Hauser  BOS 2024-03-17         145        1        23   30   10   \n",
              "4     Neemias Queta  BOS 2024-03-17         145        1         8    0    0   \n",
              "\n",
              "   fga  fgpercent  ...  opp_team_ORL  opp_team_PHI  opp_team_PHX  \\\n",
              "0   15       40.0  ...         False         False         False   \n",
              "1    7       42.9  ...         False         False         False   \n",
              "2    9       55.6  ...         False         False         False   \n",
              "3   13       76.9  ...         False         False         False   \n",
              "4    0        0.0  ...         False         False         False   \n",
              "\n",
              "   opp_team_POR  opp_team_SAC  opp_team_SAS  opp_team_TOR  opp_team_UTA  \\\n",
              "0         False         False         False         False         False   \n",
              "1         False         False         False         False         False   \n",
              "2         False         False         False         False         False   \n",
              "3         False         False         False         False         False   \n",
              "4         False         False         False         False         False   \n",
              "\n",
              "   opp_team_WAS    fp  \n",
              "0          True  28.0  \n",
              "1          True  13.0  \n",
              "2          True  17.0  \n",
              "3          True  29.0  \n",
              "4          True   1.0  \n",
              "\n",
              "[5 rows x 101 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player</th>\n",
              "      <th>team</th>\n",
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              "      <th>opp_team_SAC</th>\n",
              "      <th>opp_team_SAS</th>\n",
              "      <th>opp_team_TOR</th>\n",
              "      <th>opp_team_UTA</th>\n",
              "      <th>opp_team_WAS</th>\n",
              "      <th>fp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Payton Pritchard</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Svi Mykhailiuk</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
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              "      <td>42.9</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Luke Kornet</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
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              "      <td>55.6</td>\n",
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              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>Sam Hauser</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
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              "      <td>76.9</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Neemias Queta</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 101 columns</p>\n",
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f045e1e1-0f20-49f4-b86f-0aa4c11fddfa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kpufSpxwL3OE",
        "outputId": "e76355c3-bb70-493c-884f-a51ea6291d0d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['player', 'team', 'gamedate', 'days_since', 'winloss', 'minuters',\n",
              "       'pts', 'fgm', 'fga', 'fgpercent',\n",
              "       ...\n",
              "       'opp_team_ORL', 'opp_team_PHI', 'opp_team_PHX', 'opp_team_POR',\n",
              "       'opp_team_SAC', 'opp_team_SAS', 'opp_team_TOR', 'opp_team_UTA',\n",
              "       'opp_team_WAS', 'fp'],\n",
              "      dtype='object', length=101)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrcKBV9PL56t"
      },
      "source": [
        "## 1.4 Split per player\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "hxu0-uMqTcJQ"
      },
      "outputs": [],
      "source": [
        "class NBAFantasyDataset(Dataset):\n",
        "    def __init__(self, dataframe, look_back=10):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            dataframe (pandas.DataFrame): The dataframe containing the data.\n",
        "        \"\"\"\n",
        "        self.dataframe = dataframe\n",
        "        # player + game_date logic:\n",
        "        self.player_game_indices = self._get_player_game_indices()\n",
        "        # for normalization:\n",
        "        self.scaler = StandardScaler()\n",
        "        self._normalize_features()\n",
        "        # for \"last 10 game\" approach:\n",
        "        self.look_back = look_back\n",
        "\n",
        "    def _normalize_features(self):\n",
        "        features = self.dataframe.drop(columns=['player', 'team', 'gamedate', 'game_id', 'fp', 'int_player', 'int_gamedate', 'date', 'matchup'])\n",
        "        self.scaler.fit(features)\n",
        "        self.dataframe.loc[:, features.columns] = self.scaler.transform(features)\n",
        "\n",
        "    def _get_player_game_indices(self):\n",
        "        \"\"\"Get a list of tuples indicating the start and end indices of each player's games.\"\"\"\n",
        "        player_game_indices = []\n",
        "        players = self.dataframe['player'].unique()\n",
        "        count = 0\n",
        "        for player in players:\n",
        "            count += 1\n",
        "            player_data = self.dataframe[self.dataframe['player'] == player]\n",
        "            #player_data = player_data.sort_values(by='days_since').reset_index(drop=True) # im not sure\n",
        "            for i in range(10, len(player_data)):\n",
        "                start_index = player_data.index[i-10]\n",
        "                end_index = player_data.index[i]\n",
        "                player_game_indices.append((start_index, end_index))\n",
        "        return player_game_indices\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.player_game_indices)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        start_index, end_index = self.player_game_indices[idx]\n",
        "        player_data = self.dataframe.iloc[start_index:end_index]\n",
        "        features = player_data.drop(columns=['player', 'team', 'gamedate', 'game_id', 'fp', 'matchup', 'date']).values\n",
        "        # features = features.replace('-', 0).astype(float)\n",
        "        target = self.dataframe.iloc[end_index]['fp']\n",
        "        return torch.tensor(features, dtype=torch.float32), torch.tensor(target, dtype=torch.float32)\n",
        "\n",
        "\n",
        "class NBAFantasyDatasetFlat(Dataset):\n",
        "    def __init__(self, dataframe, look_back=10):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            dataframe (pandas.DataFrame): The dataframe containing the data.\n",
        "        \"\"\"\n",
        "        self.dataframe = dataframe\n",
        "        # player + game_date logic:\n",
        "        self.player_game_indices = self._get_player_game_indices()\n",
        "        # for normalization:\n",
        "        self.scaler = StandardScaler()\n",
        "        self._normalize_features()\n",
        "        # for \"last 10 game\" approach:\n",
        "        self.look_back = look_back\n",
        "\n",
        "    def _normalize_features(self):\n",
        "        features = self.dataframe.drop(columns=['player', 'team', 'gamedate', 'game_id', 'fp', 'int_player', 'int_gamedate', 'date', 'matchup'])\n",
        "        self.scaler.fit(features)\n",
        "        self.dataframe.loc[:, features.columns] = self.scaler.transform(features)\n",
        "\n",
        "    def _get_player_game_indices(self):\n",
        "        \"\"\"Get a list of tuples indicating the start and end indices of each player's games.\"\"\"\n",
        "        player_game_indices = []\n",
        "        players = self.dataframe['player'].unique()\n",
        "        count = 0\n",
        "        for player in players:\n",
        "            count += 1\n",
        "            player_data = self.dataframe[self.dataframe['player'] == player]\n",
        "            #player_data = player_data.sort_values(by='days_since').reset_index(drop=True) # im not sure\n",
        "            for i in range(10, len(player_data)):\n",
        "                start_index = player_data.index[i-10]\n",
        "                end_index = player_data.index[i]\n",
        "                player_game_indices.append((start_index, end_index))\n",
        "        return player_game_indices\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.player_game_indices)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        start_index, end_index = self.player_game_indices[idx]\n",
        "        player_data = self.dataframe.iloc[start_index:end_index]\n",
        "        # Flatten the last 10 games' features into one long vector\n",
        "        flattened_features = player_data.drop(columns=['player', 'team', 'gamedate', 'game_id', 'fp', 'matchup', 'date']).values.flatten()\n",
        "        target = self.dataframe.iloc[end_index]['fp']\n",
        "        return torch.tensor(flattened_features, dtype=torch.float32), torch.tensor(target, dtype=torch.float32)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sdf = df.sort_values(by=['player', 'days_since'], ascending=True, inplace=False).reset_index(drop=True)\n",
        "# print(sdf.head())"
      ],
      "metadata": {
        "id": "zewZPbMIaZoY"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "k0sLIpFEjme6"
      },
      "outputs": [],
      "source": [
        "def collate_fn(batch):\n",
        "    \"\"\"\n",
        "    Custom collate function for handling variable-length sequences.\n",
        "    \"\"\"\n",
        "    features, targets = zip(*batch)\n",
        "    # Pad the sequences to the maximum length in the batch\n",
        "    features_padded = nn.utils.rnn.pad_sequence(features, batch_first=True)\n",
        "    targets = torch.stack(targets)\n",
        "    return features_padded, targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "8eVheY7GT35b"
      },
      "outputs": [],
      "source": [
        "batch_size = 32 # we can change this if needed\n",
        "\n",
        "# Create a DataLoader\n",
        "dataset = NBAFantasyDataset(df.sort_values(by=['player', 'days_since'], ascending=True, inplace=False).reset_index(drop=True))\n",
        "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn)\n",
        "\n",
        "# Create DataLoader (Flattened Featured for SVR)\n",
        "dataset_flat = NBAFantasyDatasetFlat(df.sort_values(by=['player', 'days_since'], ascending=True, inplace=False).reset_index(drop=True))\n",
        "dataloader_flat = DataLoader(dataset_flat, batch_size=32, shuffle=True)  # Batch size of 1 since we're treating each sample independently"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1.5 Train-Test Split"
      ],
      "metadata": {
        "id": "Y0hInePdnrO9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the split sizes\n",
        "TRAIN_RATIO = 0.8\n",
        "train_size = int(TRAIN_RATIO * len(dataset))\n",
        "test_size = len(dataset) - train_size\n",
        "test_indices = random.sample(range(20000), test_size)\n",
        "\n",
        "train_dataset_flat, test_dataset_flat = random_split(dataset_flat, [train_size, test_size])\n",
        "train_dataloader_flat = DataLoader(train_dataset_flat, batch_size=32, shuffle=True)\n",
        "test_dataloader_flat = DataLoader(test_dataset_flat, batch_size=32, shuffle=True)\n",
        "\n",
        "\n",
        "# Split the dataset into training and test sets\n",
        "train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
        "\n",
        "# Create DataLoaders for the training and test sets\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn, num_workers=2)\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn, num_workers=2)"
      ],
      "metadata": {
        "id": "_eIvEZh9nDAG"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Comparing Results and Conclusion"
      ],
      "metadata": {
        "id": "4X_0pe-6P7bw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1: Getting Predictions From a Reputable Website"
      ],
      "metadata": {
        "id": "80pj_VF7QLCT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Base URL for the data\n",
        "base_url = 'https://raw.githubusercontent.com/BerkeAltiparmak/SportSense.AI/main/data/'\n",
        "\n",
        "# File names for each month\n",
        "file_names = ['fpdata_March.csv', 'fpdata_Feb.csv', 'fpdata_Jan.csv', 'fpdata_Dec.csv']\n",
        "\n",
        "# Initialize the list to store dataframes\n",
        "pw_dfs = []\n",
        "\n",
        "# Loop through the file names, download the data, and append to the list\n",
        "for file_name in file_names:\n",
        "    file_url = base_url + file_name\n",
        "    pw_df = pd.read_csv(file_url)\n",
        "    pw_dfs.append(pw_df)\n",
        "\n",
        "# Concatenate all dataframes into one\n",
        "dirty_predictor_website_df = pd.concat(pw_dfs, ignore_index=True)\n",
        "dirty_predictor_website_df.head() # it's not clean bc of NaNs, whose values appear at the bottom bc of bad data structure"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "NzjBA1RlP4eT",
        "outputId": "df5e6375-7670-4792-8f26-f68bfb4680d3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  web-scraper-order                              web-scraper-start-url  Rank  \\\n",
              "0      1712980617-1  https://fantasydata.com/nba/fantasy-basketball...   1.0   \n",
              "1      1712980617-2  https://fantasydata.com/nba/fantasy-basketball...   2.0   \n",
              "2      1712980617-3  https://fantasydata.com/nba/fantasy-basketball...   3.0   \n",
              "3      1712980617-4  https://fantasydata.com/nba/fantasy-basketball...   4.0   \n",
              "4      1712980617-5  https://fantasydata.com/nba/fantasy-basketball...   5.0   \n",
              "\n",
              "                                                Name Team  Pos  OPP  PTS  REB  \\\n",
              "0  Domantas Sabonis\\n    \\n    \\n    \\n        \\n...  NaN  NaN  NaN  NaN  NaN   \n",
              "1  Tyrese Haliburton\\n    \\n    \\n    \\n        \\...  NaN  NaN  NaN  NaN  NaN   \n",
              "2  Anthony Edwards\\n    \\n    \\n    \\n        \\n ...  NaN  NaN  NaN  NaN  NaN   \n",
              "3  Jalen Brunson\\n    \\n    \\n    \\n        \\n   ...  NaN  NaN  NaN  NaN  NaN   \n",
              "4  De'Aaron Fox\\n    \\n    \\n    \\n        \\n    ...  NaN  NaN  NaN  NaN  NaN   \n",
              "\n",
              "   AST  ...  FG_percent  FT_percent  3P_percent  FTM  2PM  3PM  Turnover  MIN  \\\n",
              "0  NaN  ...         NaN         NaN         NaN  NaN  NaN  NaN       NaN  NaN   \n",
              "1  NaN  ...         NaN         NaN         NaN  NaN  NaN  NaN       NaN  NaN   \n",
              "2  NaN  ...         NaN         NaN         NaN  NaN  NaN  NaN       NaN  NaN   \n",
              "3  NaN  ...         NaN         NaN         NaN  NaN  NaN  NaN       NaN  NaN   \n",
              "4  NaN  ...         NaN         NaN         NaN  NaN  NaN  NaN       NaN  NaN   \n",
              "\n",
              "   FPts  page  \n",
              "0   NaN   NaN  \n",
              "1   NaN   NaN  \n",
              "2   NaN   NaN  \n",
              "3   NaN   NaN  \n",
              "4   NaN   NaN  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2206d337-69ca-46d3-9728-984986cd68ba\" class=\"colab-df-container\">\n",
              "    <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>web-scraper-order</th>\n",
              "      <th>web-scraper-start-url</th>\n",
              "      <th>Rank</th>\n",
              "      <th>Name</th>\n",
              "      <th>Team</th>\n",
              "      <th>Pos</th>\n",
              "      <th>OPP</th>\n",
              "      <th>PTS</th>\n",
              "      <th>REB</th>\n",
              "      <th>AST</th>\n",
              "      <th>...</th>\n",
              "      <th>FG_percent</th>\n",
              "      <th>FT_percent</th>\n",
              "      <th>3P_percent</th>\n",
              "      <th>FTM</th>\n",
              "      <th>2PM</th>\n",
              "      <th>3PM</th>\n",
              "      <th>Turnover</th>\n",
              "      <th>MIN</th>\n",
              "      <th>FPts</th>\n",
              "      <th>page</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1712980617-1</td>\n",
              "      <td>https://fantasydata.com/nba/fantasy-basketball...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Domantas Sabonis\\n    \\n    \\n    \\n        \\n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1712980617-2</td>\n",
              "      <td>https://fantasydata.com/nba/fantasy-basketball...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Tyrese Haliburton\\n    \\n    \\n    \\n        \\...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1712980617-3</td>\n",
              "      <td>https://fantasydata.com/nba/fantasy-basketball...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Anthony Edwards\\n    \\n    \\n    \\n        \\n ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1712980617-4</td>\n",
              "      <td>https://fantasydata.com/nba/fantasy-basketball...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Jalen Brunson\\n    \\n    \\n    \\n        \\n   ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1712980617-5</td>\n",
              "      <td>https://fantasydata.com/nba/fantasy-basketball...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>De'Aaron Fox\\n    \\n    \\n    \\n        \\n    ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        document.querySelector('#df-2206d337-69ca-46d3-9728-984986cd68ba button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2206d337-69ca-46d3-9728-984986cd68ba');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f5c7de87-bf34-40be-a177-6ffa4ac1e197 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dirty_predictor_website_df"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_data(data):\n",
        "    \"\"\"\n",
        "    The logic behind this algorithm is very complex.\n",
        "    I sold my soul to the devil to get the alignment problem right.\n",
        "    \"\"\"\n",
        "    # Identifying rows with player names and stats\n",
        "    player_indices = data.index[data['Name'].notna()].tolist()\n",
        "    stats_indices = data.index[data['PTS'].notna()].tolist()\n",
        "\n",
        "    segments = []\n",
        "    current_segment = {'players': [], 'stats': []}\n",
        "\n",
        "    # Initialize flags to track when a new segment needs to be started\n",
        "    start_new_segment = True\n",
        "\n",
        "    # Collect indices into segments\n",
        "    for i in tqdm(range(len(data))):\n",
        "        if i in player_indices:\n",
        "            if start_new_segment:\n",
        "                # If a new segment is needed, start it\n",
        "                if current_segment['players'] or current_segment['stats']:\n",
        "                    segments.append(current_segment)\n",
        "                    current_segment = {'players': [], 'stats': []}\n",
        "                start_new_segment = False  # Reset the flag\n",
        "            current_segment['players'].append(i)\n",
        "        elif i in stats_indices:\n",
        "            current_segment['stats'].append(i)\n",
        "            start_new_segment = True  # New segment will start after this stats block\n",
        "\n",
        "    # Add the last segment\n",
        "    if current_segment['players'] or current_segment['stats']:\n",
        "        segments.append(current_segment)\n",
        "\n",
        "    # Process each segment separately\n",
        "    paired_data = []\n",
        "    for segment in tqdm(segments):\n",
        "        p_idx = 0\n",
        "        s_idx = 0\n",
        "        while p_idx < len(segment['players']) and s_idx < len(segment['stats']):\n",
        "            paired_data.append((segment['players'][p_idx], segment['stats'][s_idx]))\n",
        "            p_idx += 1\n",
        "            s_idx += 1\n",
        "\n",
        "\n",
        "    # Creating a structured DataFrame\n",
        "    paired_df = pd.DataFrame({\n",
        "        'web-scraper-order': [data.iloc[p[0]]['web-scraper-order'] for p in paired_data],\n",
        "        'web-scraper-start-url': [data.iloc[p[0]]['web-scraper-start-url'] for p in paired_data],\n",
        "        'Name': [data.iloc[p[0]]['Name'] for p in paired_data],\n",
        "        'Team': [data.iloc[p[1]]['Team'] for p in paired_data],\n",
        "        'Pos': [data.iloc[p[1]]['Pos'] for p in paired_data],\n",
        "        'OPP': [data.iloc[p[1]]['OPP'] for p in paired_data],\n",
        "        'PTS': [data.iloc[p[1]]['PTS'] for p in paired_data],\n",
        "        'REB': [data.iloc[p[1]]['REB'] for p in paired_data],\n",
        "        'AST': [data.iloc[p[1]]['AST'] for p in paired_data],\n",
        "        'BLK': [data.iloc[p[1]]['BLK'] for p in paired_data],\n",
        "        'STL': [data.iloc[p[1]]['STL'] for p in paired_data],\n",
        "        'FG_percent': [data.iloc[p[1]]['FG_percent'] for p in paired_data],\n",
        "        'FT_percent': [data.iloc[p[1]]['FT_percent'] for p in paired_data],\n",
        "        '3P_percent': [data.iloc[p[1]]['3P_percent'] for p in paired_data],\n",
        "        'FTM': [data.iloc[p[1]]['FTM'] for p in paired_data],\n",
        "        '2PM': [data.iloc[p[1]]['2PM'] for p in paired_data],\n",
        "        '3PM': [data.iloc[p[1]]['3PM'] for p in paired_data],\n",
        "        'Turnover': [data.iloc[p[1]]['Turnover'] for p in paired_data],\n",
        "        'MIN': [data.iloc[p[1]]['MIN'] for p in paired_data],\n",
        "        'FPts': [data.iloc[p[1]]['FPts'] for p in paired_data]\n",
        "    })\n",
        "    return paired_df"
      ],
      "metadata": {
        "id": "PA00rl-mQeiJ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_date(url):\n",
        "    from urllib.parse import urlparse, parse_qs\n",
        "    parsed_url = urlparse(url)\n",
        "    query_params = parse_qs(parsed_url.query)\n",
        "    return query_params.get('date', [None])[0]"
      ],
      "metadata": {
        "id": "pyVdHhSzQhOd"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_fantasy_points(row):\n",
        "    # Points scored\n",
        "    points_scored = row['pts'] * 0.5\n",
        "    # Rebounds, Assists, Steals, and Blocks\n",
        "    rebound_points = (row['oreb'] + row['dreb']) * 1\n",
        "    assist_points = row['ast'] * 1\n",
        "    steal_points = row['stl'] * 2\n",
        "    block_points = row['blk'] * 2\n",
        "    # Turnovers\n",
        "    turnover_points = row['tov'] * -1\n",
        "    # Three-points made\n",
        "    three_points = row['3pm'] * 0.5\n",
        "    # Double-Double and Triple-Double\n",
        "    double_digits = sum(i >= 10 for i in [row['pts'], row['oreb'] + row['dreb'], row['ast'], row['stl'], row['blk']])\n",
        "    double_double_points = 1.0 if double_digits >= 2 else 0\n",
        "    triple_double_points = 2.0 if double_digits >= 3 else 0\n",
        "    # 40+ and 50+ bonuses\n",
        "    fourty_pt_bonus = 2.0 * (row['pts'] >= 40)\n",
        "    fifty_pt_bonus = 2.0 * (row['pts'] >= 50)\n",
        "    # Total fantasy points\n",
        "    total_points = (points_scored + rebound_points + assist_points +\n",
        "                    steal_points + block_points + turnover_points + three_points +\n",
        "                    double_double_points + triple_double_points +\n",
        "                    fourty_pt_bonus + fifty_pt_bonus)\n",
        "    return total_points"
      ],
      "metadata": {
        "id": "elWzOnqyQjY2"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictor_website_df = clean_data(dirty_predictor_website_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LE6nxG9-Qj1F",
        "outputId": "659e213b-ba97-4f94-fe46-ab0f99807b4b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 44752/44752 [00:11<00:00, 3745.30it/s]\n",
            "100%|██████████| 156/156 [00:00<00:00, 10740.33it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean 'Name' column\n",
        "predictor_website_df['Name'] = predictor_website_df['Name'].str.extract(r'([^\\n]+)')\n",
        "\n",
        "# Extract 'Date' from URL\n",
        "predictor_website_df['Date'] = predictor_website_df['web-scraper-start-url'].apply(extract_date)\n",
        "predictor_website_df['Date'] = pd.to_datetime(predictor_website_df['Date'], format='%m-%d-%Y')\n",
        "\n",
        "# Split REB into OREB and DREB for our convention\n",
        "predictor_website_df['OREB'] = 0\n",
        "predictor_website_df['DREB'] = predictor_website_df['REB']"
      ],
      "metadata": {
        "id": "4UQ3jbcLQnmx"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mapping of current column names to the column names we have\n",
        "column_mapping = {\n",
        "    'Name': 'player',\n",
        "    'Team': 'team',\n",
        "    'Date': 'gamedate',\n",
        "    'PTS': 'pts',\n",
        "    'OREB': 'oreb',\n",
        "    'DREB': 'dreb',\n",
        "    'AST': 'ast',\n",
        "    'BLK': 'blk',\n",
        "    'STL': 'stl',\n",
        "    '3PM': '3pm',\n",
        "    'Turnover': 'tov',\n",
        "    'MIN': 'minuters'\n",
        "}\n",
        "predictor_website_df = predictor_website_df[list(column_mapping.keys())]\n",
        "predictor_website_df.rename(columns=column_mapping, inplace=True)\n",
        "\n",
        "# Calculate fantasy points, the way we defined them\n",
        "predictor_website_df['fp'] = predictor_website_df.apply(calculate_fantasy_points, axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fi5tdlk1Qq3V",
        "outputId": "d34a4515-3197-465a-f3ba-f67b1a3469f3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-65d3b88f3f91>:17: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  predictor_website_df.rename(columns=column_mapping, inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictor_website_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a7OoameDQys0",
        "outputId": "5fd12602-0321-448d-892f-3fc0b0463a08"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              player team   gamedate    pts  oreb   dreb    ast   blk   stl  \\\n",
              "0   Domantas Sabonis  SAC 2024-03-18  19.99     0  13.65   8.16  0.60  0.94   \n",
              "1  Tyrese Haliburton  IND 2024-03-18  23.02     0   4.38  12.40  0.74  1.28   \n",
              "2    Anthony Edwards  MIN 2024-03-18  27.82     0   5.89   5.25  0.82  1.30   \n",
              "3      Jalen Brunson   NY 2024-03-18  27.68     0   3.90   6.46  0.20  1.11   \n",
              "4       De'Aaron Fox  SAC 2024-03-18  25.51     0   4.34   5.64  0.41  1.62   \n",
              "\n",
              "    3pm   tov  minuters      fp  \n",
              "0  0.44  2.94      35.0  33.165  \n",
              "1  3.21  2.64      35.0  32.295  \n",
              "2  2.68  2.73      36.0  27.900  \n",
              "3  2.71  2.53      35.0  25.645  \n",
              "4  2.56  2.55      33.0  25.525  "
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              "      <td>Domantas Sabonis</td>\n",
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              "      <th>3</th>\n",
              "      <td>Jalen Brunson</td>\n",
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              "      <td>2024-03-18</td>\n",
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              "    <tr>\n",
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              "      <td>De'Aaron Fox</td>\n",
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              "      <td>2024-03-18</td>\n",
              "      <td>25.51</td>\n",
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              "      <td>0.41</td>\n",
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              "      <td>2.56</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "predictor_website_df",
              "summary": "{\n  \"name\": \"predictor_website_df\",\n  \"rows\": 20983,\n  \"fields\": [\n    {\n      \"column\": \"player\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 584,\n        \"samples\": [\n          \"Deni Avdija\",\n          \"Reggie Bullock\",\n          \"Georges Niang\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"team\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 32,\n        \"samples\": [\n          \"HOU\",\n          \"DET\",\n          \"BKN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gamedate\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-12-01 00:00:00\",\n        \"max\": \"2024-03-18 00:00:00\",\n        \"num_unique_values\": 101,\n        \"samples\": [\n          \"2023-12-19 00:00:00\",\n          \"2024-01-18 00:00:00\",\n          \"2024-01-07 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.848978056403017,\n        \"min\": 0.0,\n        \"max\": 35.98,\n        \"num_unique_values\": 2884,\n        \"samples\": [\n          15.88,\n          18.2,\n          8.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oreb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dreb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9007592323825757,\n        \"min\": 0.0,\n        \"max\": 17.71,\n        \"num_unique_values\": 1285,\n        \"samples\": [\n          11.12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ast\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.02685423433941,\n        \"min\": 0.0,\n        \"max\": 12.89,\n        \"num_unique_values\": 943,\n        \"samples\": [\n          1.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blk\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4603178672940464,\n        \"min\": 0.0,\n        \"max\": 3.53,\n        \"num_unique_values\": 301,\n        \"samples\": [\n          1.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stl\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.42995280166265915,\n        \"min\": 0.0,\n        \"max\": 2.15,\n        \"num_unique_values\": 191,\n        \"samples\": [\n          1.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"3pm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9686180821499751,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 440,\n        \"samples\": [\n          0.37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tov\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8195039764556046,\n        \"min\": 0.0,\n        \"max\": 4.04,\n        \"num_unique_values\": 374,\n        \"samples\": [\n          3.72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"minuters\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.902146787434528,\n        \"min\": 0.0,\n        \"max\": 45.0,\n        \"num_unique_values\": 44,\n        \"samples\": [\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.018587069648243,\n        \"min\": 0.0,\n        \"max\": 42.78000000000001,\n        \"num_unique_values\": 9157,\n        \"samples\": [\n          7.844999999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(predictor_website_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMa_SK2Ufpnv",
        "outputId": "28cea91c-e321-4490-adb4-41e8d1675788"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20983"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5.2: Evaluating the Predictions From a Reputable Website"
      ],
      "metadata": {
        "id": "w8DD5iL-RxuP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "mEeL32-zZuX2",
        "outputId": "549ab7fd-129c-40bc-d220-97a1a04ffcdb"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             player team   gamedate  days_since  winloss  minuters  pts  fgm  \\\n",
              "0  Payton Pritchard  BOS 2024-03-17         145        1        35   14    6   \n",
              "1    Svi Mykhailiuk  BOS 2024-03-17         145        1        23    9    3   \n",
              "2       Luke Kornet  BOS 2024-03-17         145        1        20   14    5   \n",
              "3        Sam Hauser  BOS 2024-03-17         145        1        23   30   10   \n",
              "4     Neemias Queta  BOS 2024-03-17         145        1         8    0    0   \n",
              "\n",
              "   fga  fgpercent  ...  opp_team_ORL  opp_team_PHI  opp_team_PHX  \\\n",
              "0   15       40.0  ...         False         False         False   \n",
              "1    7       42.9  ...         False         False         False   \n",
              "2    9       55.6  ...         False         False         False   \n",
              "3   13       76.9  ...         False         False         False   \n",
              "4    0        0.0  ...         False         False         False   \n",
              "\n",
              "   opp_team_POR  opp_team_SAC  opp_team_SAS  opp_team_TOR  opp_team_UTA  \\\n",
              "0         False         False         False         False         False   \n",
              "1         False         False         False         False         False   \n",
              "2         False         False         False         False         False   \n",
              "3         False         False         False         False         False   \n",
              "4         False         False         False         False         False   \n",
              "\n",
              "   opp_team_WAS    fp  \n",
              "0          True  28.0  \n",
              "1          True  13.0  \n",
              "2          True  17.0  \n",
              "3          True  29.0  \n",
              "4          True   1.0  \n",
              "\n",
              "[5 rows x 101 columns]"
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player</th>\n",
              "      <th>team</th>\n",
              "      <th>gamedate</th>\n",
              "      <th>days_since</th>\n",
              "      <th>winloss</th>\n",
              "      <th>minuters</th>\n",
              "      <th>pts</th>\n",
              "      <th>fgm</th>\n",
              "      <th>fga</th>\n",
              "      <th>fgpercent</th>\n",
              "      <th>...</th>\n",
              "      <th>opp_team_ORL</th>\n",
              "      <th>opp_team_PHI</th>\n",
              "      <th>opp_team_PHX</th>\n",
              "      <th>opp_team_POR</th>\n",
              "      <th>opp_team_SAC</th>\n",
              "      <th>opp_team_SAS</th>\n",
              "      <th>opp_team_TOR</th>\n",
              "      <th>opp_team_UTA</th>\n",
              "      <th>opp_team_WAS</th>\n",
              "      <th>fp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Payton Pritchard</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>40.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Svi Mykhailiuk</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>42.9</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Luke Kornet</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>55.6</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sam Hauser</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>76.9</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Neemias Queta</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 101 columns</p>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib\n",
        "\n",
        "def evaluate_prediction_website(df, predictor_website_df, test_indices, threshold):\n",
        "    actual_fps = []\n",
        "    predicted_fps = []\n",
        "    hits = 0\n",
        "\n",
        "    for idx in test_indices:\n",
        "        # Get the name and date from predictor_website_df\n",
        "        predicted_name = predictor_website_df.iloc[idx]['player']\n",
        "        predicted_date = predictor_website_df.iloc[idx]['gamedate']\n",
        "\n",
        "        # Get the predicted Fantasy Points\n",
        "        predicted_fp = predictor_website_df.iloc[idx]['fp']\n",
        "\n",
        "        # Find the corresponding actual entry in df\n",
        "        actual_entry = df[(df['player'] == predicted_name) & (df['gamedate'] == predicted_date)]\n",
        "\n",
        "        if not actual_entry.empty:\n",
        "            actual_fp = actual_entry.iloc[0]['fp']  # Taking the first matching entry\n",
        "\n",
        "            # Used following two lines to confirm we are looking at the same player and game\n",
        "            #if actual_entry.iloc[0]['team'] != predictor_website_df.iloc[idx]['team'] and actual_fp >= 5:\n",
        "                #print(\"Mismatch of\", actual_entry.iloc[0]['team'], \" vs \", predictor_website_df.iloc[idx]['team'])\n",
        "\n",
        "            # Collect data for MSELoss and plotting\n",
        "            actual_fps.append(actual_fp)\n",
        "            predicted_fps.append(predicted_fp)\n",
        "\n",
        "            # Check if prediction is within the threshold of actual\n",
        "            if abs(predicted_fp - actual_fp) <= threshold:\n",
        "                hits += 1\n",
        "\n",
        "    # Calculate accuracy only if we have at least one match\n",
        "    accuracy = hits / len(actual_fps) if actual_fps else 0\n",
        "\n",
        "    # Calculate MSELoss only if we have matches\n",
        "    mse_loss = mean_squared_error(actual_fps, predicted_fps) if actual_fps else None\n",
        "\n",
        "    # Plotting actual vs predicted\n",
        "    if actual_fps:\n",
        "        # Enable LaTeX rendering in plots\n",
        "        matplotlib.rcParams['text.usetex'] = True\n",
        "        matplotlib.rcParams['text.latex.preamble'] = r'\\usepackage{amsmath}'\n",
        "\n",
        "        # Set classic style for plots\n",
        "        plt.style.use('classic')\n",
        "\n",
        "        # Assuming 'actual_fps' and 'predicted_fps' are defined arrays or lists of actual and predicted fantasy points\n",
        "        plt.figure(figsize=(10, 6))\n",
        "        plt.scatter(actual_fps, predicted_fps, alpha=0.5, edgecolor='k', facecolor='skyblue')  # Adding edgecolor for better visibility\n",
        "        plt.plot([min(actual_fps), max(actual_fps)], [min(actual_fps), max(actual_fps)], color='red', linestyle='--', linewidth=2)  # Diagonal line for perfect predictions\n",
        "        plt.xlabel('Actual Fantasy Points')\n",
        "        plt.ylabel('Predicted Fantasy Points')\n",
        "        plt.title('Actual vs. Predicted Fantasy Points From a Reputable Website')\n",
        "        plt.grid(True, which='major', linestyle='--', linewidth='0.5', color='gray')\n",
        "        plt.minorticks_on()\n",
        "        plt.grid(True, which='minor', linestyle=':', linewidth='0.5', color='gray')\n",
        "        plt.show()\n",
        "\n",
        "    else:\n",
        "        print(\"No matching data found for predictions.\")\n",
        "\n",
        "    return accuracy, mse_loss"
      ],
      "metadata": {
        "id": "Yr0Sf_MpR9SC"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy, mse_loss = evaluate_prediction_website(df, predictor_website_df, test_indices=test_indices, threshold=5)\n",
        "print(f\"Reputable Prediction Website Accuracy: {accuracy}, MSE Loss: {mse_loss}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "OunyvQqMbdeP",
        "outputId": "c5e0ca90-ef63-4a46-fe0f-15d4816c27d0"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reputable Prediction Website Accuracy: 0.5608373310074138, MSE Loss: 48.46868736371566\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "EnrichedLSTM proves to be almost as good as the predictions from the reputable website"
      ],
      "metadata": {
        "id": "re4gnqNbeACf"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "b7GAbzMh_Cfg",
        "4jD-JDd2_IAL",
        "7bJqVyuxHo21",
        "Ob3lrIAtdtXO",
        "v3m-nQTec3aR",
        "x3kuJsMZiTha",
        "q9AzA7_sdyp3",
        "A36yG3lckDLd"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}