Colts Subreddit  - Part 2 .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictive Analysis on Colts Subreddit--- Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intro\n",
    "Part 1 involved pulling data from the Colts subreddit and merging it with the defensive stats data per week. Part 2 takes this data and uses Multiple Linear Regression to predict the subreddit counts of each player based on that players stats for a game. \n",
    "\n",
    "**Notes**\n",
    "1. If a player did not play in a game, but was mentioned 1+ times in the Colts subreddit, I changed that players reddit count to zero for that particular game.\n",
    "2. Justin Houston's data was removed from the model - last name overinflated counts\n",
    "3. Model was build using data from **Week 1-13** and **Week 14** was used for predictions\n",
    "\n",
    "\n",
    "\n",
    "Let's get to it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries that will be needed\n",
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy import stats\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "import math\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['week10_upd.csv', 'week11_upd.csv', 'week12_upd.csv', 'week13_upd.csv', 'week1_upd.csv', 'week2_upd.csv', 'week3_upd.csv', 'week4_upd.csv', 'week5_upd.csv', 'week7_upd.csv', 'week8_upd.csv', 'week9_upd.csv']\n"
     ]
    }
   ],
   "source": [
    "os.chdir(r'C:\\Users\\ddudley\\Documents\\UpdatedRed')\n",
    "filelist2 = glob.glob('*.csv')\n",
    "print(filelist2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge Weeks 1-13 results into one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df2 = pd.DataFrame()\n",
    "for file in filelist2:\n",
    "    df2 = pd.read_csv(file)\n",
    "    merged_df2 = merged_df2.append(df2,ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Clean Dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df2.drop('Unnamed: 0', axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df2.set_index(['Player'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset contains 15 columns, and 336 rows\n"
     ]
    }
   ],
   "source": [
    "print(\"This dataset contains {} columns, and {} rows\".format(merged_df2.shape[1],merged_df2.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Week              0\n",
       "T_Snaps           0\n",
       "T_pressure        0\n",
       "Sacks             0\n",
       "Hurries           0\n",
       "T_Tackles         0\n",
       "Missed_Tackles    0\n",
       "Stop              0\n",
       "Forced_Fumble     0\n",
       "Int               0\n",
       "Pass_Breakup      0\n",
       "Penalty           0\n",
       "y_per_rec         0\n",
       "rec               0\n",
       "red_count         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if the there are any null values, and if there are then assign them a value of 0\n",
    "merged_df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df2 = merged_df2.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new df which drop rows where player did not play a snap\n",
    "merged_df_played = merged_df2[merged_df2.T_Snaps != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop justin houston from the project because his last name causes problems\n",
    "merged_df_played = merged_df_played[merged_df_played.index != 'Justin Houston']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Week</th>\n",
       "      <th>T_Snaps</th>\n",
       "      <th>T_pressure</th>\n",
       "      <th>Sacks</th>\n",
       "      <th>Hurries</th>\n",
       "      <th>T_Tackles</th>\n",
       "      <th>Missed_Tackles</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Forced_Fumble</th>\n",
       "      <th>Int</th>\n",
       "      <th>Pass_Breakup</th>\n",
       "      <th>Penalty</th>\n",
       "      <th>y_per_rec</th>\n",
       "      <th>rec</th>\n",
       "      <th>red_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Anthony Walker</th>\n",
       "      <td>10</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bobby Okereke</th>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darius Leonard</th>\n",
       "      <td>10</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denico Autry</th>\n",
       "      <td>10</td>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ben Banogu</th>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al-Quadin Muhammad</th>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jabaal Sheard</th>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trevon Coley</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Margus Hunt</th>\n",
       "      <td>10</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grover Stewart</th>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kenny Moore</th>\n",
       "      <td>10</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shakial Taylor</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marvell Tell</th>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rock Ya-Sin</th>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clayton Geathers</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Week  T_Snaps  T_pressure  Sacks  Hurries  T_Tackles  \\\n",
       "Player                                                                     \n",
       "Anthony Walker        10       48           0      0        0          0   \n",
       "Bobby Okereke         10       19           0      0        0          3   \n",
       "Darius Leonard        10       65           2      1        1          9   \n",
       "Denico Autry          10       48           4      0        3          0   \n",
       "Ben Banogu            10       16           0      0        0          0   \n",
       "Al-Quadin Muhammad    10       22           3      0        2          1   \n",
       "Jabaal Sheard         10       46           3      1        2          1   \n",
       "Trevon Coley          10        2           0      0        0          0   \n",
       "Margus Hunt           10       37           2      0        1          1   \n",
       "Grover Stewart        10       40           2      0        2          3   \n",
       "Kenny Moore           10       65           1      0        1          5   \n",
       "Shakial Taylor        10        1           0      0        0          0   \n",
       "Marvell Tell          10       46           0      0        0          2   \n",
       "Rock Ya-Sin           10       64           0      0        0          4   \n",
       "Clayton Geathers      10        6           0      0        0          1   \n",
       "\n",
       "                    Missed_Tackles  Stop  Forced_Fumble  Int  Pass_Breakup  \\\n",
       "Player                                                                       \n",
       "Anthony Walker                   1     0            0.0    0             0   \n",
       "Bobby Okereke                    0     1            0.0    0             0   \n",
       "Darius Leonard                   0     5            1.0    1             0   \n",
       "Denico Autry                     0     0            0.0    0             0   \n",
       "Ben Banogu                       0     0            0.0    0             0   \n",
       "Al-Quadin Muhammad               2     1            0.0    0             0   \n",
       "Jabaal Sheard                    0     2            0.0    0             0   \n",
       "Trevon Coley                     0     0            0.0    0             0   \n",
       "Margus Hunt                      1     1            0.0    0             0   \n",
       "Grover Stewart                   0     3            0.0    0             0   \n",
       "Kenny Moore                      1     4            0.0    0             0   \n",
       "Shakial Taylor                   0     0            0.0    0             0   \n",
       "Marvell Tell                     0     0            0.0    0             1   \n",
       "Rock Ya-Sin                      0     1            0.0    0             1   \n",
       "Clayton Geathers                 0     1            0.0    0             0   \n",
       "\n",
       "                    Penalty  y_per_rec  rec  red_count  \n",
       "Player                                                  \n",
       "Anthony Walker            0       20.0    1          4  \n",
       "Bobby Okereke             0        0.0    0          0  \n",
       "Darius Leonard            0       12.8    4         54  \n",
       "Denico Autry              0        0.0    0          1  \n",
       "Ben Banogu                1        0.0    0          0  \n",
       "Al-Quadin Muhammad        0        0.0    0          0  \n",
       "Jabaal Sheard             0        9.0    1          8  \n",
       "Trevon Coley              0        0.0    0          0  \n",
       "Margus Hunt               0        0.0    0          1  \n",
       "Grover Stewart            0        0.0    0          2  \n",
       "Kenny Moore               0        6.5    4          9  \n",
       "Shakial Taylor            0        0.0    0          1  \n",
       "Marvell Tell              0       11.0    2          1  \n",
       "Rock Ya-Sin               0        6.7    3         18  \n",
       "Clayton Geathers          0        0.0    0          0  "
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#final dataframe used for modeling- first 15 rows:\n",
    "merged_df_played.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create one dataframe summing all data for each player - useful for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week1_upd.csv'\n",
    "f2 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week2_upd.csv'\n",
    "f3 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week3_upd.csv'\n",
    "f4 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week4_upd.csv'\n",
    "f5 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week5_upd.csv'\n",
    "#f6 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week6_upd.csv'\n",
    "f7 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week7_upd.csv'\n",
    "f8 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week8_upd.csv'\n",
    "f9 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week9_upd.csv'\n",
    "f10 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week10_upd.csv'\n",
    "f11 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week11_upd.csv'\n",
    "f12 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week12_upd.csv'\n",
    "f13 = r'C:\\Users\\ddudley\\Documents\\UpdatedRed\\week13_upd.csv'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(f1)\n",
    "df1 = df1.reset_index(drop=True)\n",
    "df1 = df1.drop('Unnamed: 0', axis=1)\n",
    "df2 = pd.read_csv(f2)\n",
    "df2 = df2.drop('Unnamed: 0', axis=1)\n",
    "df3 = pd.read_csv(f3)\n",
    "df3 = df3.drop('Unnamed: 0', axis=1)\n",
    "df4 = pd.read_csv(f4)\n",
    "df4 = df4.drop('Unnamed: 0', axis=1)\n",
    "df5 = pd.read_csv(f5)\n",
    "df5 = df5.drop('Unnamed: 0', axis=1)\n",
    "#df6 = pd.read_csv(f6)\n",
    "df7 = pd.read_csv(f7)\n",
    "df7 = df7.drop('Unnamed: 0', axis=1)\n",
    "df8 = pd.read_csv(f8)\n",
    "df8 = df8.drop('Unnamed: 0', axis=1)\n",
    "df9 = pd.read_csv(f9)\n",
    "df9 = df9.drop('Unnamed: 0', axis=1)\n",
    "df10 = pd.read_csv(f10)\n",
    "df10 = df10.drop('Unnamed: 0', axis=1)\n",
    "df11 = pd.read_csv(f11)\n",
    "df11 = df11.drop('Unnamed: 0', axis=1)\n",
    "df12 = pd.read_csv(f12)\n",
    "df12 = df12.drop('Unnamed: 0', axis=1)\n",
    "df13 = pd.read_csv(f13)\n",
    "df13 = df13.drop('Unnamed: 0', axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create dataframe that summs game stats from Week 1-12\n",
    "dfs = [df1, df2, df3,df4,df5,df7,df8,df9,df10,df11,df12,df13]\n",
    "summed_df = pd.concat(dfs).groupby('Player', as_index=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [],
   "source": [
    "summed_df.drop(['Week'], axis =1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop justin houston\n",
    "summed_df = summed_df[summed_df.Player != 'Justin Houston']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .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>T_Snaps</th>\n",
       "      <th>T_pressure</th>\n",
       "      <th>Sacks</th>\n",
       "      <th>Hurries</th>\n",
       "      <th>T_Tackles</th>\n",
       "      <th>Missed_Tackles</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Forced_Fumble</th>\n",
       "      <th>Int</th>\n",
       "      <th>Pass_Breakup</th>\n",
       "      <th>Penalty</th>\n",
       "      <th>y_per_rec</th>\n",
       "      <th>rec</th>\n",
       "      <th>red_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rock Ya-Sin</th>\n",
       "      <td>571</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>151.9</td>\n",
       "      <td>28</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darius Leonard</th>\n",
       "      <td>552</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>85.0</td>\n",
       "      <td>40</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kenny Moore</th>\n",
       "      <td>651</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>39</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malik Hooker</th>\n",
       "      <td>504</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quincy Wilson</th>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>74.2</td>\n",
       "      <td>10</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Khari Willis</th>\n",
       "      <td>459</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56.5</td>\n",
       "      <td>15</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bobby Okereke</th>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>21</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kemoko Turay</th>\n",
       "      <td>81</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anthony Walker</th>\n",
       "      <td>579</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>16</td>\n",
       "      <td>38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>109.8</td>\n",
       "      <td>37</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pierre Desir</th>\n",
       "      <td>409</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>95.4</td>\n",
       "      <td>29</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clayton Geathers</th>\n",
       "      <td>418</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44.8</td>\n",
       "      <td>8</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jabaal Sheard</th>\n",
       "      <td>391</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denico Autry</th>\n",
       "      <td>532</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ben Banogu</th>\n",
       "      <td>205</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EJ Speed</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George Odum</th>\n",
       "      <td>224</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>80.5</td>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grover Stewart</th>\n",
       "      <td>426</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Margus Hunt</th>\n",
       "      <td>311</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shakial Taylor</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28.5</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marvell Tell</th>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyquan Lewis</th>\n",
       "      <td>92</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rolan Milligan</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthew Adams</th>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al-Quadin Muhammad</th>\n",
       "      <td>357</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carl Davis</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zaire Franklin</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trevon Coley</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    T_Snaps  T_pressure  Sacks  Hurries  T_Tackles  \\\n",
       "Player                                                               \n",
       "Rock Ya-Sin             571           0      0        0         38   \n",
       "Darius Leonard          552          13      5        7         53   \n",
       "Kenny Moore             651           7      3        3         53   \n",
       "Malik Hooker            504           0      0        0         23   \n",
       "Quincy Wilson           105           0      0        0          8   \n",
       "Khari Willis            459           4      0        4         39   \n",
       "Bobby Okereke           346           1      1        0         28   \n",
       "Kemoko Turay             81          14      2        8          2   \n",
       "Anthony Walker          579           6      2        4         57   \n",
       "Pierre Desir            409           0      0        0         25   \n",
       "Clayton Geathers        418           0      0        0         29   \n",
       "Jabaal Sheard           391          27      4       20         15   \n",
       "Denico Autry            532          29      4       19         16   \n",
       "Ben Banogu              205          14      3       10          5   \n",
       "EJ Speed                 34           0      0        0          4   \n",
       "George Odum             224           2      1        0         18   \n",
       "Grover Stewart          426          10      2        7         15   \n",
       "Margus Hunt             311           5      0        4         11   \n",
       "Shakial Taylor           60           0      0        0          6   \n",
       "Marvell Tell            219           0      0        0         17   \n",
       "Tyquan Lewis             92           6      0        5          0   \n",
       "Rolan Milligan           71           0      0        0          4   \n",
       "Matthew Adams            94           0      0        0          8   \n",
       "Al-Quadin Muhammad      357          17      1       12         15   \n",
       "Carl Davis                7           0      0        0          0   \n",
       "Zaire Franklin            1           0      0        0          0   \n",
       "Trevon Coley             13           1      0        1          0   \n",
       "\n",
       "                    Missed_Tackles  Stop  Forced_Fumble  Int  Pass_Breakup  \\\n",
       "Player                                                                       \n",
       "Rock Ya-Sin                      3    14            0.0    1             1   \n",
       "Darius Leonard                   5    23            2.0    2             0   \n",
       "Kenny Moore                      7    31            0.0    2             1   \n",
       "Malik Hooker                     7     7            0.0    1             1   \n",
       "Quincy Wilson                    0     2            0.0    0             0   \n",
       "Khari Willis                     6    15            0.0    0             1   \n",
       "Bobby Okereke                    4    16            1.0    1             1   \n",
       "Kemoko Turay                     0     3            1.0    0             0   \n",
       "Anthony Walker                  16    38            1.0    0             1   \n",
       "Pierre Desir                     5     5            0.0    1             3   \n",
       "Clayton Geathers                 5     8            0.0    1             0   \n",
       "Jabaal Sheard                    6    18            0.0    0             1   \n",
       "Denico Autry                     2    17            1.0    0             0   \n",
       "Ben Banogu                       1     6            1.0    0             0   \n",
       "EJ Speed                         1     2            0.0    0             0   \n",
       "George Odum                      3     4            1.0    0             0   \n",
       "Grover Stewart                   1    13            0.0    0             0   \n",
       "Margus Hunt                      3     8            0.0    0             0   \n",
       "Shakial Taylor                   1     2            0.0    0             1   \n",
       "Marvell Tell                     0     5            1.0    0             5   \n",
       "Tyquan Lewis                     0     0            0.0    0             0   \n",
       "Rolan Milligan                   1     3            0.0    0             0   \n",
       "Matthew Adams                    4     1            0.0    0             0   \n",
       "Al-Quadin Muhammad               6    13            1.0    0             0   \n",
       "Carl Davis                       0     0            0.0    0             0   \n",
       "Zaire Franklin                   0     0            0.0    0             0   \n",
       "Trevon Coley                     1     0            0.0    0             0   \n",
       "\n",
       "                    Penalty  y_per_rec  rec  red_count  \n",
       "Player                                                  \n",
       "Rock Ya-Sin               7      151.9   28        474  \n",
       "Darius Leonard            2       85.0   40        459  \n",
       "Kenny Moore               0      110.0   39        371  \n",
       "Malik Hooker              0       30.0    4        216  \n",
       "Quincy Wilson             3       74.2   10        167  \n",
       "Khari Willis              1       56.5   15        122  \n",
       "Bobby Okereke             0       63.0   21        121  \n",
       "Kemoko Turay              1        0.0    0        108  \n",
       "Anthony Walker            0      109.8   37        102  \n",
       "Pierre Desir              4       95.4   29         92  \n",
       "Clayton Geathers          1       44.8    8         82  \n",
       "Jabaal Sheard             0       10.0    2         66  \n",
       "Denico Autry              0       18.0    1         61  \n",
       "Ben Banogu                1        0.0    0         50  \n",
       "EJ Speed                  0       -1.0    1         49  \n",
       "George Odum               1       80.5    7         45  \n",
       "Grover Stewart            1        0.0    0         29  \n",
       "Margus Hunt               1        9.0    1         19  \n",
       "Shakial Taylor            1       28.5    4         16  \n",
       "Marvell Tell              2       72.0   17         14  \n",
       "Tyquan Lewis              0        0.0    0          9  \n",
       "Rolan Milligan            0       57.0    4          8  \n",
       "Matthew Adams             0       29.3    5          7  \n",
       "Al-Quadin Muhammad        0        0.0    0          6  \n",
       "Carl Davis                0        0.0    0          6  \n",
       "Zaire Franklin            0        0.0    0          5  \n",
       "Trevon Coley              0        3.0    1          0  "
      ]
     },
     "execution_count": 547,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summed_df.sort_values(by = ['red_count'],ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1166.4x648 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Pair-wise Scatter Plots\n",
    "# Let's pick out a few features to look at \n",
    "cols = ['T_Snaps', 'T_Tackles', 'Stop', 'Int','red_count']\n",
    "\n",
    "pp = sns.pairplot(summed_df[cols], size=1.8, aspect=1.8,\n",
    "\n",
    "                  plot_kws=dict(edgecolor=\"k\", linewidth=0.5),\n",
    "\n",
    "                  diag_kind=\"kde\", diag_kws=dict(shade=True))\n",
    "fig = pp.fig \n",
    "fig.subplots_adjust(top=0.93, wspace=0.3)\n",
    "t = fig.suptitle('Defensive Stats Pairwise Plots', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The four features plotted against red_count generally show a positive correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Linear Regression Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.metrics import SCORERS\n",
    "#print(SCORERS.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze the correlation between each feature and the reddit counts to help decide what features to use in modeling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient total snaps is 0.33277676538546036  with a P-value of P = 4.360643845736733e-07\n",
      "The Pearson Correlation Coefficient for T_Tackles is 0.34468846204769543  with a P-value of P = 1.5623503501412543e-07\n",
      "The Pearson Correlation Coefficient for stops is 0.21994976178359674  with a P-value of P = 0.0010230583288739071\n",
      "The Pearson Correlation Coefficient for interceptions is 0.31894179142232193  with a P-value of P = 1.3621019108476299e-06\n",
      "The Pearson Correlation Coefficient for receptions is 0.37425657435076065  with a P-value of P = 1.0098454289281703e-08\n",
      "The Pearson Correlation Coefficient for Sacks is 0.07761751344707721  with a P-value of P = 0.2516190371091403\n",
      "The Pearson Correlation Coefficient for yards per reception is 0.31630437202657874  with a P-value of P = 1.6817126953146985e-06\n",
      "The Pearson Correlation Coefficient for penalties is 0.5412776187407526  with a P-value of P = 3.820411832008524e-18\n",
      "The Pearson Correlation Coefficient for Total Pressures is 0.016465592170814933  with a P-value of P = 0.8081220761666397\n",
      "The Pearson Correlation Coefficient for Hurries is -0.034015686118853986  with a P-value of P = 0.6158045402430845\n",
      "The Pearson Correlation Coefficient for Forced Fumbles is 0.05771634817517527  with a P-value of P = 0.39426660200305863\n",
      "The Pearson Correlation Coefficient for Missed Tackles is 0.01404576922552443  with a P-value of P = 0.8358881606094364\n",
      "The Pearson Correlation Coefficient for Pass Breakups is 0.051206705438926595  with a P-value of P = 0.4498366686583396\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['T_Snaps'], merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient total snaps is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['T_Tackles'], merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for T_Tackles is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['Stop'], merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for stops is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['Int'], merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for interceptions is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['rec'], merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for receptions is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['Sacks'], merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for Sacks is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['y_per_rec'],merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for yards per reception is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['Penalty'], merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for penalties is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['T_pressure'], merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for Total Pressures is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['Hurries'], merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for Hurries is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['Forced_Fumble'],merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for Forced Fumbles is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['Missed_Tackles'], merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for Missed Tackles is\", pearson_coef, \" with a P-value of P =\", p_value) \n",
    "pearson_coef, p_value = stats.pearsonr(merged_df_played['Pass_Breakup'], merged_df_played['red_count'])\n",
    "print(\"The Pearson Correlation Coefficient for Pass Breakups is\", pearson_coef, \" with a P-value of P =\", p_value) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------\n",
    "# Using Cross Validation To Help Select Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model A: Model using all features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(220, 13)\n",
      "(220, 1)\n"
     ]
    }
   ],
   "source": [
    "#create feature and predictor variables \n",
    "XDATA=merged_df_played.drop(['red_count','Week'], axis =1)\n",
    "print(XDATA.shape)\n",
    "YDATA =merged_df_played[['red_count']]\n",
    "print(YDATA.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T_Snaps</th>\n",
       "      <th>T_pressure</th>\n",
       "      <th>Sacks</th>\n",
       "      <th>Hurries</th>\n",
       "      <th>T_Tackles</th>\n",
       "      <th>Missed_Tackles</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Forced_Fumble</th>\n",
       "      <th>Int</th>\n",
       "      <th>Pass_Breakup</th>\n",
       "      <th>Penalty</th>\n",
       "      <th>y_per_rec</th>\n",
       "      <th>rec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Anthony Walker</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bobby Okereke</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                T_Snaps  T_pressure  Sacks  Hurries  T_Tackles  \\\n",
       "Player                                                           \n",
       "Anthony Walker       48           0      0        0          0   \n",
       "Bobby Okereke        19           0      0        0          3   \n",
       "\n",
       "                Missed_Tackles  Stop  Forced_Fumble  Int  Pass_Breakup  \\\n",
       "Player                                                                   \n",
       "Anthony Walker               1     0            0.0    0             0   \n",
       "Bobby Okereke                0     1            0.0    0             0   \n",
       "\n",
       "                Penalty  y_per_rec  rec  \n",
       "Player                                   \n",
       "Anthony Walker        0       20.0    1  \n",
       "Bobby Okereke         0        0.0    0  "
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XDATA.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.model_selection import cross_validate, cross_val_predict\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy import stats\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "import math\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def regression_model_all(feature_data,predictor_data):\n",
    "    \n",
    "    global regression_model_all\n",
    "    regression_model_all = LinearRegression(normalize = True)\n",
    "    global scores_all\n",
    "    scores_all = cross_val_score(regression_model_all,feature_data,predictor_data,cv=10, scoring = 'neg_mean_squared_error')\n",
    "    for_rmse = -scores_all.mean()\n",
    "    print(scores_all)\n",
    "    print(\"The average mse for this model is: {}\".format(scores_all.mean()))\n",
    "    print(\"Make positive: {}\".format(scores_all.mean()*-1))\n",
    "    print(\"RMSE: {}\".format(math.sqrt(for_rmse)))\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  -93.85625121  -786.74371338  -554.05508557  -267.41640559\n",
      "  -161.06961113  -174.32418113  -323.26305123  -592.48139541\n",
      " -1404.51866681  -605.65316481]\n",
      "The average mse for this model is: -496.3381526250334\n",
      "Make positive: 496.3381526250334\n",
      "RMSE: 22.27864790836808\n"
     ]
    }
   ],
   "source": [
    "#model with non-normalized data!\n",
    "regression_model_all(XDATA,YDATA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Should the data be normalized? Lets test setting normalizing = True\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  -93.85625121  -786.74371338  -554.05508557  -267.41640559\n",
      "  -161.06961113  -174.32418113  -323.26305123  -592.48139541\n",
      " -1404.51866681  -605.65316481]\n",
      "The average mse for this model is: -496.33815262503305\n",
      "Make positive: 496.33815262503305\n",
      "RMSE: 22.27864790836807\n"
     ]
    }
   ],
   "source": [
    "regression_model_all(XDATA,YDATA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Normalizing the data resulted in little to no difference** of the mse metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model B: Model using Top 7 most correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regression_model_top7(feature_data,predictor_data):\n",
    "    \n",
    "    global regression_model_top7\n",
    "    regression_model_top7 = LinearRegression(normalize = False)\n",
    "    global scores_top7\n",
    "    scores_top7 = cross_val_score(regression_model_top7,feature_data,predictor_data,cv=10, scoring = 'neg_mean_squared_error')\n",
    "    for_rmse = -scores_top7.mean()\n",
    "    print(scores_top7)\n",
    "    print(\"The average mse for this model is: {}\".format(scores_top7.mean()))\n",
    "    print(\"Make positive: {}\".format(scores_top7.mean()*-1))\n",
    "    print(\"RMSE: {}\".format(math.sqrt(for_rmse)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "XDATA_top7=merged_df_played.drop(['red_count','Pass_Breakup','Week','Missed_Tackles','Forced_Fumble','Hurries','T_pressure','Sacks',], axis =1)\n",
    "YDATA_top7 =merged_df_played[['red_count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T_Snaps</th>\n",
       "      <th>T_Tackles</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Int</th>\n",
       "      <th>Penalty</th>\n",
       "      <th>y_per_rec</th>\n",
       "      <th>rec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Anthony Walker</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bobby Okereke</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                T_Snaps  T_Tackles  Stop  Int  Penalty  y_per_rec  rec\n",
       "Player                                                                \n",
       "Anthony Walker       48          0     0    0        0       20.0    1\n",
       "Bobby Okereke        19          3     1    0        0        0.0    0"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XDATA_top7.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  -72.68964881  -678.84926448  -584.64370193  -236.0090249\n",
      "  -125.82234989  -134.31967159  -311.09676569  -673.65127785\n",
      " -1441.1896155   -650.39121847]\n",
      "The average mse for this model is: -490.86625391106566\n",
      "Make positive: 490.86625391106566\n",
      "RMSE: 22.155501662365168\n"
     ]
    }
   ],
   "source": [
    "regression_model_top7(XDATA_top7,YDATA_top7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model C: Model with top 5 most correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regression_model_top5(feature_data,predictor_data):\n",
    "    \n",
    "    #global regression_model_\n",
    "    regression_model_top5 = LinearRegression(normalize = False)\n",
    "    global scores_top5\n",
    "    scores_top5 = cross_val_score(regression_model_top5,feature_data,predictor_data,cv=10, scoring = 'neg_mean_squared_error')\n",
    "    for_rmse = -scores_top5.mean()\n",
    "    print(scores_top5)\n",
    "    print(\"The average mse for this model is: {}\".format(scores_top5.mean()))\n",
    "    print(\"Make positive: {}\".format(scores_top5.mean()*-1))\n",
    "    print(\"RMSE: {}\".format(math.sqrt(for_rmse)))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "XDATA_top5=merged_df_played.drop(['red_count','Pass_Breakup','y_per_rec','Week','Stop','Missed_Tackles','Forced_Fumble','Hurries','T_pressure','Sacks','Stop'], axis =1)\n",
    "YDATA_top5 =merged_df_played[['red_count']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T_Snaps</th>\n",
       "      <th>T_Tackles</th>\n",
       "      <th>Int</th>\n",
       "      <th>Penalty</th>\n",
       "      <th>rec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Anthony Walker</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bobby Okereke</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                T_Snaps  T_Tackles  Int  Penalty  rec\n",
       "Player                                               \n",
       "Anthony Walker       48          0    0        0    1\n",
       "Bobby Okereke        19          3    0        0    0"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XDATA_top5.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  -73.90431346  -670.28544723  -571.01314544  -196.65534747\n",
      "  -124.9286156   -133.61003901  -314.09306857  -666.08659838\n",
      " -1431.44670947  -591.88696114]\n",
      "The average mse for this model is: -477.39102457692053\n",
      "Make positive: 477.39102457692053\n",
      "RMSE: 21.849279726730593\n"
     ]
    }
   ],
   "source": [
    "regression_model_top5(XDATA_top5,YDATA_top5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model D: Model with top 3 most correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regression_model_top3(feature_data,predictor_data):\n",
    "    \n",
    "    #global regression_model_top3\n",
    "    regression_model_top3 = LinearRegression(normalize = False)\n",
    "    global scores_top3\n",
    "    scores_top3 = cross_val_score(regression_model_top3,feature_data,predictor_data,cv=10, scoring = 'neg_mean_squared_error')\n",
    "    for_rmse = -scores_top3.mean()\n",
    "    print(scores_top3)\n",
    "    print(\"The average mse for this model is: {}\".format(scores_top3.mean()))\n",
    "    print(\"Make positive: {}\".format(scores_top3.mean()*-1))\n",
    "    print(\"RMSE: {}\".format(math.sqrt(for_rmse)))\n",
    "    \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "XDATA_top3=merged_df_played.drop(['red_count','T_Snaps','Week','T_Tackles','Pass_Breakup','Stop','y_per_rec','T_Snaps','Missed_Tackles','Forced_Fumble','Hurries','T_pressure','Sacks','Stop'], axis =1)\n",
    "YDATA_top3 =merged_df_played[['red_count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_top3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Int</th>\n",
       "      <th>Penalty</th>\n",
       "      <th>rec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Anthony Walker</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bobby Okereke</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Int  Penalty  rec\n",
       "Player                           \n",
       "Anthony Walker    0        0    1\n",
       "Bobby Okereke     0        0    0"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XDATA_top3.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  -98.19086643  -683.47739118  -622.15024103  -203.16872237\n",
      "  -112.86492159  -144.26113781  -298.81948794  -683.02524505\n",
      " -1560.04087453  -502.87440342]\n",
      "The average mse for this model is: -490.8873291351082\n",
      "Make positive: 490.8873291351082\n",
      "RMSE: 22.15597727781621\n"
     ]
    }
   ],
   "source": [
    "regression_model_top3(XDATA_top3,YDATA_top3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which Model produced the lowest error?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE for model A w/ all features is: 22.27864790836807\n",
      "--------------------------------------------------------------------------------\n",
      "The RMSE for model B w/ top 7 most correlated features is: 22.155501662365168\n",
      "--------------------------------------------------------------------------------\n",
      "The RMSE for model C w/ 5 most correlated features is: 21.849279726730593\n",
      "--------------------------------------------------------------------------------\n",
      "The RMSE for model D w/ 3 most correlated features  is: 22.15597727781621\n"
     ]
    }
   ],
   "source": [
    "#Comparing error results from using different sets of features\n",
    "print(\"The RMSE for model A w/ all features is: {}\".format((math.sqrt(-scores_all.mean()))))\n",
    "print('--------------------------------------------------------------------------------')\n",
    "print(\"The RMSE for model B w/ top 7 most correlated features is: {}\".format(math.sqrt(-scores_top7.mean())))\n",
    "print('--------------------------------------------------------------------------------')\n",
    "print(\"The RMSE for model C w/ 5 most correlated features is: {}\".format(math.sqrt(-scores_top5.mean())))\n",
    "print('--------------------------------------------------------------------------------')\n",
    "print(\"The RMSE for model D w/ 3 most correlated features  is: {}\".format(math.sqrt(-scores_top3.mean())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the results above, Model C produced the lowest error. We will use this model in the prediction!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions using Model C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regression_model_predicting(feature_data,predictor_data,stat_array):\n",
    "    \n",
    "    #create instance of the model\n",
    "    regression_model = LinearRegression(normalize = False)\n",
    "    #fit model\n",
    "    regression_model.fit(feature_data,predictor_data)\n",
    "    global coef\n",
    "    coef = regression_model.coef_\n",
    "    intercept = regression_model.intercept_\n",
    "    \n",
    "    #Make predictions\n",
    "    global predicted_count\n",
    "    predicted_count = regression_model.predict(stat_array).flatten()\n",
    "    print(\"The coefficients are: {}\".format(coef))\n",
    "    print(\"The intercept is: {}\".format(intercept))\n",
    "    \n",
    " \n",
    "    #calculate the mean squared error\n",
    "    mse = mean_squared_error(week14countlist,predicted_count)\n",
    "    mae = mean_absolute_error(week14countlist,predicted_count)\n",
    "    rmse = math.sqrt(mse)\n",
    "    r2 = r2_score(week14countlist,predicted_count)\n",
    "\n",
    "    #display results\n",
    "    print(\"MSE: {}\".format(mse))\n",
    "    print(\"MAE: {}\".format(mae))\n",
    "    print(\"RMSE: {}\".format(rmse))\n",
    "    print(\"r2_score: {}\".format(r2))\n",
    "    return predicted_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For prediction, I will use the Week 14 data (not used in training model)** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [],
   "source": [
    "week14 = pd.read_csv(r'C:\\Users\\ddudley\\Documents\\week14_upd.csv')\n",
    "#get list of players for creating new DF\n",
    "playerlist = pd.Series(week14.Player).to_list()\n",
    "week14.drop('Unnamed: 0', axis = 1,inplace = True)\n",
    "week14.set_index('Player',inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets view the data from Week 14!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Week</th>\n",
       "      <th>T_Snaps</th>\n",
       "      <th>T_pressure</th>\n",
       "      <th>Sacks</th>\n",
       "      <th>Hurries</th>\n",
       "      <th>T_Tackles</th>\n",
       "      <th>Missed_Tackles</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Forced_Fumble</th>\n",
       "      <th>Int</th>\n",
       "      <th>Pass_Breakup</th>\n",
       "      <th>Penalty</th>\n",
       "      <th>y_per_rec</th>\n",
       "      <th>rec</th>\n",
       "      <th>red_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Darius Leonard</th>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malik Hooker</th>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pierre Desir</th>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25.7</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quincy Wilson</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George Odum</th>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rock Ya-Sin</th>\n",
       "      <td>14</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bobby Okereke</th>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EJ Speed</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Justin Houston</th>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jabaal Sheard</th>\n",
       "      <td>14</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Khari Willis</th>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denico Autry</th>\n",
       "      <td>14</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clayton Geathers</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kemoko Turay</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anthony Walker</th>\n",
       "      <td>14</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grover Stewart</th>\n",
       "      <td>14</td>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shakial Taylor</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al-Quadin Muhammad</th>\n",
       "      <td>14</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kenny Moore</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marvell Tell</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carl Davis</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trevon Coley</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyquan Lewis</th>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rolan Milligan</th>\n",
       "      <td>14</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ben Banogu</th>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthew Adams</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zaire Franklin</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Margus Hunt</th>\n",
       "      <td>14</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Week  T_Snaps  T_pressure  Sacks  Hurries  T_Tackles  \\\n",
       "Player                                                                     \n",
       "Darius Leonard        14       78           3      0        3          6   \n",
       "Malik Hooker          14       78           0      0        0          7   \n",
       "Pierre Desir          14       78           0      0        0          4   \n",
       "Quincy Wilson         14        4           0      0        0          0   \n",
       "George Odum           14       12           0      0        0          5   \n",
       "Rock Ya-Sin           14       75           0      0        0          3   \n",
       "Bobby Okereke         14       27           1      0        1          2   \n",
       "EJ Speed              14        0           0      0        0          0   \n",
       "Justin Houston        14       45           3      1        2          2   \n",
       "Jabaal Sheard         14       49           4      0        3          0   \n",
       "Khari Willis          14       68           1      0        1         10   \n",
       "Denico Autry          14       48           1      0        1          1   \n",
       "Clayton Geathers      14       14           0      0        0          2   \n",
       "Kemoko Turay          14        0           0      0        0          0   \n",
       "Anthony Walker        14       62           1      0        0          3   \n",
       "Grover Stewart        14       58           2      0        2          2   \n",
       "Shakial Taylor        14        0           0      0        0          0   \n",
       "Al-Quadin Muhammad    14       33           2      0        0          0   \n",
       "Kenny Moore           14        0           0      0        0          0   \n",
       "Marvell Tell          14        2           0      0        0          0   \n",
       "Carl Davis            14        0           0      0        0          0   \n",
       "Trevon Coley          14        0           0      0        0          0   \n",
       "Tyquan Lewis          14       26           1      0        1          1   \n",
       "Rolan Milligan        14       50           2      0        1          3   \n",
       "Ben Banogu            14       18           1      0        1          0   \n",
       "Matthew Adams         14        0           0      0        0          0   \n",
       "Zaire Franklin        14        0           0      0        0          0   \n",
       "Margus Hunt           14       33           0      0        0          2   \n",
       "\n",
       "                    Missed_Tackles  Stop  Forced_Fumble  Int  Pass_Breakup  \\\n",
       "Player                                                                       \n",
       "Darius Leonard                   1     4              0    2             0   \n",
       "Malik Hooker                     1     0              0    1             0   \n",
       "Pierre Desir                     1     0              0    0             1   \n",
       "Quincy Wilson                    0     0              0    0             0   \n",
       "George Odum                      0     0              0    0             0   \n",
       "Rock Ya-Sin                      0     0              0    0             1   \n",
       "Bobby Okereke                    0     1              0    0             0   \n",
       "EJ Speed                         0     0              0    0             0   \n",
       "Justin Houston                   0     3              0    0             0   \n",
       "Jabaal Sheard                    0     0              0    0             0   \n",
       "Khari Willis                     1     4              0    0             0   \n",
       "Denico Autry                     2     1              0    0             0   \n",
       "Clayton Geathers                 0     1              0    0             0   \n",
       "Kemoko Turay                     0     0              0    0             0   \n",
       "Anthony Walker                   3     2              0    0             0   \n",
       "Grover Stewart                   0     2              0    0             0   \n",
       "Shakial Taylor                   0     0              0    0             0   \n",
       "Al-Quadin Muhammad               1     0              0    0             0   \n",
       "Kenny Moore                      0     0              0    0             0   \n",
       "Marvell Tell                     0     0              0    0             0   \n",
       "Carl Davis                       0     0              0    0             0   \n",
       "Trevon Coley                     0     0              0    0             0   \n",
       "Tyquan Lewis                     0     1              0    0             0   \n",
       "Rolan Milligan                   1     0              0    0             0   \n",
       "Ben Banogu                       0     0              0    0             0   \n",
       "Matthew Adams                    0     0              0    0             0   \n",
       "Zaire Franklin                   0     0              0    0             0   \n",
       "Margus Hunt                      0     0              0    0             0   \n",
       "\n",
       "                    Penalty  y_per_rec  rec  red_count  \n",
       "Player                                                  \n",
       "Darius Leonard            0        7.2    5        116  \n",
       "Malik Hooker              0       14.0    4         88  \n",
       "Pierre Desir              0       25.7    3         19  \n",
       "Quincy Wilson             0       12.0    1         14  \n",
       "George Odum               0       16.0    4         12  \n",
       "Rock Ya-Sin               0       20.5    2          9  \n",
       "Bobby Okereke             0        0.0    0          7  \n",
       "EJ Speed                  0        0.0    0          6  \n",
       "Justin Houston            1       13.0    1          5  \n",
       "Jabaal Sheard             0        0.0    0          4  \n",
       "Khari Willis              0        5.0    4          3  \n",
       "Denico Autry              0        0.0    0          3  \n",
       "Clayton Geathers          0        4.0    1          3  \n",
       "Kemoko Turay              0        0.0    0          2  \n",
       "Anthony Walker            0       15.5    4          2  \n",
       "Grover Stewart            0        0.0    0          1  \n",
       "Shakial Taylor            0        0.0    0          1  \n",
       "Al-Quadin Muhammad        0        0.0    0          1  \n",
       "Kenny Moore               0        0.0    0          0  \n",
       "Marvell Tell              0       31.0    1          0  \n",
       "Carl Davis                0        0.0    0          0  \n",
       "Trevon Coley              0        0.0    0          0  \n",
       "Tyquan Lewis              0        0.0    0          0  \n",
       "Rolan Milligan            0       11.6    5          0  \n",
       "Ben Banogu                0        0.0    0          0  \n",
       "Matthew Adams             0        0.0    0          0  \n",
       "Zaire Franklin            0        0.0    0          0  \n",
       "Margus Hunt               0        0.0    0          0  "
      ]
     },
     "execution_count": 629,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week14.sort_values(by='red_count',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create testing feature set for the prediction\n",
    "x_test = week14[['T_Snaps','T_Tackles','Int','Penalty','rec']].to_numpy()\n",
    "#create numpy array to be used in df, for comparing to predicted results\n",
    "week14counts = week14[['red_count']].to_numpy()\n",
    "week14countlist = week14counts.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficients are: [[ 0.12309031  1.38555467 34.62926049 26.8231353   0.39604614]]\n",
      "The intercept is: [-1.80631905]\n",
      "MSE: 137.60392972753078\n",
      "MAE: 7.285957551672994\n",
      "RMSE: 11.730470140941955\n",
      "r2_score: 0.7973950162517764\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([11.56612859, -1.80631905,  4.2882286 , 87.34680465, -1.80631905,\n",
       "       -1.80631905,  5.48757039,  0.40930649, 33.72303557,  2.77958362,\n",
       "        2.2556611 ,  4.22510603, -1.80631905, -1.80631905,  5.02677045,\n",
       "        8.10402814, -1.80631905, -1.80631905, -1.1640923 , -0.91791168,\n",
       "       12.37421031,  3.08410074, 53.70705269, 10.48509103,  8.18272254,\n",
       "       22.00355313, -1.80631905, 14.52508205])"
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Run the prediction\n",
    "regression_model_predicting(XDATA_top5,YDATA_top5,x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prediction = pd.DataFrame({'Actual':week14countlist,'Predicted':predicted_count},index = playerlist)\n",
    "df_prediction['Difference'] = df_prediction['Predicted'] - df_prediction['Actual']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_prediction['over_under'] = np.where(df_prediction['Difference']<0,'Under','Over')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Difference</th>\n",
       "      <th>over_under</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Anthony Walker</th>\n",
       "      <td>2</td>\n",
       "      <td>11.566129</td>\n",
       "      <td>9.566129</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EJ Speed</th>\n",
       "      <td>6</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>-7.806319</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bobby Okereke</th>\n",
       "      <td>7</td>\n",
       "      <td>4.288229</td>\n",
       "      <td>-2.711771</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darius Leonard</th>\n",
       "      <td>116</td>\n",
       "      <td>87.346805</td>\n",
       "      <td>-28.653195</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zaire Franklin</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthew Adams</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denico Autry</th>\n",
       "      <td>3</td>\n",
       "      <td>5.487570</td>\n",
       "      <td>2.487570</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ben Banogu</th>\n",
       "      <td>0</td>\n",
       "      <td>0.409306</td>\n",
       "      <td>0.409306</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Justin Houston</th>\n",
       "      <td>5</td>\n",
       "      <td>33.723036</td>\n",
       "      <td>28.723036</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tyquan Lewis</th>\n",
       "      <td>0</td>\n",
       "      <td>2.779584</td>\n",
       "      <td>2.779584</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Al-Quadin Muhammad</th>\n",
       "      <td>1</td>\n",
       "      <td>2.255661</td>\n",
       "      <td>1.255661</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jabaal Sheard</th>\n",
       "      <td>4</td>\n",
       "      <td>4.225106</td>\n",
       "      <td>0.225106</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trevon Coley</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carl Davis</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Margus Hunt</th>\n",
       "      <td>0</td>\n",
       "      <td>5.026770</td>\n",
       "      <td>5.026770</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grover Stewart</th>\n",
       "      <td>1</td>\n",
       "      <td>8.104028</td>\n",
       "      <td>7.104028</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kenny Moore</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shakial Taylor</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>-2.806319</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marvell Tell</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.164092</td>\n",
       "      <td>-1.164092</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quincy Wilson</th>\n",
       "      <td>14</td>\n",
       "      <td>-0.917912</td>\n",
       "      <td>-14.917912</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rock Ya-Sin</th>\n",
       "      <td>9</td>\n",
       "      <td>12.374210</td>\n",
       "      <td>3.374210</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clayton Geathers</th>\n",
       "      <td>3</td>\n",
       "      <td>3.084101</td>\n",
       "      <td>0.084101</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malik Hooker</th>\n",
       "      <td>88</td>\n",
       "      <td>53.707053</td>\n",
       "      <td>-34.292947</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rolan Milligan</th>\n",
       "      <td>0</td>\n",
       "      <td>10.485091</td>\n",
       "      <td>10.485091</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George Odum</th>\n",
       "      <td>12</td>\n",
       "      <td>8.182723</td>\n",
       "      <td>-3.817277</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Khari Willis</th>\n",
       "      <td>3</td>\n",
       "      <td>22.003553</td>\n",
       "      <td>19.003553</td>\n",
       "      <td>Over</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kemoko Turay</th>\n",
       "      <td>2</td>\n",
       "      <td>-1.806319</td>\n",
       "      <td>-3.806319</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pierre Desir</th>\n",
       "      <td>19</td>\n",
       "      <td>14.525082</td>\n",
       "      <td>-4.474918</td>\n",
       "      <td>Under</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Actual  Predicted  Difference over_under\n",
       "Anthony Walker           2  11.566129    9.566129       Over\n",
       "EJ Speed                 6  -1.806319   -7.806319      Under\n",
       "Bobby Okereke            7   4.288229   -2.711771      Under\n",
       "Darius Leonard         116  87.346805  -28.653195      Under\n",
       "Zaire Franklin           0  -1.806319   -1.806319      Under\n",
       "Matthew Adams            0  -1.806319   -1.806319      Under\n",
       "Denico Autry             3   5.487570    2.487570       Over\n",
       "Ben Banogu               0   0.409306    0.409306       Over\n",
       "Justin Houston           5  33.723036   28.723036       Over\n",
       "Tyquan Lewis             0   2.779584    2.779584       Over\n",
       "Al-Quadin Muhammad       1   2.255661    1.255661       Over\n",
       "Jabaal Sheard            4   4.225106    0.225106       Over\n",
       "Trevon Coley             0  -1.806319   -1.806319      Under\n",
       "Carl Davis               0  -1.806319   -1.806319      Under\n",
       "Margus Hunt              0   5.026770    5.026770       Over\n",
       "Grover Stewart           1   8.104028    7.104028       Over\n",
       "Kenny Moore              0  -1.806319   -1.806319      Under\n",
       "Shakial Taylor           1  -1.806319   -2.806319      Under\n",
       "Marvell Tell             0  -1.164092   -1.164092      Under\n",
       "Quincy Wilson           14  -0.917912  -14.917912      Under\n",
       "Rock Ya-Sin              9  12.374210    3.374210       Over\n",
       "Clayton Geathers         3   3.084101    0.084101       Over\n",
       "Malik Hooker            88  53.707053  -34.292947      Under\n",
       "Rolan Milligan           0  10.485091   10.485091       Over\n",
       "George Odum             12   8.182723   -3.817277      Under\n",
       "Khari Willis             3  22.003553   19.003553       Over\n",
       "Kemoko Turay             2  -1.806319   -3.806319      Under\n",
       "Pierre Desir            19  14.525082   -4.474918      Under"
      ]
     },
     "execution_count": 605,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average difference between the prediction and actual is 7.285957551672992 reddit counts\n"
     ]
    }
   ],
   "source": [
    "print(\"The average difference between the prediction and actual is {} reddit counts\".format(df_prediction.Difference.abs().mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Under    15\n",
       "Over     13\n",
       "Name: over_under, dtype: int64"
      ]
     },
     "execution_count": 607,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prediction['over_under'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion Points\n",
    "1. The average difference between the actual counts and the models prediction is roughly 7 counts.\n",
    "2. Of 28, the model overpredicted the count 15 times, and underpredicted 13 times. \n",
    "3. The project showed that defensive statistics can be a measure of how often a player is mentioned in the Colts subreddit game threads,  but it fails to account for context of other reasons why a players are talked about in the subreddit (i.e. player pediogre vs statistical performance, player playing through injury, etc)\n",
    "4. It's fair to question the accuracy of the player statistics pulled from PFF. For example does it record every single defensive penalty? Every single pass defensed? More time could have been spent varying these statistics in Part 1. \n",
    "5. Justin Houston's data negatively impacted this project/model. Even though he leads the defense is some statistical categories, I had to delete him from the model because his last name resulted in overestimating his Reddit counts - this is because the Houston Texans are in the Colts division and the subreddit mentions that team very often. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lagniappe; Analysis of the defensive position groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, I want to prepare the data by grouping dataframes by position groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Edges\n",
    "JH_df = merged_df[(merged_df.index == 'Justin Houston')]\n",
    "JH_df2 = JH_df.sort_values(by=['Week'])\n",
    "BB_df = merged_df[(merged_df.index == 'Ben Banogu')]\n",
    "BB_df2 = BB_df.sort_values(by=['Week'])\n",
    "JS_df = merged_df[(merged_df.index == 'Jabaal Sheard')]\n",
    "JS_df2 = JS_df.sort_values(by=['Week'])\n",
    "AM_df = merged_df[(merged_df.index == 'Al-Quadin Muhammad')]\n",
    "AM_df2 = AM_df.sort_values(by=['Week'])\n",
    "KT_df = merged_df[(merged_df.index == 'Kemoko Turay')]\n",
    "KT_df2 = KT_df.sort_values(by=['Week'])\n",
    "edge_frame = [JH_df2,BB_df2,JS_df2,AM_df2,KT_df2]\n",
    "edge_df = pd.concat(edge_frame)\n",
    "\n",
    "#CB's\n",
    "RYS_df = merged_df[(merged_df.index == 'Rock Ya-Sin')]\n",
    "RYS_df2 = RYS_df.sort_values(by=['Week'])\n",
    "KM_df = merged_df[(merged_df.index == 'Kenny Moore')]\n",
    "KM_df2 = KM_df.sort_values(by=['Week'])\n",
    "QW_df = merged_df[(merged_df.index == 'Quincy Wilson')]\n",
    "QW_df2 = QW_df.sort_values(by=['Week'])\n",
    "PD_df = merged_df[(merged_df.index == 'Pierre Desir')]\n",
    "PD_df2 = PD_df.sort_values(by=['Week'])\n",
    "MT_df = merged_df[(merged_df.index == 'Marvell Tell')]\n",
    "MT_df2 =MT_df.sort_values(by=['Week'])\n",
    "cb_frame = [RYS_df2,KM_df2,QW_df2,PD_df2,MT_df2]\n",
    "cb_df = pd.concat(cb_frame)\n",
    "\n",
    "#LB's\n",
    "DL_df = merged_df[(merged_df.index == 'Darius Leonard')]\n",
    "DL_df2 = DL_df.sort_values(by=['Week'])\n",
    "AW_df = merged_df[(merged_df.index == 'Anthony Walker')]\n",
    "AW_df2 =AW_df.sort_values(by=['Week'])\n",
    "BO_df = merged_df[(merged_df.index == 'Bobby Okereke')]\n",
    "BO_df2 = BO_df.sort_values(by=['Week'])\n",
    "lb_frame = [DL_df2,AW_df2,BO_df2]\n",
    "lb_df = pd.concat(lb_frame)\n",
    "\n",
    "#SAF's\n",
    "MH_df = merged_df[(merged_df.index == 'Malik Hooker')]\n",
    "MH_df2 = MH_df.sort_values(by=['Week'])\n",
    "CG_df = merged_df[(merged_df.index == 'Clayton Geathers')]\n",
    "CG_df2 = CG_df.sort_values(by=['Week'])\n",
    "GO_df = merged_df[(merged_df.index == 'George Odum')]\n",
    "GO_df2 = GO_df.sort_values(by=['Week'])\n",
    "KW_df = merged_df[(merged_df.index == 'Khari Willis')]\n",
    "KW_df2 = KW_df.sort_values(by=['Week'])\n",
    "saf_frame = [MH_df2,CG_df2,GO_df2,KW_df2]\n",
    "saf_df = pd.concat(saf_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['T_Snaps', 'T_pressure', 'Sacks', 'Hurries', 'T_Tackles',\n",
       "       'Missed_Tackles', 'Stop', 'Forced_Fumble', 'Int', 'Pass_Breakup',\n",
       "       'Penalty', 'y_per_rec', 'rec', 'red_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 649,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summed_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defensive Awards "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Player with max T_Snaps is: Kenny Moore\n",
      "Player with max T_pressure is: Denico Autry\n",
      "Player with max Sacks is: Darius Leonard\n",
      "Player with max Hurries is: Jabaal Sheard\n",
      "Player with max T_Tackles is: Anthony Walker\n",
      "Player with max Missed_Tackles is: Anthony Walker\n",
      "Player with max Stop is: Anthony Walker\n",
      "Player with max Forced_Fumble is: Darius Leonard\n",
      "Player with max Int is: Darius Leonard\n",
      "Player with max Pass_Breakup is: Marvell Tell\n",
      "Player with max Penalty is: Rock Ya-Sin\n",
      "Player with max y_per_rec is: Rock Ya-Sin\n",
      "Player with max rec is: Darius Leonard\n",
      "Player with max red_count is: Rock Ya-Sin\n"
     ]
    }
   ],
   "source": [
    "for col in summed_df:\n",
    "    print(\"Player with max {} is: {}\".format(col,summed_df[col].idxmax()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the impact of getting just one more tackle? One more interception? Allowing one more reception? Having one more penalty? Play one more snap?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To answer this, we just need to observe the coefficients created by the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.12309031  1.38555467 34.62926049 26.8231353   0.39604614]]\n"
     ]
    }
   ],
   "source": [
    "#Let's call the coef variable that we created in the Regression_Model_Predicting function\n",
    "print(coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If a player plays one more snap, the model predicts they will be mentioned in Reddit 0.12309030769327861 more times\n",
      "If a player gets one more tackle, the model predicts they will be mentioned in Colts Reddit 1.3855546716827765 more times\n",
      "If a player gets one more interception, the model predicts they will be mentioned in Reddit 34.629260493535135 more times\n",
      "If a player gets one more penalty, the model predicts they will be mentioned in Reddit 26.82313529865658 more times\n",
      "If a player allows one more reception, the model predicts they will be mentioned in Reddit 0.3960461358118208 more times\n"
     ]
    }
   ],
   "source": [
    "print(\"If a player plays one more snap, the model predicts they will be mentioned in Reddit {} more times\".format(coef[0,0]))\n",
    "print(\"If a player gets one more tackle, the model predicts they will be mentioned in Colts Reddit {} more times\".format(coef[0,1]))\n",
    "print(\"If a player gets one more interception, the model predicts they will be mentioned in Reddit {} more times\".format(coef[0,2]))\n",
    "print(\"If a player gets one more penalty, the model predicts they will be mentioned in Reddit {} more times\".format(coef[0,3]))\n",
    "print(\"If a player allows one more reception, the model predicts they will be mentioned in Reddit {} more times\".format(coef[0,4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which position group has the most tackles?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total tackles in the Edge group is: 59\n",
      "The total tackles in the Linebacker group is: 138\n",
      "The total tackles in the Cornerback group is: 141\n",
      "The total tackles in the Safety group is: 109\n"
     ]
    }
   ],
   "source": [
    "print(\"The total tackles in the Edge group is: {}\".format(edges['T_Tackles'].sum()))\n",
    "print(\"The total tackles in the Linebacker group is: {}\".format(lbs['T_Tackles'].sum()))\n",
    "print(\"The total tackles in the Cornerback group is: {}\".format(cbs_summed['T_Tackles'].sum()))\n",
    "print(\"The total tackles in the Safety group is: {}\".format(safs['T_Tackles'].sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which position group has the most pressures on the opposing QB?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total pressures in the Edge group is: 128\n",
      "The total pressures in the Linebacker group is: 20\n",
      "The total pressures in the Cornerback group is: 7\n",
      "The total pressures in the Safety group is: 6\n"
     ]
    }
   ],
   "source": [
    "print(\"The total pressures in the Edge group is: {}\".format(edges['T_pressure'].sum()))\n",
    "print(\"The total pressures in the Linebacker group is: {}\".format(lbs['T_pressure'].sum()))\n",
    "print(\"The total pressures in the Cornerback group is: {}\".format(cbs_summed['T_pressure'].sum()))\n",
    "print(\"The total pressures in the Safety group is: {}\".format(safs['T_pressure'].sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which position group has the most takeaways (forced fumbles and interceptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total takeaways in the Edge group is: 4.0\n",
      "The total takeaways in the Linebacker group is: 7.0\n",
      "The total takeaways in the Cornerback group is: 5.0\n",
      "The total takeaways in the Safety group is: 3.0\n"
     ]
    }
   ],
   "source": [
    "print(\"The total takeaways in the Edge group is: {}\".format(edges['Int'].sum()+edges['Forced_Fumble'].sum()))\n",
    "print(\"The total takeaways in the Linebacker group is: {}\".format(lbs['Int'].sum() +lbs['Forced_Fumble'].sum()))\n",
    "print(\"The total takeaways in the Cornerback group is: {}\".format(cbs_summed['Int'].sum() + cbs_summed['Forced_Fumble'].sum()))\n",
    "print(\"The total takeaways in the Safety group is: {}\".format(safs['Int'].sum() + safs['Forced_Fumble'].sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which position group gets the most love?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total count per player in the Colts Subreddit of the Edge group is: 65.4\n",
      "The total count per player in the Colts Subreddit of the Linebacker group is: 227.33333333333334\n",
      "The total count per player in the Colts Subreddit of the Cornerback group is: 223.6\n",
      "The total count per player in the Colts Subreddit of the Safety group is: 116.25\n"
     ]
    }
   ],
   "source": [
    "print(\"The total count per player in the Colts Subreddit of the Edge group is: {}\".format((edges['red_count'].sum())/5))\n",
    "print(\"The total count per player in the Colts Subreddit of the Linebacker group is: {}\".format((lbs['red_count'].sum())/3))\n",
    "print(\"The total count per player in the Colts Subreddit of the Cornerback group is: {}\".format((cbs_summed['red_count'].sum())/5))\n",
    "print(\"The total count per player in the Colts Subreddit of the Safety group is: {}\".format((safs['red_count'].sum())/4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There it is folks, the Colts subreddit loves the Linebacker group.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linebacker Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T_Snaps</th>\n",
       "      <th>T_pressure</th>\n",
       "      <th>Sacks</th>\n",
       "      <th>Hurries</th>\n",
       "      <th>T_Tackles</th>\n",
       "      <th>Missed_Tackles</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Forced_Fumble</th>\n",
       "      <th>Int</th>\n",
       "      <th>Pass_Breakup</th>\n",
       "      <th>Penalty</th>\n",
       "      <th>y_per_rec</th>\n",
       "      <th>rec</th>\n",
       "      <th>red_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Darius Leonard</th>\n",
       "      <td>552</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>85.0</td>\n",
       "      <td>40</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anthony Walker</th>\n",
       "      <td>579</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>16</td>\n",
       "      <td>38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>109.8</td>\n",
       "      <td>37</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bobby Okereke</th>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>21</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                T_Snaps  T_pressure  Sacks  Hurries  T_Tackles  \\\n",
       "Player                                                           \n",
       "Darius Leonard      552          13      5        7         53   \n",
       "Anthony Walker      579           6      2        4         57   \n",
       "Bobby Okereke       346           1      1        0         28   \n",
       "\n",
       "                Missed_Tackles  Stop  Forced_Fumble  Int  Pass_Breakup  \\\n",
       "Player                                                                   \n",
       "Darius Leonard               5    23            2.0    2             0   \n",
       "Anthony Walker              16    38            1.0    0             1   \n",
       "Bobby Okereke                4    16            1.0    1             1   \n",
       "\n",
       "                Penalty  y_per_rec  rec  red_count  \n",
       "Player                                              \n",
       "Darius Leonard        2       85.0   40        459  \n",
       "Anthony Walker        0      109.8   37        102  \n",
       "Bobby Okereke         0       63.0   21        121  "
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lbs = summed_df.loc[['Darius Leonard','Anthony Walker','Bobby Okereke']]\n",
    "lbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 667,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=DL_df2['Week']\n",
    "\n",
    "aa_lb=dict(zip(x,DL_df2['Stop']))\n",
    "bb_lb=dict(zip(x,AW_df2['Stop']))\n",
    "cc_lb=dict(zip(x,BO_df2['Stop']))\n",
    "\n",
    "\n",
    "\n",
    "dfbar_lbs = pd.DataFrame({'Darius Leonard': aa_lb, 'Anthony Walker': bb_lb, 'Bobby Okereke': cc_lb}, index=x)\n",
    "\n",
    "# Non-stacked bar plot\n",
    "dfbar_lbs.plot.bar(figsize=(16, 6),width = 0.6)\n",
    "\n",
    "plt.title(\"Stops by Week\")\n",
    "plt.legend(title=\"Player\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cornerback Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T_Snaps</th>\n",
       "      <th>T_pressure</th>\n",
       "      <th>Sacks</th>\n",
       "      <th>Hurries</th>\n",
       "      <th>T_Tackles</th>\n",
       "      <th>Missed_Tackles</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Forced_Fumble</th>\n",
       "      <th>Int</th>\n",
       "      <th>Pass_Breakup</th>\n",
       "      <th>Penalty</th>\n",
       "      <th>y_per_rec</th>\n",
       "      <th>rec</th>\n",
       "      <th>red_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kenny Moore</th>\n",
       "      <td>651</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>39</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pierre Desir</th>\n",
       "      <td>409</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>95.4</td>\n",
       "      <td>29</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rock Ya-Sin</th>\n",
       "      <td>571</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>151.9</td>\n",
       "      <td>28</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quincy Wilson</th>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>74.2</td>\n",
       "      <td>10</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marvell Tell</th>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               T_Snaps  T_pressure  Sacks  Hurries  T_Tackles  Missed_Tackles  \\\n",
       "Player                                                                          \n",
       "Kenny Moore        651           7      3        3         53               7   \n",
       "Pierre Desir       409           0      0        0         25               5   \n",
       "Rock Ya-Sin        571           0      0        0         38               3   \n",
       "Quincy Wilson      105           0      0        0          8               0   \n",
       "Marvell Tell       219           0      0        0         17               0   \n",
       "\n",
       "               Stop  Forced_Fumble  Int  Pass_Breakup  Penalty  y_per_rec  \\\n",
       "Player                                                                      \n",
       "Kenny Moore      31            0.0    2             1        0      110.0   \n",
       "Pierre Desir      5            0.0    1             3        4       95.4   \n",
       "Rock Ya-Sin      14            0.0    1             1        7      151.9   \n",
       "Quincy Wilson     2            0.0    0             0        3       74.2   \n",
       "Marvell Tell      5            1.0    0             5        2       72.0   \n",
       "\n",
       "               rec  red_count  \n",
       "Player                         \n",
       "Kenny Moore     39        371  \n",
       "Pierre Desir    29         92  \n",
       "Rock Ya-Sin     28        474  \n",
       "Quincy Wilson   10        167  \n",
       "Marvell Tell    17         14  "
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbs_summed = summed_df.loc[['Kenny Moore','Pierre Desir','Rock Ya-Sin','Quincy Wilson','Marvell Tell']]\n",
    "cbs_summed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 668,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xcbs=KM_df2['Week']\n",
    "\n",
    "aa_cb=dict(zip(xcbs,KM_df2['rec']))\n",
    "bb_cb=dict(zip(xcbs,PD_df2['rec']))\n",
    "cc_cb=dict(zip(xcbs,RYS_df2['rec']))\n",
    "dd_cb=dict(zip(xcbs,QW_df2['rec']))\n",
    "ee_cb=dict(zip(xcbs,MT_df2['rec']))\n",
    "\n",
    "cbslabels = ['Kenny Moore','Pierre Desir','Rock Ya Sin','Quincy Wilson','Marvell Tell']\n",
    "\n",
    "\n",
    "dfbar_cbs = pd.DataFrame({'Kenny Moore': aa_cb, 'Pierre Desir': bb_cb, 'Rock Ya Sin': cc_cb,'Quincy Wilson': dd_cb,'Marvell Tell': ee_cb}, index=x)\n",
    "\n",
    "# Non-stacked bar plot\n",
    "dfbar_cbs.plot.bar(figsize=(16, 6),width = 0.6)\n",
    "\n",
    "plt.title(\"Cornerback Receptions Allowed by Week\")\n",
    "plt.legend(labels = cbslabels)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Safety Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T_Snaps</th>\n",
       "      <th>T_pressure</th>\n",
       "      <th>Sacks</th>\n",
       "      <th>Hurries</th>\n",
       "      <th>T_Tackles</th>\n",
       "      <th>Missed_Tackles</th>\n",
       "      <th>Stop</th>\n",
       "      <th>Forced_Fumble</th>\n",
       "      <th>Int</th>\n",
       "      <th>Pass_Breakup</th>\n",
       "      <th>Penalty</th>\n",
       "      <th>y_per_rec</th>\n",
       "      <th>rec</th>\n",
       "      <th>red_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Malik Hooker</th>\n",
       "      <td>504</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clayton Geathers</th>\n",
       "      <td>418</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44.8</td>\n",
       "      <td>8</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Khari Willis</th>\n",
       "      <td>459</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56.5</td>\n",
       "      <td>15</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George Odum</th>\n",
       "      <td>224</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>80.5</td>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  T_Snaps  T_pressure  Sacks  Hurries  T_Tackles  \\\n",
       "Player                                                             \n",
       "Malik Hooker          504           0      0        0         23   \n",
       "Clayton Geathers      418           0      0        0         29   \n",
       "Khari Willis          459           4      0        4         39   \n",
       "George Odum           224           2      1        0         18   \n",
       "\n",
       "                  Missed_Tackles  Stop  Forced_Fumble  Int  Pass_Breakup  \\\n",
       "Player                                                                     \n",
       "Malik Hooker                   7     7            0.0    1             1   \n",
       "Clayton Geathers               5     8            0.0    1             0   \n",
       "Khari Willis                   6    15            0.0    0             1   \n",
       "George Odum                    3     4            1.0    0             0   \n",
       "\n",
       "                  Penalty  y_per_rec  rec  red_count  \n",
       "Player                                                \n",
       "Malik Hooker            0       30.0    4        216  \n",
       "Clayton Geathers        1       44.8    8         82  \n",
       "Khari Willis            1       56.5   15        122  \n",
       "George Odum             1       80.5    7         45  "
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "safs = summed_df.loc[['Malik Hooker','Clayton Geathers','Khari Willis','George Odum']]\n",
    "safs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xsaf=KM_df2['Week']\n",
    "\n",
    "aa_saf=dict(zip(xsaf,MH_df2['T_Snaps']))\n",
    "bb_saf=dict(zip(xsaf,CG_df2['T_Snaps']))\n",
    "cc_saf=dict(zip(xsaf,KW_df2['T_Snaps']))\n",
    "dd_saf=dict(zip(xsaf,GO_df2['T_Snaps']))\n",
    "\n",
    "\n",
    "saflabels = ['Malik Hooker','Clayton Geathers','Khari Willis','George Odum']\n",
    "\n",
    "\n",
    "dfbar_saf = pd.DataFrame({'Malik Hooker': aa_saf, 'Clayton Geathers': bb_saf, 'Khari Willis': cc_saf,'George Odum': dd_saf}, index=x)\n",
    "\n",
    "# Non-stacked bar plot\n",
    "dfbar_saf.plot.bar(figsize=(16, 6),width = 0.6)\n",
    "\n",
    "plt.title(\"Safety Snaps by Week\")\n",
    "plt.legend(labels = saflabels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Edge Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges = merged_df.loc[['Justin Houston','Jabaal Sheard','Ben Banogu','Al-Quadin Muhammad','Kemoko Turay']]\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}