machine-learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = pd.read_csv(\"player_mvp_stats.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>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",
       "      <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>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": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(stats).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
       "  </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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[pd.isnull(stats[\"3P%\"])][[\"Player\", \"3PA\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[pd.isnull(stats[\"FT%\"])][[\"Player\", \"FTA\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = stats.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = stats[~(stats[\"Year\"] == 2021)]\n",
    "test = stats[stats[\"Year\"] == 2021]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "reg = Ridge(alpha=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.3)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(train[predictors],train[\"Share\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = reg.predict(test[predictors])\n",
    "predictions = pd.DataFrame(predictions, columns=[\"predictions\"], index=test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "combination = pd.concat([test[[\"Player\", \"Share\"]], predictions], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>0.155096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8624</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.163566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3651</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.143014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9907</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.206531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.073973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10997</th>\n",
       "      <td>Luka Dončić</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.150578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7464</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.117028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3536</th>\n",
       "      <td>Julius Randle</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.089546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>Derrick Rose</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.035506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11358</th>\n",
       "      <td>Rudy Gobert</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.095399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.120075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8616</th>\n",
       "      <td>Ben Simmons</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.064762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.140152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5328</th>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.114419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3736</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.147754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9909</th>\n",
       "      <td>Jeff Teague</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.007558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10672</th>\n",
       "      <td>Jalen McDaniels</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.010633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10671</th>\n",
       "      <td>Grant Riller</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.004752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10670</th>\n",
       "      <td>Gordon Hayward</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.042133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10669</th>\n",
       "      <td>Devonte' Graham</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.015590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Player  Share  predictions\n",
       "641             Nikola Jokić  0.961     0.155096\n",
       "8624             Joel Embiid  0.580     0.163566\n",
       "3651           Stephen Curry  0.449     0.143014\n",
       "9907   Giannis Antetokounmpo  0.345     0.206531\n",
       "1389              Chris Paul  0.138     0.073973\n",
       "10997            Luka Dončić  0.042     0.150578\n",
       "7464          Damian Lillard  0.038     0.117028\n",
       "3536           Julius Randle  0.020     0.089546\n",
       "3531            Derrick Rose  0.010     0.035506\n",
       "11358            Rudy Gobert  0.008     0.095399\n",
       "3876       Russell Westbrook  0.005     0.120075\n",
       "8616             Ben Simmons  0.003     0.064762\n",
       "4174            James Harden  0.001     0.140152\n",
       "5328           Kawhi Leonard  0.001     0.114419\n",
       "3736            LeBron James  0.001     0.147754\n",
       "9909             Jeff Teague  0.000    -0.007558\n",
       "10672        Jalen McDaniels  0.000    -0.010633\n",
       "10671           Grant Riller  0.000    -0.004752\n",
       "10670         Gordon Hayward  0.000     0.042133\n",
       "10669        Devonte' Graham  0.000     0.015590"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combination.sort_values(\"Share\", ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.002669615592351053"
      ]
     },
     "execution_count": 103,
     "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": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000    525\n",
       "0.001      3\n",
       "0.961      1\n",
       "0.138      1\n",
       "0.042      1\n",
       "0.038      1\n",
       "0.020      1\n",
       "0.003      1\n",
       "0.008      1\n",
       "0.005      1\n",
       "0.580      1\n",
       "0.449      1\n",
       "0.010      1\n",
       "0.345      1\n",
       "Name: Share, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combination[\"Share\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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": 106,
   "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>0.155096</td>\n",
       "      <td>1</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.155096</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>0.163566</td>\n",
       "      <td>2</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.163566</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>0.143014</td>\n",
       "      <td>3</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.143014</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>0.206531</td>\n",
       "      <td>4</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.206531</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>0.073973</td>\n",
       "      <td>5</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.073973</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       0.155096   1    0.961       0.155096   \n",
       "1            Joel Embiid    0.580       0.163566   2    0.580       0.163566   \n",
       "2          Stephen Curry    0.449       0.143014   3    0.449       0.143014   \n",
       "3  Giannis Antetokounmpo    0.345       0.206531   4    0.345       0.206531   \n",
       "4             Chris Paul    0.138       0.073973   5    0.138       0.073973   \n",
       "\n",
       "   Predicted_Rk  \n",
       "0             3  \n",
       "1             2  \n",
       "2             6  \n",
       "3             1  \n",
       "4            33  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual.merge(predicted, on=\"Player\").head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "ap = find_ap(combination)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7636363636363636"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = list(range(1991,2022))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7113393531244667"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(aps)/len(aps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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": 97,
   "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>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>0.192140</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>0.167640</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>0.128720</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>0.060349</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>0.033417</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>3576</th>\n",
       "      <td>Ennis Whatley</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010313</td>\n",
       "      <td>156</td>\n",
       "      <td>437</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12542</th>\n",
       "      <td>George McCloud</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010324</td>\n",
       "      <td>155</td>\n",
       "      <td>438</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12537</th>\n",
       "      <td>Byron Scott</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010495</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>0.010676</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>-0.055261</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     0.192140             2    1    -1\n",
       "10524  Michael Jordan  0.832     0.167640             3    2    -1\n",
       "908        Grant Hill  0.327     0.128720             6    3    -3\n",
       "4682     Tim Hardaway  0.207     0.060349            20    4   -16\n",
       "8248        Glen Rice  0.117     0.033417            53    5   -48\n",
       "...               ...    ...          ...           ...  ...   ...\n",
       "3576    Ennis Whatley  0.000     0.010313           156  437   281\n",
       "12542  George McCloud  0.000     0.010324           155  438   283\n",
       "12537     Byron Scott  0.000     0.010495           154  439   285\n",
       "1138   Aaron Williams  0.000     0.010676           153  440   287\n",
       "6792    Melvin Booker  0.000    -0.055261           441  441     0\n",
       "\n",
       "[441 rows x 6 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_ranks(all_predictions[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ap, aps, all_predictions = backtest(stats, reg, years[5:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7113393531244667"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>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>0.028389</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>0.032704</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>0.036590</td>\n",
       "      <td>38</td>\n",
       "      <td>4</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>0.051578</td>\n",
       "      <td>34</td>\n",
       "      <td>1</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>0.047312</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>0.070940</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>0.060587</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>0.072010</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>0.077944</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>0.076744</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Player  Share  predictions  Predicted_Rk  Rk  Diff\n",
       "1224        Jason Kidd  0.712     0.028389            52   2   -50\n",
       "5175        Steve Nash  0.839     0.032704            47   1   -46\n",
       "8516   Peja Stojaković  0.228     0.036590            38   4   -34\n",
       "5193        Steve Nash  0.739     0.051578            34   1   -33\n",
       "12726      Joakim Noah  0.258     0.047312            37   4   -33\n",
       "5208        Steve Nash  0.785     0.070940            23   2   -21\n",
       "4682      Tim Hardaway  0.207     0.060587            20   4   -16\n",
       "937      Allen Iverson  0.270     0.072010            14   4   -10\n",
       "6746       Kobe Bryant  0.291     0.077944            14   4   -10\n",
       "1157       Gary Payton  0.372     0.076744            12   3    -9"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions[all_predictions[\"Rk\"] < 5].sort_values(\"Diff\").head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.057397</td>\n",
       "      <td>eFG%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.033896</td>\n",
       "      <td>DRB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.026053</td>\n",
       "      <td>W/L%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.021278</td>\n",
       "      <td>ORB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.015863</td>\n",
       "      <td>2P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.012039</td>\n",
       "      <td>STL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.011504</td>\n",
       "      <td>FTA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.011186</td>\n",
       "      <td>BLK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.007421</td>\n",
       "      <td>AST</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.006126</td>\n",
       "      <td>PTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.006008</td>\n",
       "      <td>FG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.005306</td>\n",
       "      <td>FGA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.004770</td>\n",
       "      <td>3P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.001187</td>\n",
       "      <td>2P%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000362</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>0.000128</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>-0.000321</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.000332</td>\n",
       "      <td>PA/G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-0.000493</td>\n",
       "      <td>PS/G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.000607</td>\n",
       "      <td>SRS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.002397</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.004157</td>\n",
       "      <td>MP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.004200</td>\n",
       "      <td>FT%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.006952</td>\n",
       "      <td>FT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.009264</td>\n",
       "      <td>3P%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.009437</td>\n",
       "      <td>TOV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.010686</td>\n",
       "      <td>3PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.016946</td>\n",
       "      <td>2PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.026793</td>\n",
       "      <td>TRB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.120259</td>\n",
       "      <td>FG%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0     1\n",
       "13  0.057397  eFG%\n",
       "18  0.033896   DRB\n",
       "28  0.026053  W/L%\n",
       "17  0.021278   ORB\n",
       "10  0.015863    2P\n",
       "21  0.012039   STL\n",
       "15  0.011504   FTA\n",
       "22  0.011186   BLK\n",
       "20  0.007421   AST\n",
       "25  0.006126   PTS\n",
       "4   0.006008    FG\n",
       "5   0.005306   FGA\n",
       "7   0.004770    3P\n",
       "12  0.001187   2P%\n",
       "0   0.000362   Age\n",
       "29  0.000314    GB\n",
       "26  0.000128     W\n",
       "1   0.000096     G\n",
       "2  -0.000005    GS\n",
       "27 -0.000321     L\n",
       "31 -0.000332  PA/G\n",
       "30 -0.000493  PS/G\n",
       "32 -0.000607   SRS\n",
       "24 -0.002397    PF\n",
       "3  -0.004157    MP\n",
       "16 -0.004200   FT%\n",
       "14 -0.006952    FT\n",
       "9  -0.009264   3P%\n",
       "23 -0.009437   TOV\n",
       "8  -0.010686   3PA\n",
       "11 -0.016946   2PA\n",
       "19 -0.026793   TRB\n",
       "6  -0.120259   FG%"
      ]
     },
     "execution_count": 111,
     "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": 112,
   "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": 113,
   "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": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors += [\"PTS_R\", \"AST_R\", \"STL_R\", \"BLK_R\", \"3P_R\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ap, aps, all_predictions = backtest(stats, reg, years[5:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7188663114279148"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=50, random_state=1, min_samples_split=4)\n",
    "\n",
    "mean_ap, aps, all_predictions = backtest(stats, rf, years[25:], predictors + [\"NPos\", \"NTm\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7778120136943666"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>5562</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.795607</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6253</th>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.031939</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12896</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.297876</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6340</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.282859</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6334</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.496777</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12930</th>\n",
       "      <td>Michael Carter-Williams</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>183</td>\n",
       "      <td>536</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12931</th>\n",
       "      <td>Mo Bamba</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>182</td>\n",
       "      <td>537</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12932</th>\n",
       "      <td>Moritz Wagner</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>181</td>\n",
       "      <td>538</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12933</th>\n",
       "      <td>Otto Porter Jr.</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180</td>\n",
       "      <td>539</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13901</th>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>540</td>\n",
       "      <td>540</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3101 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Player  Share  predictions  Predicted_Rk   Rk  Diff\n",
       "5562             Stephen Curry  1.000     0.795607             1    1     0\n",
       "6253             Kawhi Leonard  0.484     0.031939            14    2   -12\n",
       "12896             LeBron James  0.482     0.297876             3    3     0\n",
       "6340         Russell Westbrook  0.371     0.282859             4    4     0\n",
       "6334              Kevin Durant  0.112     0.496777             2    5     3\n",
       "...                        ...    ...          ...           ...  ...   ...\n",
       "12930  Michael Carter-Williams  0.000     0.000000           183  536   353\n",
       "12931                 Mo Bamba  0.000     0.000000           182  537   355\n",
       "12932            Moritz Wagner  0.000     0.000000           181  538   357\n",
       "12933          Otto Porter Jr.  0.000     0.000000           180  539   359\n",
       "13901               Trey Lyles  0.000     0.000000           540  540     0\n",
       "\n",
       "[3101 rows x 6 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ap, aps, all_predictions = backtest(stats, reg, years[25:], predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "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>Predicted_Rk</th>\n",
       "      <th>Rk</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5562</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.182840</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6253</th>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.108710</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12896</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.157255</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6340</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.132637</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6334</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.161326</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3732</th>\n",
       "      <td>Jared Dudley</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007178</td>\n",
       "      <td>184</td>\n",
       "      <td>536</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>Jeff Green</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007269</td>\n",
       "      <td>183</td>\n",
       "      <td>537</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6098</th>\n",
       "      <td>Nemanja Bjelica</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007365</td>\n",
       "      <td>182</td>\n",
       "      <td>538</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9910</th>\n",
       "      <td>Jordan Nwora</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007435</td>\n",
       "      <td>181</td>\n",
       "      <td>539</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11768</th>\n",
       "      <td>Didi Louzada</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.067311</td>\n",
       "      <td>540</td>\n",
       "      <td>540</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3101 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Player  Share  predictions  Predicted_Rk   Rk  Diff\n",
       "5562       Stephen Curry  1.000     0.182840             1    1     0\n",
       "6253       Kawhi Leonard  0.484     0.108710            10    2    -8\n",
       "12896       LeBron James  0.482     0.157255             3    3     0\n",
       "6340   Russell Westbrook  0.371     0.132637             6    4    -2\n",
       "6334        Kevin Durant  0.112     0.161326             2    5     3\n",
       "...                  ...    ...          ...           ...  ...   ...\n",
       "3732        Jared Dudley  0.000     0.007178           184  536   352\n",
       "4175          Jeff Green  0.000     0.007269           183  537   354\n",
       "6098     Nemanja Bjelica  0.000     0.007365           182  538   356\n",
       "9910        Jordan Nwora  0.000     0.007435           181  539   358\n",
       "11768       Didi Louzada  0.000    -0.067311           540  540     0\n",
       "\n",
       "[3101 rows x 6 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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