predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We start by formatting our data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Visitor/Neutral</th>\n",
       "      <th>PTS</th>\n",
       "      <th>Home/Neutral</th>\n",
       "      <th>PTS.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>70</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>71</td>\n",
       "      <td>Los Angeles Sparks</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>77</td>\n",
       "      <td>Minnesota Lynx</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>64</td>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Atlanta Dream</td>\n",
       "      <td>78</td>\n",
       "      <td>Dallas Wings</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Visitor/Neutral  PTS        Home/Neutral  PTS.1\n",
       "0   Connecticut Sun   70       Indiana Fever     61\n",
       "1   Phoenix Mercury   71  Los Angeles Sparks     94\n",
       "2       Chicago Sky   77      Minnesota Lynx     66\n",
       "3  New York Liberty   64  Washington Mystics     80\n",
       "4     Atlanta Dream   78        Dallas Wings     85"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://www.basketball-reference.com/wnba/years/2023_games.html\n",
    "schedule = pd.read_csv(\"reg_season.csv\")\n",
    "\n",
    "# Remove first and last 2 columns (date, unnamed, and notes)\n",
    "schedule = schedule.iloc[:, 1:-2]\n",
    "# Display dataframe\n",
    "schedule.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Team</th>\n",
       "      <th>Age</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>PW</th>\n",
       "      <th>PL</th>\n",
       "      <th>MOV</th>\n",
       "      <th>SOS</th>\n",
       "      <th>SRS</th>\n",
       "      <th>ORtg</th>\n",
       "      <th>...</th>\n",
       "      <th>3PAr</th>\n",
       "      <th>TS%</th>\n",
       "      <th>eFG%</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>ORB%</th>\n",
       "      <th>FT/FGA</th>\n",
       "      <th>eFG%.1</th>\n",
       "      <th>TOV%.1</th>\n",
       "      <th>DRB%</th>\n",
       "      <th>FT/FGA.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>12.55</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>11.46</td>\n",
       "      <td>114.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360</td>\n",
       "      <td>0.597</td>\n",
       "      <td>0.553</td>\n",
       "      <td>12.5</td>\n",
       "      <td>18.9</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.480</td>\n",
       "      <td>14.4</td>\n",
       "      <td>78.2</td>\n",
       "      <td>0.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>28.8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>8.68</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>8.14</td>\n",
       "      <td>111.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.539</td>\n",
       "      <td>14.9</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.476</td>\n",
       "      <td>13.1</td>\n",
       "      <td>78.5</td>\n",
       "      <td>0.184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>29.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>3.70</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>3.43</td>\n",
       "      <td>105.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.294</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.498</td>\n",
       "      <td>14.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.486</td>\n",
       "      <td>16.7</td>\n",
       "      <td>75.8</td>\n",
       "      <td>0.236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dallas Wings</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>2.95</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>2.64</td>\n",
       "      <td>109.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.489</td>\n",
       "      <td>13.7</td>\n",
       "      <td>32.8</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.504</td>\n",
       "      <td>15.0</td>\n",
       "      <td>77.2</td>\n",
       "      <td>0.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Washington Mystics</td>\n",
       "      <td>28.8</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>101.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.486</td>\n",
       "      <td>13.9</td>\n",
       "      <td>18.7</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.496</td>\n",
       "      <td>16.3</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Team   Age     W     L  PW  PL    MOV   SOS    SRS   ORtg  \\\n",
       "0      Las Vegas Aces  29.0  34.0   6.0  35   5  12.55 -1.09  11.46  114.8   \n",
       "1    New York Liberty  28.8  32.0   8.0  32   8   8.68 -0.53   8.14  111.8   \n",
       "2     Connecticut Sun  29.1  27.0  13.0  26  14   3.70 -0.27   3.43  105.4   \n",
       "3        Dallas Wings  26.0  22.0  18.0  25  15   2.95 -0.31   2.64  109.5   \n",
       "4  Washington Mystics  28.8  19.0  21.0  19  21  -0.35  0.03  -0.32  101.5   \n",
       "\n",
       "   ...   3PAr    TS%   eFG%  TOV%  ORB%  FT/FGA  eFG%.1  TOV%.1  DRB%  \\\n",
       "0  ...  0.360  0.597  0.553  12.5  18.9   0.236   0.480    14.4  78.2   \n",
       "1  ...  0.424  0.577  0.539  14.9  25.8   0.198   0.476    13.1  78.5   \n",
       "2  ...  0.294  0.540  0.498  14.0  23.9   0.224   0.486    16.7  75.8   \n",
       "3  ...  0.292  0.535  0.489  13.7  32.8   0.224   0.504    15.0  77.2   \n",
       "4  ...  0.342  0.533  0.486  13.9  18.7   0.222   0.496    16.3  78.0   \n",
       "\n",
       "   FT/FGA.1  \n",
       "0     0.171  \n",
       "1     0.184  \n",
       "2     0.236  \n",
       "3     0.237  \n",
       "4     0.238  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://www.basketball-reference.com/wnba/years/2023.html\n",
    "advanced_stats = pd.read_csv(\"advanced_stats.csv\")\n",
    "\n",
    "# Remove nan columns\n",
    "advanced_stats = advanced_stats.dropna(axis=1, how='all')\n",
    "# Remove fist and last columns (rank, and arena)\n",
    "advanced_stats = advanced_stats.iloc[:, 1:-1]\n",
    "# Display dataframe\n",
    "advanced_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Visitor/Neutral</th>\n",
       "      <th>PTS</th>\n",
       "      <th>Home/Neutral</th>\n",
       "      <th>PTS.1</th>\n",
       "      <th>Age_x</th>\n",
       "      <th>W_x</th>\n",
       "      <th>L_x</th>\n",
       "      <th>PW_x</th>\n",
       "      <th>PL_x</th>\n",
       "      <th>MOV_x</th>\n",
       "      <th>...</th>\n",
       "      <th>3PAr_y</th>\n",
       "      <th>TS%_y</th>\n",
       "      <th>eFG%_y</th>\n",
       "      <th>TOV%_y</th>\n",
       "      <th>ORB%_y</th>\n",
       "      <th>FT/FGA_y</th>\n",
       "      <th>eFG%.1_y</th>\n",
       "      <th>TOV%.1_y</th>\n",
       "      <th>DRB%_y</th>\n",
       "      <th>FT/FGA.1_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>70</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>61</td>\n",
       "      <td>29.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>3.70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.491</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.509</td>\n",
       "      <td>13.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>88</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>72</td>\n",
       "      <td>29.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>3.70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.491</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.509</td>\n",
       "      <td>13.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>85</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>82</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>-8.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.491</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.509</td>\n",
       "      <td>13.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>71</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>72</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>-8.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.491</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.509</td>\n",
       "      <td>13.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>89</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>87</td>\n",
       "      <td>27.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>...</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.491</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.509</td>\n",
       "      <td>13.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Visitor/Neutral  PTS   Home/Neutral  PTS.1  Age_x   W_x   L_x  PW_x  PL_x  \\\n",
       "0  Connecticut Sun   70  Indiana Fever     61   29.1  27.0  13.0    26    14   \n",
       "1  Connecticut Sun   88  Indiana Fever     72   29.1  27.0  13.0    26    14   \n",
       "2  Phoenix Mercury   85  Indiana Fever     82   27.8   9.0  31.0     8    32   \n",
       "3  Phoenix Mercury   71  Indiana Fever     72   27.8   9.0  31.0     8    32   \n",
       "4      Chicago Sky   89  Indiana Fever     87   27.2  18.0  22.0    17    23   \n",
       "\n",
       "   MOV_x  ...  3PAr_y  TS%_y  eFG%_y  TOV%_y  ORB%_y  FT/FGA_y  eFG%.1_y  \\\n",
       "0   3.70  ...    0.29  0.532   0.491    15.6    26.4     0.204     0.509   \n",
       "1   3.70  ...    0.29  0.532   0.491    15.6    26.4     0.204     0.509   \n",
       "2  -8.30  ...    0.29  0.532   0.491    15.6    26.4     0.204     0.509   \n",
       "3  -8.30  ...    0.29  0.532   0.491    15.6    26.4     0.204     0.509   \n",
       "4  -1.63  ...    0.29  0.532   0.491    15.6    26.4     0.204     0.509   \n",
       "\n",
       "   TOV%.1_y  DRB%_y  FT/FGA.1_y  \n",
       "0      13.9    76.8       0.248  \n",
       "1      13.9    76.8       0.248  \n",
       "2      13.9    76.8       0.248  \n",
       "3      13.9    76.8       0.248  \n",
       "4      13.9    76.8       0.248  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the two data frames by team name\n",
    "df = pd.merge(schedule, advanced_stats, left_on=\"Visitor/Neutral\", right_on=\"Team\")\n",
    "df = pd.merge(df, advanced_stats, left_on=\"Home/Neutral\", right_on=\"Team\")\n",
    "\n",
    "# Remove duplicate columns\n",
    "df = df.drop(['Team_x', 'Team_y'], axis=1)\n",
    "# Display dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\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>Visitor/Neutral</th>\n",
       "      <th>PTS</th>\n",
       "      <th>Home/Neutral</th>\n",
       "      <th>PTS.1</th>\n",
       "      <th>Age_x</th>\n",
       "      <th>W_x</th>\n",
       "      <th>L_x</th>\n",
       "      <th>PW_x</th>\n",
       "      <th>PL_x</th>\n",
       "      <th>MOV_x</th>\n",
       "      <th>...</th>\n",
       "      <th>TS%_y</th>\n",
       "      <th>eFG%_y</th>\n",
       "      <th>TOV%_y</th>\n",
       "      <th>ORB%_y</th>\n",
       "      <th>FT/FGA_y</th>\n",
       "      <th>eFG%.1_y</th>\n",
       "      <th>TOV%.1_y</th>\n",
       "      <th>DRB%_y</th>\n",
       "      <th>FT/FGA.1_y</th>\n",
       "      <th>Home_Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>70</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>61</td>\n",
       "      <td>29.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>3.70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.491</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.509</td>\n",
       "      <td>13.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Connecticut Sun</td>\n",
       "      <td>88</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>72</td>\n",
       "      <td>29.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>3.70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.491</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.509</td>\n",
       "      <td>13.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>85</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>82</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>-8.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.491</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.509</td>\n",
       "      <td>13.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>71</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>72</td>\n",
       "      <td>27.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>-8.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.491</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.509</td>\n",
       "      <td>13.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.248</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>89</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>87</td>\n",
       "      <td>27.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>...</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.491</td>\n",
       "      <td>15.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.509</td>\n",
       "      <td>13.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Visitor/Neutral  PTS   Home/Neutral  PTS.1  Age_x   W_x   L_x  PW_x  PL_x  \\\n",
       "0  Connecticut Sun   70  Indiana Fever     61   29.1  27.0  13.0    26    14   \n",
       "1  Connecticut Sun   88  Indiana Fever     72   29.1  27.0  13.0    26    14   \n",
       "2  Phoenix Mercury   85  Indiana Fever     82   27.8   9.0  31.0     8    32   \n",
       "3  Phoenix Mercury   71  Indiana Fever     72   27.8   9.0  31.0     8    32   \n",
       "4      Chicago Sky   89  Indiana Fever     87   27.2  18.0  22.0    17    23   \n",
       "\n",
       "   MOV_x  ...  TS%_y  eFG%_y  TOV%_y  ORB%_y  FT/FGA_y  eFG%.1_y  TOV%.1_y  \\\n",
       "0   3.70  ...  0.532   0.491    15.6    26.4     0.204     0.509      13.9   \n",
       "1   3.70  ...  0.532   0.491    15.6    26.4     0.204     0.509      13.9   \n",
       "2  -8.30  ...  0.532   0.491    15.6    26.4     0.204     0.509      13.9   \n",
       "3  -8.30  ...  0.532   0.491    15.6    26.4     0.204     0.509      13.9   \n",
       "4  -1.63  ...  0.532   0.491    15.6    26.4     0.204     0.509      13.9   \n",
       "\n",
       "   DRB%_y  FT/FGA.1_y  Home_Winner  \n",
       "0    76.8       0.248          0.0  \n",
       "1    76.8       0.248          0.0  \n",
       "2    76.8       0.248          0.0  \n",
       "3    76.8       0.248          1.0  \n",
       "4    76.8       0.248          0.0  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a column for to show if the home team won\n",
    "for index, row in df.iterrows():\n",
    "    # Determine which team had more points\n",
    "    if df.loc[index, 'PTS'] > df.loc[index, 'PTS.1']:\n",
    "        # Place 0 for home loss\n",
    "        df.loc[index, 'Home_Winner'] = 0\n",
    "    else:\n",
    "        # Place 1 for home win\n",
    "        df.loc[index, 'Home_Winner'] = 1\n",
    "\n",
    "# Display dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.204</td>\n",
       "      <td>0.509</td>\n",
       "      <td>13.9</td>\n",
       "      <td>76.8</td>\n",
       "      <td>0.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>-8.30</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-7.75</td>\n",
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       "      <td>0.248</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>23</td>\n",
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       "      <td>0.11</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>103.3</td>\n",
       "      <td>105.3</td>\n",
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       "      <td>0.248</td>\n",
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       "<p>5 rows × 46 columns</p>\n",
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      "text/plain": [
       "   Age_x   W_x   L_x  PW_x  PL_x  MOV_x  SOS_x  SRS_x  ORtg_x  DRtg_x  ...  \\\n",
       "0   29.1  27.0  13.0    26    14   3.70  -0.27   3.43   105.4   100.7  ...   \n",
       "1   29.1  27.0  13.0    26    14   3.70  -0.27   3.43   105.4   100.7  ...   \n",
       "2   27.8   9.0  31.0     8    32  -8.30   0.55  -7.75    99.1   109.9  ...   \n",
       "3   27.8   9.0  31.0     8    32  -8.30   0.55  -7.75    99.1   109.9  ...   \n",
       "4   27.2  18.0  22.0    17    23  -1.63   0.11  -1.52   103.3   105.3  ...   \n",
       "\n",
       "   3PAr_y  TS%_y  eFG%_y  TOV%_y  ORB%_y  FT/FGA_y  eFG%.1_y  TOV%.1_y  \\\n",
       "0    0.29  0.532   0.491    15.6    26.4     0.204     0.509      13.9   \n",
       "1    0.29  0.532   0.491    15.6    26.4     0.204     0.509      13.9   \n",
       "2    0.29  0.532   0.491    15.6    26.4     0.204     0.509      13.9   \n",
       "3    0.29  0.532   0.491    15.6    26.4     0.204     0.509      13.9   \n",
       "4    0.29  0.532   0.491    15.6    26.4     0.204     0.509      13.9   \n",
       "\n",
       "   DRB%_y  FT/FGA.1_y  \n",
       "0    76.8       0.248  \n",
       "1    76.8       0.248  \n",
       "2    76.8       0.248  \n",
       "3    76.8       0.248  \n",
       "4    76.8       0.248  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which columns we don't want to use for our prediction model\n",
    "\n",
    "# Columns we want to remove\n",
    "remove_cols = [\"Home/Neutral\", \"Visitor/Neutral\", \"Home_Winner\", \"PTS\", \"PTS.1\"]\n",
    "\n",
    "# Columns we want to keep\n",
    "selected_cols = [x for x in df.columns if x not in remove_cols]\n",
    "\n",
    "# Display columns we want to keep\n",
    "df[selected_cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.392405</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.690909</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>85</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>82</td>\n",
       "      <td>0.59375</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.177215</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.659574</td>\n",
       "      <td>0.546099</td>\n",
       "      <td>0.392405</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.690909</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Phoenix Mercury</td>\n",
       "      <td>71</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>72</td>\n",
       "      <td>0.59375</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.177215</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.659574</td>\n",
       "      <td>0.546099</td>\n",
       "      <td>0.392405</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.690909</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chicago Sky</td>\n",
       "      <td>89</td>\n",
       "      <td>Indiana Fever</td>\n",
       "      <td>87</td>\n",
       "      <td>0.40625</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.319904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.177215</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.659574</td>\n",
       "      <td>0.546099</td>\n",
       "      <td>0.392405</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.216216</td>\n",
       "      <td>0.690909</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Visitor/Neutral  PTS   Home/Neutral  PTS.1    Age_x   W_x   L_x      PW_x  \\\n",
       "0  Connecticut Sun   70  Indiana Fever     61  1.00000  0.72  0.28  0.666667   \n",
       "1  Connecticut Sun   88  Indiana Fever     72  1.00000  0.72  0.28  0.666667   \n",
       "2  Phoenix Mercury   85  Indiana Fever     82  0.59375  0.00  1.00  0.000000   \n",
       "3  Phoenix Mercury   71  Indiana Fever     72  0.59375  0.00  1.00  0.000000   \n",
       "4      Chicago Sky   89  Indiana Fever     87  0.40625  0.36  0.64  0.333333   \n",
       "\n",
       "       PL_x     MOV_x  ...     TS%_y  eFG%_y    TOV%_y    ORB%_y  FT/FGA_y  \\\n",
       "0  0.333333  0.575540  ...  0.177215   0.225  0.659574  0.546099  0.392405   \n",
       "1  0.333333  0.575540  ...  0.177215   0.225  0.659574  0.546099  0.392405   \n",
       "2  1.000000  0.000000  ...  0.177215   0.225  0.659574  0.546099  0.392405   \n",
       "3  1.000000  0.000000  ...  0.177215   0.225  0.659574  0.546099  0.392405   \n",
       "4  0.666667  0.319904  ...  0.177215   0.225  0.659574  0.546099  0.392405   \n",
       "\n",
       "   eFG%.1_y  TOV%.1_y    DRB%_y  FT/FGA.1_y  Home_Winner  \n",
       "0  0.891892  0.216216  0.690909      0.9625          0.0  \n",
       "1  0.891892  0.216216  0.690909      0.9625          0.0  \n",
       "2  0.891892  0.216216  0.690909      0.9625          0.0  \n",
       "3  0.891892  0.216216  0.690909      0.9625          1.0  \n",
       "4  0.891892  0.216216  0.690909      0.9625          0.0  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scale our data\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Initialize scaler\n",
    "scalar = MinMaxScaler()\n",
    "# Scale our data\n",
    "df[selected_cols] = scalar.fit_transform(df[selected_cols])\n",
    "# Display dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Then determine which predictors we should use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SequentialFeatureSelector\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "\n",
    "# Initialize our ridge regression classification\n",
    "rr = RidgeClassifier(alpha=1.0)\n",
    "\n",
    "# Select the best 10 features backward\n",
    "sfs = SequentialFeatureSelector(rr, n_features_to_select=10, direction='backward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SequentialFeatureSelector(direction=&#x27;backward&#x27;, estimator=RidgeClassifier(),\n",
       "                          n_features_to_select=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SequentialFeatureSelector</label><div class=\"sk-toggleable__content\"><pre>SequentialFeatureSelector(direction=&#x27;backward&#x27;, estimator=RidgeClassifier(),\n",
       "                          n_features_to_select=10)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RidgeClassifier</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeClassifier</label><div class=\"sk-toggleable__content\"><pre>RidgeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SequentialFeatureSelector(direction='backward', estimator=RidgeClassifier(),\n",
       "                          n_features_to_select=10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which columns are the most impactful when predicting the winner\n",
    "sfs.fit(df[selected_cols], df['Home_Winner'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>ORB%_x</th>\n",
       "      <th>eFG%.1_x</th>\n",
       "      <th>TOV%.1_x</th>\n",
       "      <th>L_y</th>\n",
       "      <th>TS%_y</th>\n",
       "      <th>eFG%_y</th>\n",
       "      <th>ORB%_y</th>\n",
       "      <th>FT/FGA_y</th>\n",
       "      <th>eFG%.1_y</th>\n",
       "      <th>TOV%.1_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.368794</td>\n",
       "      <td>0.270270</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.177215</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.546099</td>\n",
       "      <td>0.392405</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.216216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.368794</td>\n",
       "      <td>0.270270</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.177215</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.546099</td>\n",
       "      <td>0.392405</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.216216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.156028</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.081081</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.177215</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.546099</td>\n",
       "      <td>0.392405</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.216216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.156028</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.081081</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.177215</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.546099</td>\n",
       "      <td>0.392405</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.216216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.404255</td>\n",
       "      <td>0.621622</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.177215</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.546099</td>\n",
       "      <td>0.392405</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.216216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ORB%_x  eFG%.1_x  TOV%.1_x   L_y     TS%_y  eFG%_y    ORB%_y  FT/FGA_y  \\\n",
       "0  0.368794  0.270270  0.972973  0.84  0.177215   0.225  0.546099  0.392405   \n",
       "1  0.368794  0.270270  0.972973  0.84  0.177215   0.225  0.546099  0.392405   \n",
       "2  0.156028  0.810811  0.081081  0.84  0.177215   0.225  0.546099  0.392405   \n",
       "3  0.156028  0.810811  0.081081  0.84  0.177215   0.225  0.546099  0.392405   \n",
       "4  0.404255  0.621622  0.405405  0.84  0.177215   0.225  0.546099  0.392405   \n",
       "\n",
       "   eFG%.1_y  TOV%.1_y  \n",
       "0  0.891892  0.216216  \n",
       "1  0.891892  0.216216  \n",
       "2  0.891892  0.216216  \n",
       "3  0.891892  0.216216  \n",
       "4  0.891892  0.216216  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of the most impactful columns\n",
    "predictors = list(df[selected_cols].columns[sfs.get_support()])\n",
    "# Display the most impactful columns\n",
    "df[predictors].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we train and test a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def monte_carlo(n):\n",
    "    accuracy = []\n",
    "    for i in range(n):\n",
    "        # Split the data into training and test sets\n",
    "        X_train, X_test, y_train, y_test = train_test_split(df[predictors], df['Home_Winner'], test_size=0.2)\n",
    "\n",
    "        # Train a logistic regression model on the training data\n",
    "        model = LogisticRegression()\n",
    "        # Fit the model to our training data\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        # Predict the winners for the test data\n",
    "        y_pred = model.predict(X_test)\n",
    "\n",
    "        # Evaluate the accuracy of the model on the test data\n",
    "        accuracy.append(accuracy_score(y_test, y_pred))\n",
    "\n",
    "    # Get the average accuracy\n",
    "    score = sum(accuracy) / len(accuracy)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7045416666666663\n"
     ]
    }
   ],
   "source": [
    "score = monte_carlo(1000)\n",
    "print(f\"Accuracy: {score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on 1000 simulations, our model is 70% accurate in predicting outcomes of WNBA games."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now, let's use it to predict the outcome of the Finals games"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Visitor/Neutral</th>\n",
       "      <th>PTS</th>\n",
       "      <th>Home/Neutral</th>\n",
       "      <th>PTS.1</th>\n",
       "      <th>Age_x</th>\n",
       "      <th>W_x</th>\n",
       "      <th>L_x</th>\n",
       "      <th>PW_x</th>\n",
       "      <th>PL_x</th>\n",
       "      <th>MOV_x</th>\n",
       "      <th>...</th>\n",
       "      <th>TS%_y</th>\n",
       "      <th>eFG%_y</th>\n",
       "      <th>TOV%_y</th>\n",
       "      <th>ORB%_y</th>\n",
       "      <th>FT/FGA_y</th>\n",
       "      <th>eFG%.1_y</th>\n",
       "      <th>TOV%.1_y</th>\n",
       "      <th>DRB%_y</th>\n",
       "      <th>FT/FGA.1_y</th>\n",
       "      <th>Home_Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Visitor/Neutral, PTS, Home/Neutral, PTS.1, Age_x, W_x, L_x, PW_x, PL_x, MOV_x, SOS_x, SRS_x, ORtg_x, DRtg_x, NRtg_x, Pace_x, FTr_x, 3PAr_x, TS%_x, eFG%_x, TOV%_x, ORB%_x, FT/FGA_x, eFG%.1_x, TOV%.1_x, DRB%_x, FT/FGA.1_x, Age_y, W_y, L_y, PW_y, PL_y, MOV_y, SOS_y, SRS_y, ORtg_y, DRtg_y, NRtg_y, Pace_y, FTr_y, 3PAr_y, TS%_y, eFG%_y, TOV%_y, ORB%_y, FT/FGA_y, eFG%.1_y, TOV%.1_y, DRB%_y, FT/FGA.1_y, Home_Winner]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 51 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the rows where the Aces and Liberty play against each other\n",
    "non_aces_liberty_game = df\n",
    "non_aces_liberty_game = non_aces_liberty_game.drop(non_aces_liberty_game[(non_aces_liberty_game['Home/Neutral'] == 'Las Vegas Aces') & (non_aces_liberty_game['Visitor/Neutral'] == 'New York Liberty')].index)\n",
    "non_aces_liberty_game = non_aces_liberty_game.drop(non_aces_liberty_game[(non_aces_liberty_game['Home/Neutral'] == 'New York Liberty') & (non_aces_liberty_game['Visitor/Neutral'] == 'Las Vegas Aces')].index)\n",
    "non_aces_liberty_game[(non_aces_liberty_game['Home/Neutral'] == 'Las Vegas Aces') & (non_aces_liberty_game['Visitor/Neutral'] == 'New York Liberty')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>ORB%_x</th>\n",
       "      <th>eFG%.1_x</th>\n",
       "      <th>TOV%.1_x</th>\n",
       "      <th>L_y</th>\n",
       "      <th>TS%_y</th>\n",
       "      <th>eFG%_y</th>\n",
       "      <th>ORB%_y</th>\n",
       "      <th>FT/FGA_y</th>\n",
       "      <th>eFG%.1_y</th>\n",
       "      <th>TOV%.1_y</th>\n",
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       "      <th>144</th>\n",
       "      <td>0.503546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014184</td>\n",
       "      <td>0.797468</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>0.351351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ORB%_x  eFG%.1_x  TOV%.1_x  L_y  TS%_y  eFG%_y    ORB%_y  FT/FGA_y  \\\n",
       "144  0.503546       0.0       0.0  0.0    1.0     1.0  0.014184  0.797468   \n",
       "\n",
       "     eFG%.1_y  TOV%.1_y  \n",
       "144  0.108108  0.351351  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_matchup = df[(df['Home/Neutral'] == 'Las Vegas Aces') & (df['Visitor/Neutral'] == 'New York Liberty')][:1]\n",
    "final_matchup[predictors]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Predict the winner of the final matchup\n",
    "model = LogisticRegression()\n",
    "model.fit(non_aces_liberty_game[predictors], non_aces_liberty_game['Home_Winner'])\n",
    "\n",
    "y_pred = model.predict(final_matchup[predictors])\n",
    "print(f\"Prediction: {y_pred[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prediction is that the Home team wins, meaning the Aces win!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More complex prediction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab rows from the df where Aces are playing the Liberty (home and away) (the matchup for the finals)\n",
    "final_matchup_1 = df[(df['Home/Neutral'] == 'Las Vegas Aces') & (df['Visitor/Neutral'] == 'New York Liberty')][1:]\n",
    "final_matchup_2 = df[(df['Home/Neutral'] == 'New York Liberty') & (df['Visitor/Neutral'] == 'Las Vegas Aces')][1:]\n",
    "\n",
    "# Combine the two data frames to match the games played in the finals\n",
    "finals = pd.concat([final_matchup_1, final_matchup_1, final_matchup_2, final_matchup_2, final_matchup_1, final_matchup_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the winner of the final matchup\n",
    "model = LogisticRegression()\n",
    "model.fit(non_aces_liberty_game[predictors], non_aces_liberty_game['Home_Winner'])\n",
    "\n",
    "# Predict the outcome of the final_matchup\n",
    "y_pred = model.predict(final_matchup[predictors])\n",
    "print(f\"Prediction: {y_pred[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Home/Neutral</th>\n",
       "      <th>Visitor/Neutral</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>New York Liberty</td>\n",
       "      <td>Las Vegas Aces</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Home/Neutral   Visitor/Neutral  Home_Winner\n",
       "145    Las Vegas Aces  New York Liberty          1.0\n",
       "145    Las Vegas Aces  New York Liberty          1.0\n",
       "49   New York Liberty    Las Vegas Aces          1.0\n",
       "49   New York Liberty    Las Vegas Aces          1.0\n",
       "145    Las Vegas Aces  New York Liberty          1.0\n",
       "49   New York Liberty    Las Vegas Aces          1.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finals[['Home/Neutral', 'Visitor/Neutral', 'Home_Winner']]"
   ]
  }
 ],
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