mvp/machine_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f3d07065-beeb-451a-b964-65f2e0e1899c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cd2b0693-88c0-42d0-b81e-28c93019b738",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = pd.read_csv(\"player_mvp_stats.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a5c19e02-0ad5-49ad-919b-fae7211cb666",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>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>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>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>0.0</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>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>0.0</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>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>0.0</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>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>0.0</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>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>0.0</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",
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       "      <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>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>0.484</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>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>0.286</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>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>0.470</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>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>0.459</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>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>0.455</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 × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Player Pos  Age   Tm   G  GS    MP   FG   FGA    FG%  ...  \\\n",
       "0          A.C. Green  PF   27  LAL  82  21  26.4  3.1   6.6  0.476  ...   \n",
       "1         Byron Scott  SG   29  LAL  82  82  32.1  6.1  12.8  0.477  ...   \n",
       "2      Elden Campbell  PF   22  LAL  52   0   7.3  1.1   2.4  0.455  ...   \n",
       "3       Irving Thomas  PF   25  LAL  26   0   4.2  0.7   1.9  0.340  ...   \n",
       "4        James Worthy  SF   29  LAL  78  74  38.6  9.2  18.7  0.492  ...   \n",
       "...               ...  ..  ...  ...  ..  ..   ...  ...   ...    ...  ...   \n",
       "14087   Spencer Hawes  PF   28  MIL  54   1  14.8  2.5   5.1  0.484  ...   \n",
       "14088     Steve Novak  PF   33  MIL   8   0   2.8  0.3   0.9  0.286  ...   \n",
       "14089  Terrence Jones  PF   25  MIL  54  12  23.5  4.3   9.1  0.470  ...   \n",
       "14090      Thon Maker   C   19  MIL  57  34   9.9  1.5   3.2  0.459  ...   \n",
       "14091      Tony Snell  SG   25  MIL  80  80  29.2  3.1   6.8  0.455  ...   \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 41 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "61186247-5be8-4af1-b6e4-0a48c162d507",
   "metadata": {},
   "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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(stats).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8eaf9675-ae4d-49f3-9a64-369fb5311d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[pd.isnull(stats[\"3P%\"])][[\"Player\", \"3PA\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8efffab5-6355-45c5-b47e-08a90fed81df",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>FTA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>John Coker</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Jason Sasser</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Adrian Caldwell</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Bruno Šundov</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Jamal Robinson</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Player  FTA\n",
       "77        John Coker  0.0\n",
       "92      Jason Sasser  0.0\n",
       "103  Adrian Caldwell  0.0\n",
       "119     Bruno Šundov  0.0\n",
       "158   Jamal Robinson  0.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[pd.isnull(stats[\"FT%\"])][[\"Player\", \"FTA\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9778f0c1-3155-49ca-8719-a2619e75a2cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = stats.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "557722cc-0320-4e01-97ab-ca8dc84cc556",
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a464d398-c2b0-4bf8-a0c1-a651c6486875",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = [\"Age\", \"G\", \"GS\", \"MP\", \"FG\", \"FGA\", 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%', 'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS', 'W', 'L', 'W/L%',\n",
    "       'GB', 'PS/G', 'PA/G', 'SRS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c0bc6e22-a948-486d-b8b2-f87b81c49a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = stats[~(stats[\"Year\"] == 2021)]\n",
    "test = stats[stats[\"Year\"] == 2021]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3f616579-b877-4d41-a3a0-7c58fb24a7cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "reg = Ridge(alpha=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "187a19a9-184a-460a-a85c-90baaf42f980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.1)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(train[predictors],train[\"Share\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3e2b4b9e-1585-43a8-969b-4ea334a7f7c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = reg.predict(test[predictors])\n",
    "predictions = pd.DataFrame(predictions, columns=[\"predictions\"], index=test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "74cc62c2-effa-43af-9300-4ef5fc0e9e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "combination = pd.concat([test[[\"Player\", \"Share\"]], predictions], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "53e0cc23-8aab-4892-a671-84cbdde10c03",
   "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>0.155155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8624</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.163637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.142490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9907</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.206428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.073984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10997</th>\n",
       "      <td>Luka Dončić</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.150260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7464</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.116646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3536</th>\n",
       "      <td>Julius Randle</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.089415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>Derrick Rose</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.035417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11358</th>\n",
       "      <td>Rudy Gobert</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.095294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.120053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8616</th>\n",
       "      <td>Ben Simmons</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.064743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.139975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5328</th>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.114312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3736</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.147459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9909</th>\n",
       "      <td>Jeff Teague</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.007576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10672</th>\n",
       "      <td>Jalen McDaniels</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.010576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10671</th>\n",
       "      <td>Grant Riller</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.004480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10670</th>\n",
       "      <td>Gordon Hayward</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.041892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10669</th>\n",
       "      <td>Devonte' Graham</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.015287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Player  Share  predictions\n",
       "641             Nikola Jokić  0.961     0.155155\n",
       "8624             Joel Embiid  0.580     0.163637\n",
       "3651           Stephen Curry  0.449     0.142490\n",
       "9907   Giannis Antetokounmpo  0.345     0.206428\n",
       "1389              Chris Paul  0.138     0.073984\n",
       "10997            Luka Dončić  0.042     0.150260\n",
       "7464          Damian Lillard  0.038     0.116646\n",
       "3536           Julius Randle  0.020     0.089415\n",
       "3531            Derrick Rose  0.010     0.035417\n",
       "11358            Rudy Gobert  0.008     0.095294\n",
       "3876       Russell Westbrook  0.005     0.120053\n",
       "8616             Ben Simmons  0.003     0.064743\n",
       "4174            James Harden  0.001     0.139975\n",
       "5328           Kawhi Leonard  0.001     0.114312\n",
       "3736            LeBron James  0.001     0.147459\n",
       "9909             Jeff Teague  0.000    -0.007576\n",
       "10672        Jalen McDaniels  0.000    -0.010576\n",
       "10671           Grant Riller  0.000    -0.004480\n",
       "10670         Gordon Hayward  0.000     0.041892\n",
       "10669        Devonte' Graham  0.000     0.015287"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combination.sort_values(\"Share\", ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8fbed07a-ac77-4b40-af01-cdc0aa1b9ea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0026666831687637144"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mean_squared_error(combination[\"Share\"], combination[\"predictions\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "18b887f0-210e-4d85-90bd-53f9943cc7cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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: Share, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combination[\"Share\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ead1281b-80ef-4c3a-8473-e1d9cbcb7fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "actual = combination.sort_values(\"Share\", ascending=False)\n",
    "predicted = combination.sort_values(\"predictions\", ascending=False)\n",
    "actual[\"Rk\"] = list(range(1,actual.shape[0]+1))\n",
    "predicted[\"Predicted_Rk\"] = list(range(1,predicted.shape[0]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8301b824-92fb-416e-8648-5ccdbe810e6a",
   "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_x</th>\n",
       "      <th>predictions_x</th>\n",
       "      <th>Rk</th>\n",
       "      <th>Share_y</th>\n",
       "      <th>predictions_y</th>\n",
       "      <th>Predicted_Rk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nikola Jokić</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.155155</td>\n",
       "      <td>1</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.155155</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.163637</td>\n",
       "      <td>2</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.163637</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.142490</td>\n",
       "      <td>3</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.142490</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.206428</td>\n",
       "      <td>4</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.206428</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.073984</td>\n",
       "      <td>5</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.073984</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Player  Share_x  predictions_x  Rk  Share_y  predictions_y  \\\n",
       "0           Nikola Jokić    0.961       0.155155   1    0.961       0.155155   \n",
       "1            Joel Embiid    0.580       0.163637   2    0.580       0.163637   \n",
       "2          Stephen Curry    0.449       0.142490   3    0.449       0.142490   \n",
       "3  Giannis Antetokounmpo    0.345       0.206428   4    0.345       0.206428   \n",
       "4             Chris Paul    0.138       0.073984   5    0.138       0.073984   \n",
       "\n",
       "   Predicted_Rk  \n",
       "0             3  \n",
       "1             2  \n",
       "2             6  \n",
       "3             1  \n",
       "4            33  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual.merge(predicted, on=\"Player\").head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4683b4f6-c46e-4408-9e80-3fab4a120c0b",
   "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",
    "\n",
    "    return sum(ps) / len(ps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b278290e-ace4-490d-b862-f83d36b2bbb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ap = find_ap(combination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "45d3c67b-1a79-4737-b2e3-60130a1a696c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7636363636363636"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8ffb3a4f-4308-4451-a15e-462ab6d78a1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "years = list(range(1991,2022))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "21a004ae-3c10-4611-b3f7-e72c70119d28",
   "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": 26,
   "id": "56b66938-9359-4354-a950-ab869e384ec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7110668523458931"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(aps) / len(aps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "302913d2-332e-4a5e-b42a-fc4662110721",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_ranks(predictions):\n",
    "    predictions = predictions.sort_values(\"predictions\", ascending=False)\n",
    "    predictions[\"Predicted_Rk\"] = list(range(1,predictions.shape[0]+1))\n",
    "    predictions = predictions.sort_values(\"Share\", ascending=False)\n",
    "    predictions[\"Rk\"] = list(range(1,predictions.shape[0]+1))\n",
    "    predictions[\"Diff\"] = (predictions[\"Rk\"] - predictions[\"Predicted_Rk\"])\n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "91b3e6f9-b14c-45be-9d49-b06cdd66c45b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Share</th>\n",
       "      <th>predictions</th>\n",
       "      <th>Predicted_Rk</th>\n",
       "      <th>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>0.192360</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>0.167672</td>\n",
       "      <td>3</td>\n",
       "      <td>2</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>0.128664</td>\n",
       "      <td>6</td>\n",
       "      <td>3</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>0.059992</td>\n",
       "      <td>20</td>\n",
       "      <td>4</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>0.033122</td>\n",
       "      <td>53</td>\n",
       "      <td>5</td>\n",
       "      <td>-48</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10136</th>\n",
       "      <td>Horacio Llamas</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010225</td>\n",
       "      <td>156</td>\n",
       "      <td>437</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3576</th>\n",
       "      <td>Ennis Whatley</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010287</td>\n",
       "      <td>155</td>\n",
       "      <td>438</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10594</th>\n",
       "      <td>Kevin Salvadori</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010594</td>\n",
       "      <td>154</td>\n",
       "      <td>439</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>Aaron Williams</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010608</td>\n",
       "      <td>153</td>\n",
       "      <td>440</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6792</th>\n",
       "      <td>Melvin Booker</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.055141</td>\n",
       "      <td>441</td>\n",
       "      <td>441</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>441 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Player  Share  predictions  Predicted_Rk   Rk  Diff\n",
       "1600       Karl Malone  0.857     0.192360             2    1    -1\n",
       "10524   Michael Jordan  0.832     0.167672             3    2    -1\n",
       "908         Grant Hill  0.327     0.128664             6    3    -3\n",
       "4682      Tim Hardaway  0.207     0.059992            20    4   -16\n",
       "8248         Glen Rice  0.117     0.033122            53    5   -48\n",
       "...                ...    ...          ...           ...  ...   ...\n",
       "10136   Horacio Llamas  0.000     0.010225           156  437   281\n",
       "3576     Ennis Whatley  0.000     0.010287           155  438   283\n",
       "10594  Kevin Salvadori  0.000     0.010594           154  439   285\n",
       "1138    Aaron Williams  0.000     0.010608           153  440   287\n",
       "6792     Melvin Booker  0.000    -0.055141           441  441     0\n",
       "\n",
       "[441 rows x 6 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_ranks(all_predictions[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a79182aa-d8d6-4bc5-8aff-9b7e3aab5c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtest(stats, model, years, predictors):\n",
    "    aps = []\n",
    "    all_predictions = []\n",
    "    for year in years:\n",
    "        train = stats[stats[\"Year\"] < year]\n",
    "        test = stats[stats[\"Year\"] == year]\n",
    "        model.fit(train[predictors],train[\"Share\"])\n",
    "        predictions = model.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": 30,
   "id": "5748eaf4-98e7-4e84-8068-8193a9344321",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ap, aps, all_predictions = backtest(stats, reg, years[5:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a9bd3b8b-a7c6-4dd1-9239-762e06c6d2a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7110668523458931"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8e956bed-038f-4911-8b6c-146107d59a45",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Share</th>\n",
       "      <th>predictions</th>\n",
       "      <th>Predicted_Rk</th>\n",
       "      <th>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>0.027956</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5175</th>\n",
       "      <td>Steve Nash</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.032686</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>-46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8516</th>\n",
       "      <td>Peja Stojaković</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.035581</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5193</th>\n",
       "      <td>Steve Nash</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.051337</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12726</th>\n",
       "      <td>Joakim Noah</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.047390</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5208</th>\n",
       "      <td>Steve Nash</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.070790</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4682</th>\n",
       "      <td>Tim Hardaway</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.059992</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Allen Iverson</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.071899</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6746</th>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.077829</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1157</th>\n",
       "      <td>Gary Payton</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.076145</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Player  Share  predictions  Predicted_Rk  Rk  Diff\n",
       "1224        Jason Kidd  0.712     0.027956            52   2   -50\n",
       "5175        Steve Nash  0.839     0.032686            47   1   -46\n",
       "8516   Peja Stojaković  0.228     0.035581            39   4   -35\n",
       "5193        Steve Nash  0.739     0.051337            35   1   -34\n",
       "12726      Joakim Noah  0.258     0.047390            37   4   -33\n",
       "5208        Steve Nash  0.785     0.070790            23   2   -21\n",
       "4682      Tim Hardaway  0.207     0.059992            20   4   -16\n",
       "937      Allen Iverson  0.270     0.071899            14   4   -10\n",
       "6746       Kobe Bryant  0.291     0.077829            14   4   -10\n",
       "1157       Gary Payton  0.372     0.076145            13   3   -10"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions[all_predictions[\"Rk\"] < 5].sort_values(\"Diff\").head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a53cb239-ed18-407c-8b30-217f6b3e20cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.065312</td>\n",
       "      <td>eFG%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.034362</td>\n",
       "      <td>DRB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.028501</td>\n",
       "      <td>W/L%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.021790</td>\n",
       "      <td>ORB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.015914</td>\n",
       "      <td>2P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.012054</td>\n",
       "      <td>STL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.011474</td>\n",
       "      <td>FTA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.011175</td>\n",
       "      <td>BLK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.007435</td>\n",
       "      <td>AST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.006140</td>\n",
       "      <td>PTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005893</td>\n",
       "      <td>FG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.005526</td>\n",
       "      <td>FGA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.004922</td>\n",
       "      <td>3P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.003043</td>\n",
       "      <td>2P%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000361</td>\n",
       "      <td>Age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.000314</td>\n",
       "      <td>GB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000114</td>\n",
       "      <td>W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000096</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.000005</td>\n",
       "      <td>GS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.000308</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.000326</td>\n",
       "      <td>PA/G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-0.000500</td>\n",
       "      <td>PS/G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.000609</td>\n",
       "      <td>SRS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.002366</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.004164</td>\n",
       "      <td>MP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.004287</td>\n",
       "      <td>FT%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.006911</td>\n",
       "      <td>FT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.009442</td>\n",
       "      <td>TOV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.009815</td>\n",
       "      <td>3P%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.011057</td>\n",
       "      <td>3PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.017115</td>\n",
       "      <td>2PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.027270</td>\n",
       "      <td>TRB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.130269</td>\n",
       "      <td>FG%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0     1\n",
       "13  0.065312  eFG%\n",
       "18  0.034362   DRB\n",
       "28  0.028501  W/L%\n",
       "17  0.021790   ORB\n",
       "10  0.015914    2P\n",
       "21  0.012054   STL\n",
       "15  0.011474   FTA\n",
       "22  0.011175   BLK\n",
       "20  0.007435   AST\n",
       "25  0.006140   PTS\n",
       "4   0.005893    FG\n",
       "5   0.005526   FGA\n",
       "7   0.004922    3P\n",
       "12  0.003043   2P%\n",
       "0   0.000361   Age\n",
       "29  0.000314    GB\n",
       "26  0.000114     W\n",
       "1   0.000096     G\n",
       "2  -0.000005    GS\n",
       "27 -0.000308     L\n",
       "31 -0.000326  PA/G\n",
       "30 -0.000500  PS/G\n",
       "32 -0.000609   SRS\n",
       "24 -0.002366    PF\n",
       "3  -0.004164    MP\n",
       "16 -0.004287   FT%\n",
       "14 -0.006911    FT\n",
       "23 -0.009442   TOV\n",
       "9  -0.009815   3P%\n",
       "8  -0.011057   3PA\n",
       "11 -0.017115   2PA\n",
       "19 -0.027270   TRB\n",
       "6  -0.130269   FG%"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "id": "124f71ed-204a-4934-ac67-500b941c8754",
   "metadata": {},
   "outputs": [],
   "source": [
    "stat_ratios = stats[[\"PTS\", \"AST\", \"STL\", \"BLK\", \"3P\", \"Year\"]].groupby(\"Year\").apply(lambda x: x/x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c709fe1b-f2cd-4cf9-bc8c-a1df8ac34cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats[[\"PTS_R\", \"AST_R\", \"STL_R\", \"BLK_R\", \"3P_R\"]] = stat_ratios[[\"PTS\", \"AST\", \"STL\", \"BLK\", \"3P\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d7f99f1c-67ef-4ae2-95fc-548fe264bc06",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors += [\"PTS_R\", \"AST_R\", \"STL_R\", \"BLK_R\", \"3P_R\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "83de9ead-3c61-4789-a896-bf5ed39451b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ap, aps, all_predictions = backtest(stats, reg, years[5:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e7be9af2-78c8-4a11-815d-9c87a05136c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7180143406004313"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f50d7d6a-28e1-4735-814f-1991eb57ffbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats[\"NPos\"] = stats[\"Pos\"].astype(\"category\").cat.codes\n",
    "stats[\"NTm\"] = stats[\"Tm\"].astype(\"category\").cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fb63efb0-bcb5-4a32-ade0-0cf914e5fe60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=50, random_state=1, min_samples_split=5)\n",
    "\n",
    "mean_ap, aps, all_predictions = backtest(stats, rf, years[28:], predictors + [\"NPos\", \"NTm\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a6de9545-35c7-462b-be0f-2477cc57e7f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8544707580001697"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8b87822a-3711-4a67-afcf-f547fe31312b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ap, aps, all_predictions = backtest(stats, reg, years[28:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "90075aae-8843-443c-9451-a2bc8538e4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7981818181818182"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1466e897-2b5f-4784-be5f-f325c3fa5d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e9fdb714-2808-4a00-b727-b76b61f31e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtest(stats, model, years, predictors):\n",
    "    aps = []\n",
    "    all_predictions = []\n",
    "    for year in years:\n",
    "        train = stats[stats[\"Year\"] < year].copy()\n",
    "        test = stats[stats[\"Year\"] == year].copy()\n",
    "        sc.fit(train[predictors])\n",
    "        train[predictors] = sc.transform(train[predictors])\n",
    "        test[predictors] = sc.transform(test[predictors])\n",
    "        model.fit(train[predictors],train[\"Share\"])\n",
    "        predictions = model.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": 53,
   "id": "a895d0ee-fafd-4c09-a4b5-e8eb8ff99d37",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ap, aps, all_predictions = backtest(stats, reg, years[28:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3ffcb30e-00c7-44a2-971f-342125de3814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7981818181818182"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2827d113-401a-42e9-a7d3-a996e0b86cfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.04758869,  1.15207811, -0.1657698 , ..., -0.05582165,\n",
       "        -0.27719578, -0.38154061],\n",
       "       [ 0.51919526,  1.15207811,  1.91917626, ...,  0.93001944,\n",
       "        -0.27719578,  2.77745153],\n",
       "       [-1.13142771, -0.03932364, -0.88353811, ..., -1.04166274,\n",
       "         0.48509261, -0.77641463],\n",
       "       ...,\n",
       "       [-0.42401787,  0.04010314, -0.47338479, ...,  0.17152606,\n",
       "         1.31341113, -0.37208834],\n",
       "       [-1.83883755,  0.15924332,  0.27856297, ..., -0.97670625,\n",
       "         0.23224953, -0.27100677],\n",
       "       [-0.42401787,  1.07265133,  1.85081738, ...,  0.17152606,\n",
       "        -0.41644743,  1.04305367]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.transform(stats[predictors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ba1958f-3d30-4971-9191-ce57a9351694",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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