NBA Stats Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a25d6724",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "43ba91dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38fe28ce",
   "metadata": {},
   "source": [
    "### Load player mvp stats CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9408b74",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = pd.read_csv(\"player_mvp_stats.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd83e257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Unnamed: 0</th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>...</th>\n",
       "      <th>Pts Max</th>\n",
       "      <th>Share</th>\n",
       "      <th>Team</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>W/L%</th>\n",
       "      <th>GB</th>\n",
       "      <th>PS/G</th>\n",
       "      <th>PA/G</th>\n",
       "      <th>SRS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>PF</td>\n",
       "      <td>27</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82</td>\n",
       "      <td>21</td>\n",
       "      <td>26.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>58</td>\n",
       "      <td>24</td>\n",
       "      <td>0.707</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>6.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Byron Scott</td>\n",
       "      <td>SG</td>\n",
       "      <td>29</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>32.1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>12.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>58</td>\n",
       "      <td>24</td>\n",
       "      <td>0.707</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>6.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Elden Campbell</td>\n",
       "      <td>PF</td>\n",
       "      <td>22</td>\n",
       "      <td>LAL</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>58</td>\n",
       "      <td>24</td>\n",
       "      <td>0.707</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>6.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Irving Thomas</td>\n",
       "      <td>PF</td>\n",
       "      <td>25</td>\n",
       "      <td>LAL</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>58</td>\n",
       "      <td>24</td>\n",
       "      <td>0.707</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>6.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>James Worthy</td>\n",
       "      <td>SF</td>\n",
       "      <td>29</td>\n",
       "      <td>LAL</td>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>38.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>18.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>58</td>\n",
       "      <td>24</td>\n",
       "      <td>0.707</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>6.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14087</th>\n",
       "      <td>14087</td>\n",
       "      <td>Spencer Hawes</td>\n",
       "      <td>PF</td>\n",
       "      <td>28</td>\n",
       "      <td>MIL</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>0.512</td>\n",
       "      <td>9.0</td>\n",
       "      <td>103.6</td>\n",
       "      <td>103.8</td>\n",
       "      <td>-0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14088</th>\n",
       "      <td>14088</td>\n",
       "      <td>Steve Novak</td>\n",
       "      <td>PF</td>\n",
       "      <td>33</td>\n",
       "      <td>MIL</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>0.512</td>\n",
       "      <td>9.0</td>\n",
       "      <td>103.6</td>\n",
       "      <td>103.8</td>\n",
       "      <td>-0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14089</th>\n",
       "      <td>14089</td>\n",
       "      <td>Terrence Jones</td>\n",
       "      <td>PF</td>\n",
       "      <td>25</td>\n",
       "      <td>MIL</td>\n",
       "      <td>54</td>\n",
       "      <td>12</td>\n",
       "      <td>23.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>0.512</td>\n",
       "      <td>9.0</td>\n",
       "      <td>103.6</td>\n",
       "      <td>103.8</td>\n",
       "      <td>-0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14090</th>\n",
       "      <td>14090</td>\n",
       "      <td>Thon Maker</td>\n",
       "      <td>C</td>\n",
       "      <td>19</td>\n",
       "      <td>MIL</td>\n",
       "      <td>57</td>\n",
       "      <td>34</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>0.512</td>\n",
       "      <td>9.0</td>\n",
       "      <td>103.6</td>\n",
       "      <td>103.8</td>\n",
       "      <td>-0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14091</th>\n",
       "      <td>14091</td>\n",
       "      <td>Tony Snell</td>\n",
       "      <td>SG</td>\n",
       "      <td>25</td>\n",
       "      <td>MIL</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>29.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>0.512</td>\n",
       "      <td>9.0</td>\n",
       "      <td>103.6</td>\n",
       "      <td>103.8</td>\n",
       "      <td>-0.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14092 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0          Player Pos  Age   Tm   G  GS    MP   FG   FGA  ...   \n",
       "0               0      A.C. Green  PF   27  LAL  82  21  26.4  3.1   6.6  ...  \\\n",
       "1               1     Byron Scott  SG   29  LAL  82  82  32.1  6.1  12.8  ...   \n",
       "2               2  Elden Campbell  PF   22  LAL  52   0   7.3  1.1   2.4  ...   \n",
       "3               3   Irving Thomas  PF   25  LAL  26   0   4.2  0.7   1.9  ...   \n",
       "4               4    James Worthy  SF   29  LAL  78  74  38.6  9.2  18.7  ...   \n",
       "...           ...             ...  ..  ...  ...  ..  ..   ...  ...   ...  ...   \n",
       "14087       14087   Spencer Hawes  PF   28  MIL  54   1  14.8  2.5   5.1  ...   \n",
       "14088       14088     Steve Novak  PF   33  MIL   8   0   2.8  0.3   0.9  ...   \n",
       "14089       14089  Terrence Jones  PF   25  MIL  54  12  23.5  4.3   9.1  ...   \n",
       "14090       14090      Thon Maker   C   19  MIL  57  34   9.9  1.5   3.2  ...   \n",
       "14091       14091      Tony Snell  SG   25  MIL  80  80  29.2  3.1   6.8  ...   \n",
       "\n",
       "       Pts Max  Share                Team   W   L   W/L%   GB   PS/G   PA/G   \n",
       "0          0.0    0.0  Los Angeles Lakers  58  24  0.707  5.0  106.3   99.6  \\\n",
       "1          0.0    0.0  Los Angeles Lakers  58  24  0.707  5.0  106.3   99.6   \n",
       "2          0.0    0.0  Los Angeles Lakers  58  24  0.707  5.0  106.3   99.6   \n",
       "3          0.0    0.0  Los Angeles Lakers  58  24  0.707  5.0  106.3   99.6   \n",
       "4          0.0    0.0  Los Angeles Lakers  58  24  0.707  5.0  106.3   99.6   \n",
       "...        ...    ...                 ...  ..  ..    ...  ...    ...    ...   \n",
       "14087      0.0    0.0     Milwaukee Bucks  42  40  0.512  9.0  103.6  103.8   \n",
       "14088      0.0    0.0     Milwaukee Bucks  42  40  0.512  9.0  103.6  103.8   \n",
       "14089      0.0    0.0     Milwaukee Bucks  42  40  0.512  9.0  103.6  103.8   \n",
       "14090      0.0    0.0     Milwaukee Bucks  42  40  0.512  9.0  103.6  103.8   \n",
       "14091      0.0    0.0     Milwaukee Bucks  42  40  0.512  9.0  103.6  103.8   \n",
       "\n",
       "        SRS  \n",
       "0      6.73  \n",
       "1      6.73  \n",
       "2      6.73  \n",
       "3      6.73  \n",
       "4      6.73  \n",
       "...     ...  \n",
       "14087 -0.45  \n",
       "14088 -0.45  \n",
       "14089 -0.45  \n",
       "14090 -0.45  \n",
       "14091 -0.45  \n",
       "\n",
       "[14092 rows x 42 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0370c455",
   "metadata": {},
   "outputs": [],
   "source": [
    "del stats[\"Unnamed: 0\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e634cf8",
   "metadata": {},
   "source": [
    "### Find null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ca745680",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Player        0\n",
       "Pos           0\n",
       "Age           0\n",
       "Tm            0\n",
       "G             0\n",
       "GS            0\n",
       "MP            0\n",
       "FG            0\n",
       "FGA           0\n",
       "FG%          50\n",
       "3P            0\n",
       "3PA           0\n",
       "3P%        2042\n",
       "2P            0\n",
       "2PA           0\n",
       "2P%          84\n",
       "eFG%         50\n",
       "FT            0\n",
       "FTA           0\n",
       "FT%         462\n",
       "ORB           0\n",
       "DRB           0\n",
       "TRB           0\n",
       "AST           0\n",
       "STL           0\n",
       "BLK           0\n",
       "TOV           0\n",
       "PF            0\n",
       "PTS           0\n",
       "Year          0\n",
       "Pts Won       0\n",
       "Pts Max       0\n",
       "Share         0\n",
       "Team          0\n",
       "W             0\n",
       "L             0\n",
       "W/L%          0\n",
       "GB            0\n",
       "PS/G          0\n",
       "PA/G          0\n",
       "SRS           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(stats).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a73509",
   "metadata": {},
   "source": [
    "### Verify percentage is null b/c attempts are 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30e18858",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\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>Player</th>\n",
       "      <th>3PA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Elden Campbell</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Irving Thomas</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jack Haley</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Keith Owens</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Benoit Benjamin</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14061</th>\n",
       "      <td>Evan Eschmeyer</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14062</th>\n",
       "      <td>Gheorghe Mureșan</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14064</th>\n",
       "      <td>Jim McIlvaine</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14070</th>\n",
       "      <td>Mark Hendrickson</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14071</th>\n",
       "      <td>Michael Cage</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2042 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Player  3PA\n",
       "2        Elden Campbell  0.0\n",
       "3         Irving Thomas  0.0\n",
       "18           Jack Haley  0.0\n",
       "20          Keith Owens  0.0\n",
       "30      Benoit Benjamin  0.0\n",
       "...                 ...  ...\n",
       "14061    Evan Eschmeyer  0.0\n",
       "14062  Gheorghe Mureșan  0.0\n",
       "14064     Jim McIlvaine  0.0\n",
       "14070  Mark Hendrickson  0.0\n",
       "14071      Michael Cage  0.0\n",
       "\n",
       "[2042 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[pd.isnull(stats[\"3P%\"])][[\"Player\", \"3PA\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5cb6782",
   "metadata": {},
   "source": [
    "### Fill missing values with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "112353f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = stats.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f5c250c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Player', 'Pos', 'Age', 'Tm', 'G', 'GS', 'MP', 'FG', 'FGA', 'FG%', '3P',\n",
       "       '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%', 'ORB',\n",
       "       'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS', 'Year',\n",
       "       'Pts Won', 'Pts Max', 'Share', 'Team', 'W', 'L', 'W/L%', 'GB', 'PS/G',\n",
       "       'PA/G', 'SRS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c59906c",
   "metadata": {},
   "source": [
    "### Only use numerical values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab315f46",
   "metadata": {},
   "source": [
    "#### Pts Won, Pts Max, Share not included since they directly correlate with the dependent value (Share)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a9e22c27",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = ['Age', 'G', 'GS', 'MP', 'FG', 'FGA', 'FG%', '3P',\n",
    "       '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%', 'ORB',\n",
    "       'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS', 'Year', 'W',\n",
    "       'L','W/L%', 'GB', 'PS/G', 'PA/G', 'SRS']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cb81f7e",
   "metadata": {},
   "source": [
    "### Split into Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e96f42d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = stats[stats[\"Year\"] < 2021]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aa2e8ac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = stats[stats[\"Year\"] == 2021]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c054de96",
   "metadata": {},
   "source": [
    "### Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b9795fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = Ridge(alpha=.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d995bb",
   "metadata": {},
   "source": [
    "### Fit Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "54a63dec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=0.1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=0.1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(train[predictors], train[\"Share\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b8e494",
   "metadata": {},
   "source": [
    "### Test Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ccf3431d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = reg.predict(test[predictors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d521dc8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = pd.DataFrame(pred, columns=[\"predictions\"], index=test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7faa93e4",
   "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>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>0.013567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>-0.013756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>0.002414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>-0.004421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>0.010734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13897</th>\n",
       "      <td>-0.012571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13898</th>\n",
       "      <td>-0.011575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13899</th>\n",
       "      <td>0.016424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13900</th>\n",
       "      <td>-0.020434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13901</th>\n",
       "      <td>-0.005421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>540 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       predictions\n",
       "630       0.013567\n",
       "631      -0.013756\n",
       "632       0.002414\n",
       "633      -0.004421\n",
       "634       0.010734\n",
       "...            ...\n",
       "13897    -0.012571\n",
       "13898    -0.011575\n",
       "13899     0.016424\n",
       "13900    -0.020434\n",
       "13901    -0.005421\n",
       "\n",
       "[540 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32770d30",
   "metadata": {},
   "source": [
    "### Compare Actual vs Expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f54de396",
   "metadata": {},
   "outputs": [],
   "source": [
    "combination = pd.concat([test[[\"Player\", \"Share\"]], pred], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7237a893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Share</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>Nikola Jokić</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.154306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8624</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.162713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.142386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9907</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.207436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.072293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10997</th>\n",
       "      <td>Luka Dončić</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.151430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7464</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.116303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3536</th>\n",
       "      <td>Julius Randle</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.088877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>Derrick Rose</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.033001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11358</th>\n",
       "      <td>Rudy Gobert</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.095350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Player  Share  predictions\n",
       "641             Nikola Jokić  0.961     0.154306\n",
       "8624             Joel Embiid  0.580     0.162713\n",
       "3651           Stephen Curry  0.449     0.142386\n",
       "9907   Giannis Antetokounmpo  0.345     0.207436\n",
       "1389              Chris Paul  0.138     0.072293\n",
       "10997            Luka Dončić  0.042     0.151430\n",
       "7464          Damian Lillard  0.038     0.116303\n",
       "3536           Julius Randle  0.020     0.088877\n",
       "3531            Derrick Rose  0.010     0.033001\n",
       "11358            Rudy Gobert  0.008     0.095350"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combination.sort_values(\"Share\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee23d9d",
   "metadata": {},
   "source": [
    "### Decide on Error Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d4660e4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0026668960013828667"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(combination[\"Share\"], combination[\"predictions\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4a9dd99c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Share\n",
       "0.000    525\n",
       "0.001      3\n",
       "0.961      1\n",
       "0.138      1\n",
       "0.010      1\n",
       "0.020      1\n",
       "0.449      1\n",
       "0.005      1\n",
       "0.038      1\n",
       "0.003      1\n",
       "0.580      1\n",
       "0.345      1\n",
       "0.042      1\n",
       "0.008      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combination[\"Share\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75f966c",
   "metadata": {},
   "source": [
    "### Add Actual Rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "32cf7d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "combination = combination.sort_values(\"Share\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "62130794",
   "metadata": {},
   "outputs": [],
   "source": [
    "combination[\"Rk\"] = list(range(1, combination.shape[0]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d96e7c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Share</th>\n",
       "      <th>predictions</th>\n",
       "      <th>Rk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>Nikola Jokić</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.154306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8624</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.162713</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.142386</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9907</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.207436</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.072293</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10997</th>\n",
       "      <td>Luka Dončić</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.151430</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7464</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.116303</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3536</th>\n",
       "      <td>Julius Randle</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.088877</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>Derrick Rose</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.033001</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11358</th>\n",
       "      <td>Rudy Gobert</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.095350</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Player  Share  predictions  Rk\n",
       "641             Nikola Jokić  0.961     0.154306   1\n",
       "8624             Joel Embiid  0.580     0.162713   2\n",
       "3651           Stephen Curry  0.449     0.142386   3\n",
       "9907   Giannis Antetokounmpo  0.345     0.207436   4\n",
       "1389              Chris Paul  0.138     0.072293   5\n",
       "10997            Luka Dončić  0.042     0.151430   6\n",
       "7464          Damian Lillard  0.038     0.116303   7\n",
       "3536           Julius Randle  0.020     0.088877   8\n",
       "3531            Derrick Rose  0.010     0.033001   9\n",
       "11358            Rudy Gobert  0.008     0.095350  10"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combination.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67762c5b",
   "metadata": {},
   "source": [
    "### Add Prediccted Rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "88c33cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "combination = combination.sort_values(\"predictions\", ascending=False)\n",
    "combination[\"Predicted_Rk\"] = list(range(1, combination.shape[0]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c0bac6cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Share</th>\n",
       "      <th>predictions</th>\n",
       "      <th>Rk</th>\n",
       "      <th>Predicted_Rk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9907</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.207436</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8624</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.162713</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>Nikola Jokić</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.154306</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10997</th>\n",
       "      <td>Luka Dončić</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.151430</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3736</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.147512</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.142386</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4177</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.141350</td>\n",
       "      <td>531</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.140598</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11784</th>\n",
       "      <td>Zion Williamson</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.127926</td>\n",
       "      <td>251</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.120227</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Player  Share  predictions   Rk  Predicted_Rk\n",
       "9907   Giannis Antetokounmpo  0.345     0.207436    4             1\n",
       "8624             Joel Embiid  0.580     0.162713    2             2\n",
       "641             Nikola Jokić  0.961     0.154306    1             3\n",
       "10997            Luka Dončić  0.042     0.151430    6             4\n",
       "3736            LeBron James  0.001     0.147512   15             5\n",
       "3651           Stephen Curry  0.449     0.142386    3             6\n",
       "4177            Kevin Durant  0.000     0.141350  531             7\n",
       "4174            James Harden  0.001     0.140598   13             8\n",
       "11784        Zion Williamson  0.000     0.127926  251             9\n",
       "3876       Russell Westbrook  0.005     0.120227   11            10"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combination.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "18f164b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Share</th>\n",
       "      <th>predictions</th>\n",
       "      <th>Rk</th>\n",
       "      <th>Predicted_Rk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>Nikola Jokić</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.154306</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8624</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.162713</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.142386</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9907</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.207436</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.072293</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10997</th>\n",
       "      <td>Luka Dončić</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.151430</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7464</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.116303</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3536</th>\n",
       "      <td>Julius Randle</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.088877</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>Derrick Rose</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.033001</td>\n",
       "      <td>9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11358</th>\n",
       "      <td>Rudy Gobert</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.095350</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Player  Share  predictions  Rk  Predicted_Rk\n",
       "641             Nikola Jokić  0.961     0.154306   1             3\n",
       "8624             Joel Embiid  0.580     0.162713   2             2\n",
       "3651           Stephen Curry  0.449     0.142386   3             6\n",
       "9907   Giannis Antetokounmpo  0.345     0.207436   4             1\n",
       "1389              Chris Paul  0.138     0.072293   5            33\n",
       "10997            Luka Dončić  0.042     0.151430   6             4\n",
       "7464          Damian Lillard  0.038     0.116303   7            12\n",
       "3536           Julius Randle  0.020     0.088877   8            24\n",
       "3531            Derrick Rose  0.010     0.033001   9            76\n",
       "11358            Rudy Gobert  0.008     0.095350  10            19"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combination.sort_values(\"Share\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c32dd0",
   "metadata": {},
   "source": [
    "### Average Precision Error Metrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b076c5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_ap(combination):\n",
    "    actual = combination.sort_values(\"Share\", ascending=False).head(5)\n",
    "    predicted = combination.sort_values(\"predictions\", ascending=False)\n",
    "    ps = []\n",
    "    found = 0\n",
    "    seen = 1\n",
    "    for index, row in predicted.iterrows():\n",
    "        if row[\"Player\"] in actual[\"Player\"].values:\n",
    "            found += 1\n",
    "            ps.append(found/seen)\n",
    "        seen += 1\n",
    "    return sum(ps) / len(ps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "59a3542b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7636363636363636"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_ap(combination)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d0c1b0",
   "metadata": {},
   "source": [
    "### Backtest data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "82e3c52d",
   "metadata": {},
   "outputs": [],
   "source": [
    "years = list((range(1991, 2022)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "37ed776e",
   "metadata": {},
   "outputs": [],
   "source": [
    "aps = []\n",
    "all_predictions = []\n",
    "for year in years[5:]:\n",
    "    train = stats[stats[\"Year\"] < year]\n",
    "    test = stats[stats[\"Year\"] == year]\n",
    "    reg.fit(train[predictors], train[\"Share\"])\n",
    "    predictions = reg.predict(test[predictors])\n",
    "    predictions = pd.DataFrame(predictions, columns=[\"predictions\"], index=test.index)\n",
    "    combination = pd.concat([test[[\"Player\", \"Share\"]], predictions], axis=1)\n",
    "    all_predictions.append(combination)\n",
    "    aps.append(find_ap(combination))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "22f11280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7112884360789578"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(aps) / len(aps)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4234a84",
   "metadata": {},
   "source": [
    "### Add actual, predicted and difference between ranks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "969b6ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_ranks(combination):\n",
    "    combination = combination.sort_values(\"Share\", ascending=False)\n",
    "    combination[\"Rk\"] = list(range(1, combination.shape[0]+1))\n",
    "    combination = combination.sort_values(\"predictions\", ascending=False)\n",
    "    combination[\"Predicted_Rk\"] = list(range(1, combination.shape[0]+1))\n",
    "    combination[\"Diff\"] = combination[\"Rk\"] - combination[\"Predicted_Rk\"]\n",
    "    return combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0c9e589b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Share</th>\n",
       "      <th>predictions</th>\n",
       "      <th>Rk</th>\n",
       "      <th>Predicted_Rk</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>Karl Malone</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.192318</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10524</th>\n",
       "      <td>Michael Jordan</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.167629</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>Grant Hill</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.128646</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4682</th>\n",
       "      <td>Tim Hardaway</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.059984</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8248</th>\n",
       "      <td>Glen Rice</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.033110</td>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>-48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Player  Share  predictions  Rk  Predicted_Rk  Diff\n",
       "1600      Karl Malone  0.857     0.192318   1             2    -1\n",
       "10524  Michael Jordan  0.832     0.167629   2             3    -1\n",
       "908        Grant Hill  0.327     0.128646   3             6    -3\n",
       "4682     Tim Hardaway  0.207     0.059984   4            20   -16\n",
       "8248        Glen Rice  0.117     0.033110   5            53   -48"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranking = add_ranks(all_predictions[1])\n",
    "ranking[ranking[\"Rk\"] < 6].sort_values(\"Diff\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f7c9dec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtest(stats, model, year, predictors):\n",
    "    aps = []\n",
    "    all_predictions = []\n",
    "    for year in years[5:]:\n",
    "        train = stats[stats[\"Year\"] < year]\n",
    "        test = stats[stats[\"Year\"] == year]\n",
    "        model.fit(train[predictors], train[\"Share\"])\n",
    "        predictions = reg.predict(test[predictors])\n",
    "        predictions = pd.DataFrame(predictions, columns=[\"predictions\"], index=test.index)\n",
    "        combination = pd.concat([test[[\"Player\", \"Share\"]], predictions], axis=1)\n",
    "        combination = add_ranks(combination)\n",
    "        all_predictions.append(combination)\n",
    "        aps.append(find_ap(combination))\n",
    "    return sum(aps)/len(aps), aps, pd.concat(all_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e66fc4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ap, aps, all_predictions = backtest(stats, reg, years[5:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7a8e092b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7112884360789578"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01660598",
   "metadata": {},
   "source": [
    "### Diagnose model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4a2a6607",
   "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>Player</th>\n",
       "      <th>Share</th>\n",
       "      <th>predictions</th>\n",
       "      <th>Rk</th>\n",
       "      <th>Predicted_Rk</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>Jason Kidd</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.028210</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8248</th>\n",
       "      <td>Glen Rice</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.033110</td>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>-48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5175</th>\n",
       "      <td>Steve Nash</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.034100</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8516</th>\n",
       "      <td>Peja Stojaković</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.036270</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5193</th>\n",
       "      <td>Steve Nash</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.054129</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12726</th>\n",
       "      <td>Joakim Noah</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.046968</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3657</th>\n",
       "      <td>Chauncey Billups</td>\n",
       "      <td>0.344</td>\n",
       "      <td>0.052696</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.072293</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5208</th>\n",
       "      <td>Steve Nash</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.074421</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4682</th>\n",
       "      <td>Tim Hardaway</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.059984</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Player  Share  predictions  Rk  Predicted_Rk  Diff\n",
       "1224         Jason Kidd  0.712     0.028210   2            52   -50\n",
       "8248          Glen Rice  0.117     0.033110   5            53   -48\n",
       "5175         Steve Nash  0.839     0.034100   1            45   -44\n",
       "8516    Peja Stojaković  0.228     0.036270   4            38   -34\n",
       "5193         Steve Nash  0.739     0.054129   1            34   -33\n",
       "12726       Joakim Noah  0.258     0.046968   4            37   -33\n",
       "3657   Chauncey Billups  0.344     0.052696   5            35   -30\n",
       "1389         Chris Paul  0.138     0.072293   5            33   -28\n",
       "5208         Steve Nash  0.785     0.074421   2            21   -19\n",
       "4682       Tim Hardaway  0.207     0.059984   4            20   -16"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions[all_predictions[\"Rk\"] <= 5].sort_values(\"Diff\").head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cff4353d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.070001</td>\n",
       "      <td>eFG%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.035041</td>\n",
       "      <td>DRB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.027125</td>\n",
       "      <td>W/L%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.021610</td>\n",
       "      <td>ORB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.016945</td>\n",
       "      <td>2P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.011635</td>\n",
       "      <td>STL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.011351</td>\n",
       "      <td>FTA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.011234</td>\n",
       "      <td>BLK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.007455</td>\n",
       "      <td>AST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.005894</td>\n",
       "      <td>PTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005694</td>\n",
       "      <td>FG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.005517</td>\n",
       "      <td>FGA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.004569</td>\n",
       "      <td>3P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.004083</td>\n",
       "      <td>2P%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000321</td>\n",
       "      <td>Age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.000270</td>\n",
       "      <td>GB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.000098</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000092</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.000006</td>\n",
       "      <td>GS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.000184</td>\n",
       "      <td>Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.000219</td>\n",
       "      <td>PA/G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.000295</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.000521</td>\n",
       "      <td>SRS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.000556</td>\n",
       "      <td>PS/G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.002635</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.004150</td>\n",
       "      <td>MP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.004575</td>\n",
       "      <td>FT%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.006586</td>\n",
       "      <td>FT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.009696</td>\n",
       "      <td>TOV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.009822</td>\n",
       "      <td>3P%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.010395</td>\n",
       "      <td>3PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.017319</td>\n",
       "      <td>2PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.027506</td>\n",
       "      <td>TRB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.135024</td>\n",
       "      <td>FG%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0     1\n",
       "13  0.070001  eFG%\n",
       "18  0.035041   DRB\n",
       "29  0.027125  W/L%\n",
       "17  0.021610   ORB\n",
       "10  0.016945    2P\n",
       "21  0.011635   STL\n",
       "15  0.011351   FTA\n",
       "22  0.011234   BLK\n",
       "20  0.007455   AST\n",
       "25  0.005894   PTS\n",
       "4   0.005694    FG\n",
       "5   0.005517   FGA\n",
       "7   0.004569    3P\n",
       "12  0.004083   2P%\n",
       "0   0.000321   Age\n",
       "30  0.000270    GB\n",
       "27  0.000098     W\n",
       "1   0.000092     G\n",
       "2  -0.000006    GS\n",
       "26 -0.000184  Year\n",
       "32 -0.000219  PA/G\n",
       "28 -0.000295     L\n",
       "33 -0.000521   SRS\n",
       "31 -0.000556  PS/G\n",
       "24 -0.002635    PF\n",
       "3  -0.004150    MP\n",
       "16 -0.004575   FT%\n",
       "14 -0.006586    FT\n",
       "23 -0.009696   TOV\n",
       "9  -0.009822   3P%\n",
       "8  -0.010395   3PA\n",
       "11 -0.017319   2PA\n",
       "19 -0.027506   TRB\n",
       "6  -0.135024   FG%"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([pd.Series(reg.coef_), pd.Series(predictors)], axis=1).sort_values(0, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2b3660bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "stat_ratios = stats[[\"PTS\", \"AST\", \"STL\", \"BLK\", \"3P\", \"Year\"]].groupby(\"Year\", as_index=False).apply(lambda x: x/x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cb474dd6",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PTS</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>3P</th>\n",
       "      <th>Year</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>1.013334</td>\n",
       "      <td>0.420714</td>\n",
       "      <td>0.961127</td>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.508587</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.614653</td>\n",
       "      <td>1.028412</td>\n",
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       "      <td>0.673469</td>\n",
       "      <td>4.577279</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.311795</td>\n",
       "      <td>0.093492</td>\n",
       "      <td>0.274608</td>\n",
       "      <td>1.571429</td>\n",
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       "    <tr>\n",
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       "      <td>0.186984</td>\n",
       "      <td>0.274608</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>4</th>\n",
       "      <td>2.383005</td>\n",
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       "      <td>1.784950</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>1.525760</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th rowspan=\"5\" valign=\"top\">30</th>\n",
       "      <th>13897</th>\n",
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       "      <td>0.971223</td>\n",
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       "      <td>0.647482</td>\n",
       "      <td>0.240964</td>\n",
       "      <td>0.099741</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>13899</th>\n",
       "      <td>1.274824</td>\n",
       "      <td>0.704370</td>\n",
       "      <td>1.133094</td>\n",
       "      <td>1.445783</td>\n",
       "      <td>1.695604</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>13900</th>\n",
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       "      <td>0.553433</td>\n",
       "      <td>0.323741</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099741</td>\n",
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       "    <tr>\n",
       "      <th>13901</th>\n",
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       "      <td>0.301873</td>\n",
       "      <td>0.485612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.598448</td>\n",
       "      <td>1.0</td>\n",
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       "<p>14092 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               PTS       AST       STL       BLK        3P  Year\n",
       "0  0      1.013334  0.420714  0.961127  0.673469  0.508587   1.0\n",
       "   1      1.614653  1.028412  1.647646  0.673469  4.577279   1.0\n",
       "   2      0.311795  0.093492  0.274608  1.571429  0.000000   1.0\n",
       "   3      0.200440  0.186984  0.274608  0.000000  0.000000   1.0\n",
       "   4      2.383005  1.636110  1.784950  0.897959  1.525760   1.0\n",
       "...            ...       ...       ...       ...       ...   ...\n",
       "30 13897  1.207728  1.207491  0.971223  0.000000  2.393794   1.0\n",
       "   13898  0.257201  0.201248  0.647482  0.240964  0.099741   1.0\n",
       "   13899  1.274824  0.704370  1.133094  1.445783  1.695604   1.0\n",
       "   13900  0.279567  0.553433  0.323741  0.000000  0.099741   1.0\n",
       "   13901  0.559134  0.301873  0.485612  0.000000  0.598448   1.0\n",
       "\n",
       "[14092 rows x 6 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1d033bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats[[\"PTS_R\", \"AST_R\", \"STL_R\", \"BLK_R\", \"3P_R\"]] = stat_ratios[[\"PTS\", \"AST\", \"STL\", \"BLK\", \"3P\"]].reset_index(level=0, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9a2eadda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tm</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>...</th>\n",
       "      <th>W/L%</th>\n",
       "      <th>GB</th>\n",
       "      <th>PS/G</th>\n",
       "      <th>PA/G</th>\n",
       "      <th>SRS</th>\n",
       "      <th>PTS_R</th>\n",
       "      <th>AST_R</th>\n",
       "      <th>STL_R</th>\n",
       "      <th>BLK_R</th>\n",
       "      <th>3P_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A.C. Green</td>\n",
       "      <td>PF</td>\n",
       "      <td>27</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82</td>\n",
       "      <td>21</td>\n",
       "      <td>26.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.707</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>6.73</td>\n",
       "      <td>1.013334</td>\n",
       "      <td>0.420714</td>\n",
       "      <td>0.961127</td>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.508587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Byron Scott</td>\n",
       "      <td>SG</td>\n",
       "      <td>29</td>\n",
       "      <td>LAL</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>32.1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0.477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.707</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>6.73</td>\n",
       "      <td>1.614653</td>\n",
       "      <td>1.028412</td>\n",
       "      <td>1.647646</td>\n",
       "      <td>0.673469</td>\n",
       "      <td>4.577279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Elden Campbell</td>\n",
       "      <td>PF</td>\n",
       "      <td>22</td>\n",
       "      <td>LAL</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.455</td>\n",
       "      <td>...</td>\n",
       "      <td>0.707</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>6.73</td>\n",
       "      <td>0.311795</td>\n",
       "      <td>0.093492</td>\n",
       "      <td>0.274608</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Irving Thomas</td>\n",
       "      <td>PF</td>\n",
       "      <td>25</td>\n",
       "      <td>LAL</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.340</td>\n",
       "      <td>...</td>\n",
       "      <td>0.707</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>6.73</td>\n",
       "      <td>0.200440</td>\n",
       "      <td>0.186984</td>\n",
       "      <td>0.274608</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James Worthy</td>\n",
       "      <td>SF</td>\n",
       "      <td>29</td>\n",
       "      <td>LAL</td>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>38.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>18.7</td>\n",
       "      <td>0.492</td>\n",
       "      <td>...</td>\n",
       "      <td>0.707</td>\n",
       "      <td>5.0</td>\n",
       "      <td>106.3</td>\n",
       "      <td>99.6</td>\n",
       "      <td>6.73</td>\n",
       "      <td>2.383005</td>\n",
       "      <td>1.636110</td>\n",
       "      <td>1.784950</td>\n",
       "      <td>0.897959</td>\n",
       "      <td>1.525760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Player Pos  Age   Tm   G  GS    MP   FG   FGA    FG%  ...   W/L%   \n",
       "0      A.C. Green  PF   27  LAL  82  21  26.4  3.1   6.6  0.476  ...  0.707  \\\n",
       "1     Byron Scott  SG   29  LAL  82  82  32.1  6.1  12.8  0.477  ...  0.707   \n",
       "2  Elden Campbell  PF   22  LAL  52   0   7.3  1.1   2.4  0.455  ...  0.707   \n",
       "3   Irving Thomas  PF   25  LAL  26   0   4.2  0.7   1.9  0.340  ...  0.707   \n",
       "4    James Worthy  SF   29  LAL  78  74  38.6  9.2  18.7  0.492  ...  0.707   \n",
       "\n",
       "    GB   PS/G  PA/G   SRS     PTS_R     AST_R     STL_R     BLK_R      3P_R  \n",
       "0  5.0  106.3  99.6  6.73  1.013334  0.420714  0.961127  0.673469  0.508587  \n",
       "1  5.0  106.3  99.6  6.73  1.614653  1.028412  1.647646  0.673469  4.577279  \n",
       "2  5.0  106.3  99.6  6.73  0.311795  0.093492  0.274608  1.571429  0.000000  \n",
       "3  5.0  106.3  99.6  6.73  0.200440  0.186984  0.274608  0.000000  0.000000  \n",
       "4  5.0  106.3  99.6  6.73  2.383005  1.636110  1.784950  0.897959  1.525760  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2539c5e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors += [\"PTS_R\", \"AST_R\", \"STL_R\", \"BLK_R\", \"3P_R\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2f99e71f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ap, aps, all_predictions = backtest(stats, reg, years[5:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9273c6b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7208380973034985"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "96aeb636",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats[\"NPos\"] = stats[\"Pos\"].astype(\"category\").cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3f2e6f78",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats[\"NTm\"] = stats[\"Tm\"].astype(\"category\").cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6648d412",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor(n_estimators=500, random_state=1, min_samples_split=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "01fa396f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ap, aps, all_predictions = backtest(stats, rf, years[5:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cc5073c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7456311092998757"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b3a5c19",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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