Models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import brier_score_loss\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prep Master Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (0,1,2,3,5,6,8,9,11,12,13,14,15,16,18,19,20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#create master player data\n",
    "\n",
    "e_fg_df = pd.read_csv(\"e_fg.txt\", sep=\",\")\n",
    "e_fg_df = e_fg_df[['Player', 'eFG%▼']]\n",
    "e_fg_df.columns = ['Player', 'eFG%']\n",
    "names = list(e_fg_df['Player'])\n",
    "names = [item.partition(\"\\\\\")[0] for item in names]\n",
    "e_fg_df['Player'] = names\n",
    "\n",
    "#link names with ID\n",
    "text = open(\"Hackathon_player_names_matched_team.txt\")\n",
    "line = text.readline()\n",
    "line = text.readline()\n",
    "IDtoName = {} # ID as keys\n",
    "NametoID = {} # name as keys\n",
    "\n",
    "while line != \"\" :\n",
    "    data = line.split()\n",
    "    name = data[4] + \" \" + data[5]\n",
    "    name = name.replace(\"\\\"\",\"\")\n",
    "    ID = (float(data[3]))\n",
    "    if ID not in IDtoName:\n",
    "        IDtoName[ID] = name\n",
    "    if name not in NametoID:\n",
    "        NametoID[name] = ID\n",
    "    line = text.readline()\n",
    "    \n",
    "#print(NametoID[\"LeBron James\"])\n",
    "#print(IDtoName[2544])\n",
    "\n",
    "shot_df_name = pd.read_csv(\"Hackathon_sv_shot_summary_2015-16.txt\", sep=\" \")\n",
    "#have 165 occurences of null for closet defender id\n",
    "shot_df_name = shot_df_name.replace({'(null)': -1})\n",
    "#shot_df = shot_df.drop(shot_df.index[222316])\n",
    "shot_df_name = shot_df_name[shot_df_name['SEASON'] != \"GAME_ID\"]\n",
    "shot_df_name = shot_df_name[['PERSON_ID', 'SV_PLAYER_ID']]\n",
    "#convert columns to numbers\n",
    "shot_df_name[['PERSON_ID', 'SV_PLAYER_ID']] = shot_df_name[['PERSON_ID', 'SV_PLAYER_ID']].apply(pd.to_numeric).drop_duplicates()\n",
    "names_df = pd.DataFrame(list(NametoID.items()), columns = ['NAME', 'PERSON_ID'])\n",
    "names_df = pd.merge(names_df, shot_df_name,  how='left', left_on='PERSON_ID', right_on = 'PERSON_ID')\n",
    "names_df = names_df.dropna()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "#eFG%\n",
    "e_fg_df_all = names_df.merge(e_fg_df, how = \"left\", left_on = 'NAME', right_on = 'Player')\n",
    "e_fg_df_all = e_fg_df_all[['NAME', 'eFG%', 'SV_PLAYER_ID']]\n",
    "\n",
    "#0.502 is average shooting percentage. Might want to drop because we have the best shooters\n",
    "e_fg_df_all.fillna(value = 0.502, inplace = True)\n",
    "\n",
    "#Blocks\n",
    "blocks_df = pd.read_csv(\"blocks.txt\", sep=\",\")\n",
    "blocks_df = blocks_df[['Player', 'BLK▼']]\n",
    "blocks_df.columns = ['Player', 'BLK']\n",
    "names = list(blocks_df['Player'])\n",
    "names = [item.partition(\"\\\\\")[0] for item in names]\n",
    "blocks_df['Player'] = names\n",
    "master_df = e_fg_df_all.merge(blocks_df, how = \"left\", left_on = 'NAME', right_on = 'Player')\n",
    "master_df.fillna(value = 0, inplace = True)\n",
    "master_df = master_df[['NAME', 'eFG%', 'BLK', 'SV_PLAYER_ID']]\n",
    "\n",
    "#Advanced Stats\n",
    "adv_df = pd.read_csv(\"advanced_stats.txt\", sep=\",\")\n",
    "adv_df = adv_df[[\n",
    " 'Player','PER','OWS','DWS','WS','WS/48','OBPM','DBPM','BPM','VORP']]\n",
    "names = list(adv_df['Player'])\n",
    "names = [item.partition(\"\\\\\")[0] for item in names]\n",
    "adv_df['Player'] = names\n",
    "master_df = master_df.merge(adv_df, how = \"left\", left_on = 'NAME', right_on = 'Player')\n",
    "\n",
    "master_df = master_df.drop(columns = 'Player')\n",
    "master_df = master_df.drop_duplicates(subset='NAME', keep=\"first\")\n",
    "master_df = master_df.drop_duplicates(subset='SV_PLAYER_ID', keep=\"first\")\n",
    "\n",
    "\n",
    "#some missing data for advanced stats. Rounded it to the average of the row\n",
    "master_df.fillna(round(master_df.mean(), 3), inplace=True)\n",
    "\n",
    "rpm_df = pd.read_csv(\"rpm.csv\", sep=\",\")\n",
    "\n",
    "rpm_df = rpm_df[['NAME', 'ORPM', 'DRPM', 'RPM']]\n",
    "names = list(rpm_df['NAME'])\n",
    "names = [item.partition(\",\")[0] for item in names]\n",
    "rpm_df['NAME'] = names\n",
    "\n",
    "master_df = master_df.merge(rpm_df, how = \"left\", left_on = 'NAME', right_on = 'NAME')\n",
    "master_df = master_df.fillna(value = 0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
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       "        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>NAME</th>\n",
       "      <th>eFG%</th>\n",
       "      <th>BLK</th>\n",
       "      <th>SV_PLAYER_ID</th>\n",
       "      <th>PER</th>\n",
       "      <th>OWS</th>\n",
       "      <th>DWS</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/48</th>\n",
       "      <th>OBPM</th>\n",
       "      <th>DBPM</th>\n",
       "      <th>BPM</th>\n",
       "      <th>VORP</th>\n",
       "      <th>ORPM</th>\n",
       "      <th>DRPM</th>\n",
       "      <th>RPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Josh Smith</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3834.0</td>\n",
       "      <td>10.600</td>\n",
       "      <td>-1.200</td>\n",
       "      <td>1.200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-3.600</td>\n",
       "      <td>1.600</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.14</td>\n",
       "      <td>1.05</td>\n",
       "      <td>-2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Marvin Williams</td>\n",
       "      <td>0.554</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3928.0</td>\n",
       "      <td>16.800</td>\n",
       "      <td>4.700</td>\n",
       "      <td>3.200</td>\n",
       "      <td>7.800</td>\n",
       "      <td>0.161</td>\n",
       "      <td>1.700</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.700</td>\n",
       "      <td>2.70</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jason Terry</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3333.0</td>\n",
       "      <td>10.200</td>\n",
       "      <td>1.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>1.500</td>\n",
       "      <td>0.057</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-2.600</td>\n",
       "      <td>-2.800</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-3.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kent Bazemore</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5102.0</td>\n",
       "      <td>13.400</td>\n",
       "      <td>0.300</td>\n",
       "      <td>3.800</td>\n",
       "      <td>4.100</td>\n",
       "      <td>0.094</td>\n",
       "      <td>-1.300</td>\n",
       "      <td>1.600</td>\n",
       "      <td>0.300</td>\n",
       "      <td>1.20</td>\n",
       "      <td>-1.49</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Joe Johnson</td>\n",
       "      <td>0.509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3520.0</td>\n",
       "      <td>12.000</td>\n",
       "      <td>1.900</td>\n",
       "      <td>0.800</td>\n",
       "      <td>2.700</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.300</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>-1.300</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.80</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tim Hardaway</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5194.0</td>\n",
       "      <td>11.700</td>\n",
       "      <td>0.900</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.900</td>\n",
       "      <td>0.105</td>\n",
       "      <td>-0.900</td>\n",
       "      <td>-1.200</td>\n",
       "      <td>-2.100</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thabo Sefolosha</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4141.0</td>\n",
       "      <td>12.400</td>\n",
       "      <td>1.300</td>\n",
       "      <td>3.200</td>\n",
       "      <td>4.500</td>\n",
       "      <td>0.122</td>\n",
       "      <td>-1.700</td>\n",
       "      <td>2.800</td>\n",
       "      <td>1.100</td>\n",
       "      <td>1.40</td>\n",
       "      <td>-1.97</td>\n",
       "      <td>1.62</td>\n",
       "      <td>-0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>0.547</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4245.0</td>\n",
       "      <td>19.400</td>\n",
       "      <td>4.900</td>\n",
       "      <td>4.500</td>\n",
       "      <td>9.400</td>\n",
       "      <td>0.172</td>\n",
       "      <td>1.500</td>\n",
       "      <td>2.600</td>\n",
       "      <td>4.100</td>\n",
       "      <td>4.10</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lamar Patterson</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5359.0</td>\n",
       "      <td>5.100</td>\n",
       "      <td>-0.300</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.025</td>\n",
       "      <td>-4.600</td>\n",
       "      <td>-0.300</td>\n",
       "      <td>-4.900</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Boris Diaw</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3724.0</td>\n",
       "      <td>14.000</td>\n",
       "      <td>1.600</td>\n",
       "      <td>2.200</td>\n",
       "      <td>3.800</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.200</td>\n",
       "      <td>1.10</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mike Scott</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5077.0</td>\n",
       "      <td>14.000</td>\n",
       "      <td>1.700</td>\n",
       "      <td>1.500</td>\n",
       "      <td>3.200</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Paul Millsap</td>\n",
       "      <td>0.505</td>\n",
       "      <td>1.7</td>\n",
       "      <td>4175.0</td>\n",
       "      <td>21.300</td>\n",
       "      <td>4.100</td>\n",
       "      <td>6.000</td>\n",
       "      <td>10.100</td>\n",
       "      <td>0.183</td>\n",
       "      <td>1.100</td>\n",
       "      <td>4.200</td>\n",
       "      <td>5.300</td>\n",
       "      <td>4.90</td>\n",
       "      <td>1.98</td>\n",
       "      <td>3.26</td>\n",
       "      <td>5.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kyle Korver</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3754.0</td>\n",
       "      <td>9.700</td>\n",
       "      <td>1.200</td>\n",
       "      <td>2.900</td>\n",
       "      <td>4.100</td>\n",
       "      <td>0.082</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>0.500</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>1.10</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dennis Schroder</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5187.0</td>\n",
       "      <td>15.500</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>2.300</td>\n",
       "      <td>2.200</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.900</td>\n",
       "      <td>-1.400</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DeMarre Carroll</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>11.900</td>\n",
       "      <td>0.200</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.200</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Zaza Pachulia</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3745.0</td>\n",
       "      <td>16.200</td>\n",
       "      <td>3.400</td>\n",
       "      <td>2.700</td>\n",
       "      <td>6.000</td>\n",
       "      <td>0.145</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>2.200</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.70</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>2.59</td>\n",
       "      <td>1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Jeff Teague</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4624.0</td>\n",
       "      <td>17.900</td>\n",
       "      <td>2.800</td>\n",
       "      <td>3.000</td>\n",
       "      <td>5.900</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1.200</td>\n",
       "      <td>-0.900</td>\n",
       "      <td>0.300</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.12</td>\n",
       "      <td>-1.81</td>\n",
       "      <td>-0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4916.0</td>\n",
       "      <td>13.600</td>\n",
       "      <td>0.200</td>\n",
       "      <td>1.200</td>\n",
       "      <td>1.300</td>\n",
       "      <td>0.061</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-1.200</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Devin Harris</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3822.0</td>\n",
       "      <td>13.100</td>\n",
       "      <td>1.200</td>\n",
       "      <td>1.300</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.095</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Jamal Crawford</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3407.0</td>\n",
       "      <td>14.000</td>\n",
       "      <td>1.800</td>\n",
       "      <td>1.500</td>\n",
       "      <td>3.300</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-2.400</td>\n",
       "      <td>-2.600</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-2.72</td>\n",
       "      <td>-2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Anthony Morrow</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.1</td>\n",
       "      <td>4527.0</td>\n",
       "      <td>10.700</td>\n",
       "      <td>1.400</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.900</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.500</td>\n",
       "      <td>-3.200</td>\n",
       "      <td>-2.700</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-3.05</td>\n",
       "      <td>-2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kirk Hinrich</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3710.0</td>\n",
       "      <td>7.200</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.044</td>\n",
       "      <td>-2.700</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-3.700</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>James Anderson</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4751.0</td>\n",
       "      <td>6.000</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>0.400</td>\n",
       "      <td>-0.300</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>-3.900</td>\n",
       "      <td>-0.300</td>\n",
       "      <td>-4.200</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-3.45</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>-3.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Nazr Mohammed</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>6.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>-7.400</td>\n",
       "      <td>-5.900</td>\n",
       "      <td>-13.200</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>0.39</td>\n",
       "      <td>-0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Justin Holiday</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5142.0</td>\n",
       "      <td>9.400</td>\n",
       "      <td>-0.300</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.040</td>\n",
       "      <td>-2.700</td>\n",
       "      <td>0.300</td>\n",
       "      <td>-2.500</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-2.15</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>John Jenkins</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5057.0</td>\n",
       "      <td>10.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-3.000</td>\n",
       "      <td>-2.400</td>\n",
       "      <td>-5.400</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Louis Williams</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3971.0</td>\n",
       "      <td>13.502</td>\n",
       "      <td>1.395</td>\n",
       "      <td>1.296</td>\n",
       "      <td>2.688</td>\n",
       "      <td>0.083</td>\n",
       "      <td>-1.116</td>\n",
       "      <td>-0.345</td>\n",
       "      <td>-1.459</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mike Muscala</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5214.0</td>\n",
       "      <td>13.600</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.900</td>\n",
       "      <td>1.600</td>\n",
       "      <td>0.136</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>1.500</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>0.57</td>\n",
       "      <td>-0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Tiago Splitter</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4306.0</td>\n",
       "      <td>13.700</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.900</td>\n",
       "      <td>1.700</td>\n",
       "      <td>0.144</td>\n",
       "      <td>-1.900</td>\n",
       "      <td>1.100</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Anthony Tolliver</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4371.0</td>\n",
       "      <td>10.200</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1.300</td>\n",
       "      <td>2.800</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>-0.41</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>Alec Burks</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4893.0</td>\n",
       "      <td>14.300</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.800</td>\n",
       "      <td>1.500</td>\n",
       "      <td>0.090</td>\n",
       "      <td>-0.900</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>-2.500</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-1.02</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>-2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>Trevor Booker</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4754.0</td>\n",
       "      <td>14.400</td>\n",
       "      <td>1.800</td>\n",
       "      <td>2.600</td>\n",
       "      <td>4.300</td>\n",
       "      <td>0.128</td>\n",
       "      <td>-0.900</td>\n",
       "      <td>2.300</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.40</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>2.37</td>\n",
       "      <td>1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>Gordon Hayward</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4724.0</td>\n",
       "      <td>18.300</td>\n",
       "      <td>5.500</td>\n",
       "      <td>3.400</td>\n",
       "      <td>8.900</td>\n",
       "      <td>0.147</td>\n",
       "      <td>2.600</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>2.500</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.92</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>Trey Burke</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5160.0</td>\n",
       "      <td>14.000</td>\n",
       "      <td>0.900</td>\n",
       "      <td>1.200</td>\n",
       "      <td>2.100</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.400</td>\n",
       "      <td>-2.500</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-2.89</td>\n",
       "      <td>-3.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>Rodney Hood</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5334.0</td>\n",
       "      <td>14.100</td>\n",
       "      <td>3.400</td>\n",
       "      <td>2.500</td>\n",
       "      <td>6.000</td>\n",
       "      <td>0.112</td>\n",
       "      <td>1.600</td>\n",
       "      <td>-0.900</td>\n",
       "      <td>0.700</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.21</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5472.0</td>\n",
       "      <td>11.600</td>\n",
       "      <td>0.600</td>\n",
       "      <td>1.700</td>\n",
       "      <td>2.300</td>\n",
       "      <td>0.079</td>\n",
       "      <td>-2.400</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>-2.800</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>-2.10</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>-2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>Rudy Gobert</td>\n",
       "      <td>0.502</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5197.0</td>\n",
       "      <td>17.500</td>\n",
       "      <td>2.600</td>\n",
       "      <td>3.800</td>\n",
       "      <td>6.400</td>\n",
       "      <td>0.160</td>\n",
       "      <td>-1.100</td>\n",
       "      <td>4.800</td>\n",
       "      <td>3.700</td>\n",
       "      <td>2.80</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>3.89</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5217.0</td>\n",
       "      <td>10.200</td>\n",
       "      <td>0.200</td>\n",
       "      <td>1.600</td>\n",
       "      <td>1.800</td>\n",
       "      <td>0.058</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>-2.300</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>-0.52</td>\n",
       "      <td>-2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4762.0</td>\n",
       "      <td>3.500</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>0.100</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.041</td>\n",
       "      <td>-7.000</td>\n",
       "      <td>-1.100</td>\n",
       "      <td>-8.100</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>0.42</td>\n",
       "      <td>-0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>JJ O'Brien</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5533.0</td>\n",
       "      <td>13.502</td>\n",
       "      <td>1.395</td>\n",
       "      <td>1.296</td>\n",
       "      <td>2.688</td>\n",
       "      <td>0.083</td>\n",
       "      <td>-1.116</td>\n",
       "      <td>-0.345</td>\n",
       "      <td>-1.459</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Marc Gasol</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4325.0</td>\n",
       "      <td>17.700</td>\n",
       "      <td>1.800</td>\n",
       "      <td>1.900</td>\n",
       "      <td>3.700</td>\n",
       "      <td>0.098</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>1.700</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.30</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Mike Conley</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4246.0</td>\n",
       "      <td>19.400</td>\n",
       "      <td>4.200</td>\n",
       "      <td>1.100</td>\n",
       "      <td>5.300</td>\n",
       "      <td>0.145</td>\n",
       "      <td>3.700</td>\n",
       "      <td>-1.900</td>\n",
       "      <td>1.700</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>PJ Hairston</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5337.0</td>\n",
       "      <td>13.502</td>\n",
       "      <td>1.395</td>\n",
       "      <td>1.296</td>\n",
       "      <td>2.688</td>\n",
       "      <td>0.083</td>\n",
       "      <td>-1.116</td>\n",
       "      <td>-0.345</td>\n",
       "      <td>-1.459</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Jarell Martin</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5485.0</td>\n",
       "      <td>14.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.100</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>-3.200</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Xavier Munford</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5599.0</td>\n",
       "      <td>9.900</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.017</td>\n",
       "      <td>-2.900</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-3.100</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-2.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>Jordan Adams</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5333.0</td>\n",
       "      <td>17.300</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.015</td>\n",
       "      <td>-2.900</td>\n",
       "      <td>4.800</td>\n",
       "      <td>1.900</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>-1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>Bradley Beal</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5009.0</td>\n",
       "      <td>15.500</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.300</td>\n",
       "      <td>2.700</td>\n",
       "      <td>0.076</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.29</td>\n",
       "      <td>-2.53</td>\n",
       "      <td>-2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>John Wall</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.8</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>19.800</td>\n",
       "      <td>2.200</td>\n",
       "      <td>3.500</td>\n",
       "      <td>5.700</td>\n",
       "      <td>0.098</td>\n",
       "      <td>2.200</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.200</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>Otto Porter</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5154.0</td>\n",
       "      <td>14.500</td>\n",
       "      <td>3.000</td>\n",
       "      <td>2.700</td>\n",
       "      <td>5.600</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.800</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.800</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>Kelly Oubre</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.1</td>\n",
       "      <td>5475.0</td>\n",
       "      <td>8.200</td>\n",
       "      <td>-0.300</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-4.300</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>-5.800</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>Andre Drummond</td>\n",
       "      <td>0.522</td>\n",
       "      <td>1.4</td>\n",
       "      <td>5015.0</td>\n",
       "      <td>21.200</td>\n",
       "      <td>2.000</td>\n",
       "      <td>5.500</td>\n",
       "      <td>7.400</td>\n",
       "      <td>0.133</td>\n",
       "      <td>-2.100</td>\n",
       "      <td>1.600</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>3.16</td>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>Kentavious Caldwell-Pope</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5159.0</td>\n",
       "      <td>12.400</td>\n",
       "      <td>2.600</td>\n",
       "      <td>2.700</td>\n",
       "      <td>5.300</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.600</td>\n",
       "      <td>1.100</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>Stanley Johnson</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5468.0</td>\n",
       "      <td>8.700</td>\n",
       "      <td>-1.300</td>\n",
       "      <td>1.900</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.018</td>\n",
       "      <td>-3.100</td>\n",
       "      <td>0.500</td>\n",
       "      <td>-2.600</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-1.92</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>-2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>Darrun Hilliard</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5498.0</td>\n",
       "      <td>10.700</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.066</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>-3.600</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-1.48</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>-2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Spencer Dinwiddie</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5349.0</td>\n",
       "      <td>8.900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.028</td>\n",
       "      <td>-3.100</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>-5.100</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>Cody Zeller</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.9</td>\n",
       "      <td>5155.0</td>\n",
       "      <td>16.100</td>\n",
       "      <td>3.600</td>\n",
       "      <td>2.700</td>\n",
       "      <td>6.300</td>\n",
       "      <td>0.169</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.30</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>Kemba Walker</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4890.0</td>\n",
       "      <td>20.800</td>\n",
       "      <td>6.300</td>\n",
       "      <td>3.600</td>\n",
       "      <td>9.900</td>\n",
       "      <td>0.165</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>4.40</td>\n",
       "      <td>2.93</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>2.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>Michael Kidd-Gilchrist</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5008.0</td>\n",
       "      <td>16.900</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.152</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>-0.700</td>\n",
       "      <td>-1.400</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.78</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>Frank Kaminsky</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5469.0</td>\n",
       "      <td>12.500</td>\n",
       "      <td>1.200</td>\n",
       "      <td>2.100</td>\n",
       "      <td>3.300</td>\n",
       "      <td>0.093</td>\n",
       "      <td>-1.600</td>\n",
       "      <td>0.400</td>\n",
       "      <td>-1.200</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>Aaron Harrison</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5523.0</td>\n",
       "      <td>4.300</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.014</td>\n",
       "      <td>-5.600</td>\n",
       "      <td>0.100</td>\n",
       "      <td>-5.500</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-1.63</td>\n",
       "      <td>-0.88</td>\n",
       "      <td>-2.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>472 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         NAME   eFG%  BLK  SV_PLAYER_ID     PER    OWS    DWS  \\\n",
       "0                  Josh Smith  0.502  0.0        3834.0  10.600 -1.200  1.200   \n",
       "1             Marvin Williams  0.554  1.0        3928.0  16.800  4.700  3.200   \n",
       "2                 Jason Terry  0.502  0.1        3333.0  10.200  1.100  0.400   \n",
       "3               Kent Bazemore  0.517  0.5        5102.0  13.400  0.300  3.800   \n",
       "4                 Joe Johnson  0.509  0.0        3520.0  12.000  1.900  0.800   \n",
       "5                Tim Hardaway  0.502  0.0        5194.0  11.700  0.900  1.000   \n",
       "6             Thabo Sefolosha  0.502  0.5        4141.0  12.400  1.300  3.200   \n",
       "7                  Al Horford  0.547  1.5        4245.0  19.400  4.900  4.500   \n",
       "8             Lamar Patterson  0.502  0.0        5359.0   5.100 -0.300  0.500   \n",
       "9                  Boris Diaw  0.502  0.3        3724.0  14.000  1.600  2.200   \n",
       "10                 Mike Scott  0.502  0.2        5077.0  14.000  1.700  1.500   \n",
       "11               Paul Millsap  0.505  1.7        4175.0  21.300  4.100  6.000   \n",
       "12                Kyle Korver  0.502  0.4        3754.0   9.700  1.200  2.900   \n",
       "13            Dennis Schroder  0.471  0.1        5187.0  15.500 -0.200  2.300   \n",
       "14            DeMarre Carroll  0.502  0.0        4632.0  11.900  0.200  1.000   \n",
       "15              Zaza Pachulia  0.502  0.3        3745.0  16.200  3.400  2.700   \n",
       "16                Jeff Teague  0.495  0.3        4624.0  17.900  2.800  3.000   \n",
       "17               Shelvin Mack  0.502  0.0        4916.0  13.600  0.200  1.200   \n",
       "18               Devin Harris  0.502  0.2        3822.0  13.100  1.200  1.300   \n",
       "19             Jamal Crawford  0.466  0.2        3407.0  14.000  1.800  1.500   \n",
       "20             Anthony Morrow  0.502  0.1        4527.0  10.700  1.400  0.500   \n",
       "21               Kirk Hinrich  0.502  0.0        3710.0   7.200  0.100  0.500   \n",
       "22             James Anderson  0.502  0.0        4751.0   6.000 -0.600  0.400   \n",
       "23              Nazr Mohammed  0.502  0.0        3272.0   6.000  0.000  0.000   \n",
       "24             Justin Holiday  0.502  0.0        5142.0   9.400 -0.300  0.900   \n",
       "25               John Jenkins  0.502  0.0        5057.0  10.000  0.000  0.200   \n",
       "26             Louis Williams  0.502  0.0        3971.0  13.502  1.395  1.296   \n",
       "27               Mike Muscala  0.502  0.5        5214.0  13.600  0.700  0.900   \n",
       "28             Tiago Splitter  0.502  0.0        4306.0  13.700  0.900  0.900   \n",
       "29           Anthony Tolliver  0.502  0.2        4371.0  10.200  1.500  1.300   \n",
       "..                        ...    ...  ...           ...     ...    ...    ...   \n",
       "442                Alec Burks  0.502  0.0        4893.0  14.300  0.700  0.800   \n",
       "443             Trevor Booker  0.502  0.5        4754.0  14.400  1.800  2.600   \n",
       "444            Gordon Hayward  0.493  0.3        4724.0  18.300  5.500  3.400   \n",
       "445                Trey Burke  0.502  0.1        5160.0  14.000  0.900  1.200   \n",
       "446               Rodney Hood  0.503  0.2        5334.0  14.100  3.400  2.500   \n",
       "447                Trey Lyles  0.502  0.2        5472.0  11.600  0.600  1.700   \n",
       "448               Rudy Gobert  0.502  2.2        5197.0  17.500  2.600  3.800   \n",
       "449                 Raul Neto  0.502  0.0        5217.0  10.200  0.200  1.600   \n",
       "450              Tibor Pleiss  0.502  0.0        4762.0   3.500 -0.200  0.100   \n",
       "451                JJ O'Brien  0.502  0.0        5533.0  13.502  1.395  1.296   \n",
       "452                Marc Gasol  0.465  0.0        4325.0  17.700  1.800  1.900   \n",
       "453               Mike Conley  0.502  0.0        4246.0  19.400  4.200  1.100   \n",
       "454               PJ Hairston  0.502  0.0        5337.0  13.502  1.395  1.296   \n",
       "455             Jarell Martin  0.502  0.0        5485.0  14.000  0.500  0.200   \n",
       "456            Xavier Munford  0.502  0.0        5599.0   9.900 -0.200  0.200   \n",
       "457              Jordan Adams  0.502  0.0        5333.0  17.300  0.000  0.000   \n",
       "458              Bradley Beal  0.515  0.0        5009.0  15.500  1.400  1.300   \n",
       "459                 John Wall  0.467  0.8        4716.0  19.800  2.200  3.500   \n",
       "460               Otto Porter  0.541  0.4        5154.0  14.500  3.000  2.700   \n",
       "461               Kelly Oubre  0.502  0.1        5475.0   8.200 -0.300  0.700   \n",
       "462            Andre Drummond  0.522  1.4        5015.0  21.200  2.000  5.500   \n",
       "463  Kentavious Caldwell-Pope  0.479  0.2        5159.0  12.400  2.600  2.700   \n",
       "464           Stanley Johnson  0.502  0.2        5468.0   8.700 -1.300  1.900   \n",
       "465           Darrun Hilliard  0.502  0.0        5498.0  10.700  0.200  0.300   \n",
       "466         Spencer Dinwiddie  0.502  0.0        5349.0   8.900  0.000  0.100   \n",
       "467               Cody Zeller  0.502  0.9        5155.0  16.100  3.600  2.700   \n",
       "468              Kemba Walker  0.495  0.5        4890.0  20.800  6.300  3.600   \n",
       "469    Michael Kidd-Gilchrist  0.502  0.0        5008.0  16.900  0.400  0.200   \n",
       "470            Frank Kaminsky  0.502  0.5        5469.0  12.500  1.200  2.100   \n",
       "471            Aaron Harrison  0.502  0.0        5523.0   4.300 -0.200  0.100   \n",
       "\n",
       "         WS  WS/48   OBPM   DBPM     BPM  VORP  ORPM  DRPM   RPM  \n",
       "0     0.000  0.000 -3.600  1.600  -2.000  0.00 -3.14  1.05 -2.09  \n",
       "1     7.800  0.161  1.700  1.000   2.700  2.70  2.08  0.59  2.67  \n",
       "2     1.500  0.057 -0.200 -2.600  -2.800 -0.30 -0.33 -2.81 -3.14  \n",
       "3     4.100  0.094 -1.300  1.600   0.300  1.20 -1.49  0.15 -1.34  \n",
       "4     2.700  0.048  0.300 -1.600  -1.300  0.40  0.80 -0.24  0.56  \n",
       "5     1.900  0.105 -0.900 -1.200  -2.100  0.00  0.00  0.00  0.00  \n",
       "6     4.500  0.122 -1.700  2.800   1.100  1.40 -1.97  1.62 -0.35  \n",
       "7     9.400  0.172  1.500  2.600   4.100  4.10  0.89  2.08  2.97  \n",
       "8     0.200  0.025 -4.600 -0.300  -4.900 -0.30 -1.76 -0.53 -2.29  \n",
       "9     3.800  0.132 -0.100  1.400   1.200  1.10 -0.57  0.64  0.07  \n",
       "10    3.200  0.132 -0.200 -0.400  -0.600  0.40  0.32  0.99  1.31  \n",
       "11   10.100  0.183  1.100  4.200   5.300  4.90  1.98  3.26  5.24  \n",
       "12    4.100  0.082 -0.700  0.500  -0.200  1.10 -0.13  1.82  1.69  \n",
       "13    2.200  0.064 -0.500 -0.900  -1.400  0.30  0.88 -0.31  0.57  \n",
       "14    1.200  0.074  0.000  0.300   0.200  0.40 -0.87  0.96  0.09  \n",
       "15    6.000  0.145 -0.800  2.200   1.400  1.70 -1.04  2.59  1.55  \n",
       "16    5.900  0.125  1.200 -0.900   0.300  1.30  1.12 -1.81 -0.69  \n",
       "17    1.300  0.061 -0.500 -0.800  -1.200  0.20 -0.27 -0.46 -0.73  \n",
       "18    2.500  0.095 -0.100  0.400   0.300  0.70 -1.52 -0.12 -1.64  \n",
       "19    3.300  0.076 -0.200 -2.400  -2.600 -0.30  0.41 -2.72 -2.31  \n",
       "20    1.900  0.097  0.500 -3.200  -2.700 -0.20  0.46 -3.05 -2.59  \n",
       "21    0.600  0.044 -2.700 -1.000  -3.700 -0.30 -1.56  0.03 -1.53  \n",
       "22   -0.300 -0.017 -3.900 -0.300  -4.200 -0.40 -3.45 -0.40 -3.85  \n",
       "23    0.000 -0.009 -7.400 -5.900 -13.200 -0.10 -1.10  0.39 -0.71  \n",
       "24    0.600  0.040 -2.700  0.300  -2.500 -0.10 -2.15 -1.22 -3.37  \n",
       "25    0.200  0.022 -3.000 -2.400  -5.400 -0.40  0.00  0.00  0.00  \n",
       "26    2.688  0.083 -1.116 -0.345  -1.459  0.65  0.00  0.00  0.00  \n",
       "27    1.600  0.136 -2.000  1.500  -0.500  0.20 -1.44  0.57 -0.87  \n",
       "28    1.700  0.144 -1.900  1.100  -0.800  0.20 -0.74  1.99  1.25  \n",
       "29    2.800  0.100  0.000  0.000   0.000  0.70  0.00 -0.41 -0.41  \n",
       "..      ...    ...    ...    ...     ...   ...   ...   ...   ...  \n",
       "442   1.500  0.090 -0.900 -1.600  -2.500 -0.10 -1.02 -0.99 -2.01  \n",
       "443   4.300  0.128 -0.900  2.300   1.400  1.40 -0.82  2.37  1.55  \n",
       "444   8.900  0.147  2.600 -0.100   2.500  3.30  2.92 -0.02  2.90  \n",
       "445   2.100  0.074  0.400 -2.500  -2.000  0.00 -0.24 -2.89 -3.13  \n",
       "446   6.000  0.112  1.600 -0.900   0.700  1.70  2.21 -0.98  1.23  \n",
       "447   2.300  0.079 -2.400 -0.400  -2.800 -0.30 -2.10 -0.28 -2.38  \n",
       "448   6.400  0.160 -1.100  4.800   3.700  2.80 -1.55  3.89  2.34  \n",
       "449   1.800  0.058 -1.600 -0.700  -2.300 -0.10 -1.51 -0.52 -2.03  \n",
       "450  -0.100 -0.041 -7.000 -1.100  -8.100 -0.10 -1.10  0.42 -0.68  \n",
       "451   2.688  0.083 -1.116 -0.345  -1.459  0.65  0.00  0.00  0.00  \n",
       "452   3.700  0.098 -0.700  1.700   1.000  1.30 -0.90  2.01  1.11  \n",
       "453   5.300  0.145  3.700 -1.900   1.700  1.70  2.78  0.08  2.86  \n",
       "454   2.688  0.083 -1.116 -0.345  -1.459  0.65  0.00  0.00  0.00  \n",
       "455   0.800  0.100 -1.600 -1.600  -3.200 -0.10 -1.26 -0.80 -2.06  \n",
       "456   0.100  0.017 -2.900 -0.100  -3.100 -0.10 -1.18 -1.34 -2.52  \n",
       "457   0.000  0.015 -2.900  4.800   1.900  0.00 -1.21 -0.59 -1.80  \n",
       "458   2.700  0.076  1.000 -1.600  -0.600  0.60  0.29 -2.53 -2.24  \n",
       "459   5.700  0.098  2.200  1.000   3.200  3.70  2.60  0.12  2.72  \n",
       "460   5.600  0.119  0.800  1.000   1.800  2.20  0.00  0.00  0.00  \n",
       "461   0.400  0.031 -4.300 -1.600  -5.800 -0.70  0.00  0.00  0.00  \n",
       "462   7.400  0.133 -2.100  1.600  -0.500  1.00 -0.19  3.16  2.97  \n",
       "463   5.300  0.092  0.600  0.600   1.100  2.20  0.85  0.52  1.37  \n",
       "464   0.600  0.018 -3.100  0.500  -2.600 -0.20 -1.92 -0.78 -2.70  \n",
       "465   0.500  0.066 -2.000 -1.600  -3.600 -0.20 -1.48 -0.71 -2.19  \n",
       "466   0.100  0.028 -3.100 -2.000  -5.100 -0.10  0.00  0.00  0.00  \n",
       "467   6.300  0.169 -1.000  2.000   1.000  1.30 -0.31  2.26  1.95  \n",
       "468   9.900  0.165  4.000  0.000   4.000  4.40  2.93 -0.35  2.58  \n",
       "469   0.600  0.152 -0.700 -0.700  -1.400  0.00 -0.78  0.31 -0.47  \n",
       "470   3.300  0.093 -1.600  0.400  -1.200  0.30 -1.21 -0.23 -1.44  \n",
       "471   0.000 -0.014 -5.600  0.100  -5.500 -0.10 -1.63 -0.88 -2.51  \n",
       "\n",
       "[472 rows x 16 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prep Shot DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (0,1,2,3,5,6,8,9,11,12,13,14,15,16,18,19,20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#prep shot dataframe\n",
    "shot_df = pd.read_csv(\"Hackathon_sv_shot_summary_2015-16.txt\", sep=\" \")\n",
    "\n",
    "#have 165 occurences of null for closet defender id\n",
    "shot_df = shot_df.replace({'(null)': -1})\n",
    "\n",
    "#shot_df = shot_df.drop(shot_df.index[222316])\n",
    "shot_df = shot_df[shot_df['SEASON'] != \"GAME_ID\"]\n",
    "\n",
    "#convert columns to numbers\n",
    "shot_df[[\"SEASON\", \"GAME_ID\", \"SV_GAME_ID\", \"TEAM_ID\", \"SV_TEAM_ID\", \"PERSON_ID\", \"SV_PLAYER_ID\", \"PERIOD\", \"GAME_CLOCK\", \"WALL_CLOCK\", \"DRIBBLES\", \"SHOT_DIST\", \"TOUCH_TIME\", \"CLOSE_DEF_PERSON_ID\", \"CLOSE_DEF_SV_PLAYER_ID\", \"CLOSE_DEF_DIST\", \"PT_VALUE\", \"PTS\"]] = shot_df[[\"SEASON\", \"GAME_ID\", \"SV_GAME_ID\", \"TEAM_ID\", \"SV_TEAM_ID\", \"PERSON_ID\", \"SV_PLAYER_ID\", \"PERIOD\", \"GAME_CLOCK\", \"WALL_CLOCK\", \"DRIBBLES\", \"SHOT_DIST\", \"TOUCH_TIME\", \"CLOSE_DEF_PERSON_ID\", \"CLOSE_DEF_SV_PLAYER_ID\", \"CLOSE_DEF_DIST\", \"PT_VALUE\", \"PTS\"]].apply(pd.to_numeric)\n",
    "\n",
    "#convert missed/made to 0/1\n",
    "shot_df['SHOT_RESULT'] = shot_df['SHOT_RESULT'].replace({'missed': 0})\n",
    "shot_df['SHOT_RESULT'] = shot_df['SHOT_RESULT'].replace({'made': 1})\n",
    "\n",
    "#classify shot distances\n",
    "shot_df.loc[shot_df['PT_VALUE'] == 3, 'SHOT_TYPE'] = \"Three Pointer\"\n",
    "shot_df.loc[(shot_df['SHOT_DIST'] >= 15) & (shot_df['PT_VALUE'] == 2), 'SHOT_TYPE'] = \"Long Two-Pointer\"\n",
    "shot_df.loc[(shot_df['SHOT_DIST'] >= 5) & (shot_df['SHOT_DIST'] < 15), 'SHOT_TYPE'] = \"Midrange\"\n",
    "shot_df.loc[shot_df['SHOT_DIST']< 5, 'SHOT_TYPE'] = \"Layup\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep only select shot data\n",
    "shot_df = shot_df[['SHOT_RESULT', \"SHOT_DIST\", \"DRIBBLES\", \"CLOSE_DEF_DIST\", \"TOUCH_TIME\", \"SV_PLAYER_ID\", \"CLOSE_DEF_SV_PLAYER_ID\"]]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>SHOT_RESULT</th>\n",
       "      <th>SHOT_DIST</th>\n",
       "      <th>DRIBBLES</th>\n",
       "      <th>CLOSE_DEF_DIST</th>\n",
       "      <th>TOUCH_TIME</th>\n",
       "      <th>SV_PLAYER_ID</th>\n",
       "      <th>CLOSE_DEF_SV_PLAYER_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>40.10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.87</td>\n",
       "      <td>3741</td>\n",
       "      <td>5359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2.43</td>\n",
       "      <td>16</td>\n",
       "      <td>2.90</td>\n",
       "      <td>16.96</td>\n",
       "      <td>5187</td>\n",
       "      <td>4895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>21.09</td>\n",
       "      <td>0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>0.84</td>\n",
       "      <td>5359</td>\n",
       "      <td>4895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4.60</td>\n",
       "      <td>13</td>\n",
       "      <td>0.97</td>\n",
       "      <td>9.88</td>\n",
       "      <td>5187</td>\n",
       "      <td>4895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>22.59</td>\n",
       "      <td>3</td>\n",
       "      <td>4.52</td>\n",
       "      <td>5.42</td>\n",
       "      <td>4895</td>\n",
       "      <td>4245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>5.22</td>\n",
       "      <td>1</td>\n",
       "      <td>1.88</td>\n",
       "      <td>2.45</td>\n",
       "      <td>5187</td>\n",
       "      <td>5150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>22.64</td>\n",
       "      <td>0</td>\n",
       "      <td>5.77</td>\n",
       "      <td>0.80</td>\n",
       "      <td>4175</td>\n",
       "      <td>4895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>9.66</td>\n",
       "      <td>1</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1.80</td>\n",
       "      <td>4175</td>\n",
       "      <td>5468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>3.77</td>\n",
       "      <td>10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>9.38</td>\n",
       "      <td>5187</td>\n",
       "      <td>4895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>24.60</td>\n",
       "      <td>0</td>\n",
       "      <td>5.38</td>\n",
       "      <td>0.76</td>\n",
       "      <td>4895</td>\n",
       "      <td>4141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1.96</td>\n",
       "      <td>3</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.52</td>\n",
       "      <td>4141</td>\n",
       "      <td>5015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>5015</td>\n",
       "      <td>4141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>19.53</td>\n",
       "      <td>8</td>\n",
       "      <td>8.51</td>\n",
       "      <td>6.94</td>\n",
       "      <td>4141</td>\n",
       "      <td>5159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>21.56</td>\n",
       "      <td>0</td>\n",
       "      <td>6.46</td>\n",
       "      <td>0.72</td>\n",
       "      <td>5468</td>\n",
       "      <td>5359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0</td>\n",
       "      <td>4.83</td>\n",
       "      <td>0.72</td>\n",
       "      <td>5187</td>\n",
       "      <td>4906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>8.47</td>\n",
       "      <td>3</td>\n",
       "      <td>1.65</td>\n",
       "      <td>3.47</td>\n",
       "      <td>4906</td>\n",
       "      <td>5187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4175</td>\n",
       "      <td>5015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>3.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.35</td>\n",
       "      <td>4306</td>\n",
       "      <td>5015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>11.48</td>\n",
       "      <td>2</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2.98</td>\n",
       "      <td>5159</td>\n",
       "      <td>5359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>5.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.53</td>\n",
       "      <td>4895</td>\n",
       "      <td>4306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>0.19</td>\n",
       "      <td>5.76</td>\n",
       "      <td>4895</td>\n",
       "      <td>4306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4306</td>\n",
       "      <td>3962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>13.13</td>\n",
       "      <td>17</td>\n",
       "      <td>4.01</td>\n",
       "      <td>15.32</td>\n",
       "      <td>4624</td>\n",
       "      <td>4906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>23.64</td>\n",
       "      <td>0</td>\n",
       "      <td>6.47</td>\n",
       "      <td>0.64</td>\n",
       "      <td>5159</td>\n",
       "      <td>3754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>21.69</td>\n",
       "      <td>0</td>\n",
       "      <td>14.89</td>\n",
       "      <td>1.21</td>\n",
       "      <td>4141</td>\n",
       "      <td>5015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>17.13</td>\n",
       "      <td>0</td>\n",
       "      <td>3.38</td>\n",
       "      <td>1.66</td>\n",
       "      <td>4895</td>\n",
       "      <td>4141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>24.51</td>\n",
       "      <td>0</td>\n",
       "      <td>5.22</td>\n",
       "      <td>0.84</td>\n",
       "      <td>3962</td>\n",
       "      <td>4175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>11.55</td>\n",
       "      <td>3</td>\n",
       "      <td>4.57</td>\n",
       "      <td>4.79</td>\n",
       "      <td>4175</td>\n",
       "      <td>3962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>4.25</td>\n",
       "      <td>3</td>\n",
       "      <td>3.82</td>\n",
       "      <td>2.89</td>\n",
       "      <td>4895</td>\n",
       "      <td>5102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>20.83</td>\n",
       "      <td>0</td>\n",
       "      <td>5.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4245</td>\n",
       "      <td>5015</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222288</th>\n",
       "      <td>0</td>\n",
       "      <td>5.39</td>\n",
       "      <td>13</td>\n",
       "      <td>3.07</td>\n",
       "      <td>12.07</td>\n",
       "      <td>4840</td>\n",
       "      <td>5069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222289</th>\n",
       "      <td>0</td>\n",
       "      <td>25.75</td>\n",
       "      <td>0</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.94</td>\n",
       "      <td>5069</td>\n",
       "      <td>4391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222290</th>\n",
       "      <td>0</td>\n",
       "      <td>11.15</td>\n",
       "      <td>2</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.17</td>\n",
       "      <td>4391</td>\n",
       "      <td>5013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222291</th>\n",
       "      <td>0</td>\n",
       "      <td>22.41</td>\n",
       "      <td>0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.81</td>\n",
       "      <td>3826</td>\n",
       "      <td>4391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222292</th>\n",
       "      <td>0</td>\n",
       "      <td>4.99</td>\n",
       "      <td>24</td>\n",
       "      <td>3.44</td>\n",
       "      <td>20.43</td>\n",
       "      <td>3704</td>\n",
       "      <td>3826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222293</th>\n",
       "      <td>0</td>\n",
       "      <td>15.55</td>\n",
       "      <td>2</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4892</td>\n",
       "      <td>4840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222294</th>\n",
       "      <td>0</td>\n",
       "      <td>12.12</td>\n",
       "      <td>4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>7.10</td>\n",
       "      <td>3704</td>\n",
       "      <td>4612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222295</th>\n",
       "      <td>0</td>\n",
       "      <td>27.82</td>\n",
       "      <td>0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>1.08</td>\n",
       "      <td>4612</td>\n",
       "      <td>4840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222296</th>\n",
       "      <td>0</td>\n",
       "      <td>22.02</td>\n",
       "      <td>20</td>\n",
       "      <td>5.48</td>\n",
       "      <td>19.00</td>\n",
       "      <td>3704</td>\n",
       "      <td>5064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222297</th>\n",
       "      <td>1</td>\n",
       "      <td>5.18</td>\n",
       "      <td>2</td>\n",
       "      <td>5.46</td>\n",
       "      <td>2.73</td>\n",
       "      <td>4892</td>\n",
       "      <td>3835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222298</th>\n",
       "      <td>1</td>\n",
       "      <td>27.70</td>\n",
       "      <td>23</td>\n",
       "      <td>5.46</td>\n",
       "      <td>20.75</td>\n",
       "      <td>3704</td>\n",
       "      <td>5064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222299</th>\n",
       "      <td>1</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.04</td>\n",
       "      <td>5069</td>\n",
       "      <td>4884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222300</th>\n",
       "      <td>0</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6</td>\n",
       "      <td>2.40</td>\n",
       "      <td>5.97</td>\n",
       "      <td>4612</td>\n",
       "      <td>3523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222301</th>\n",
       "      <td>0</td>\n",
       "      <td>17.56</td>\n",
       "      <td>0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.81</td>\n",
       "      <td>3523</td>\n",
       "      <td>4612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222302</th>\n",
       "      <td>1</td>\n",
       "      <td>22.58</td>\n",
       "      <td>0</td>\n",
       "      <td>3.33</td>\n",
       "      <td>1.04</td>\n",
       "      <td>4892</td>\n",
       "      <td>4898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222303</th>\n",
       "      <td>0</td>\n",
       "      <td>24.87</td>\n",
       "      <td>1</td>\n",
       "      <td>5.50</td>\n",
       "      <td>1.72</td>\n",
       "      <td>3835</td>\n",
       "      <td>5013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222304</th>\n",
       "      <td>1</td>\n",
       "      <td>28.66</td>\n",
       "      <td>3</td>\n",
       "      <td>5.20</td>\n",
       "      <td>3.52</td>\n",
       "      <td>4612</td>\n",
       "      <td>4884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222305</th>\n",
       "      <td>1</td>\n",
       "      <td>6.58</td>\n",
       "      <td>21</td>\n",
       "      <td>3.24</td>\n",
       "      <td>18.32</td>\n",
       "      <td>3704</td>\n",
       "      <td>3826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222306</th>\n",
       "      <td>0</td>\n",
       "      <td>13.25</td>\n",
       "      <td>11</td>\n",
       "      <td>2.75</td>\n",
       "      <td>9.78</td>\n",
       "      <td>4840</td>\n",
       "      <td>4892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222307</th>\n",
       "      <td>0</td>\n",
       "      <td>24.87</td>\n",
       "      <td>0</td>\n",
       "      <td>5.48</td>\n",
       "      <td>0.84</td>\n",
       "      <td>5013</td>\n",
       "      <td>3523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222308</th>\n",
       "      <td>0</td>\n",
       "      <td>19.22</td>\n",
       "      <td>0</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0.73</td>\n",
       "      <td>4391</td>\n",
       "      <td>5069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222309</th>\n",
       "      <td>1</td>\n",
       "      <td>2.29</td>\n",
       "      <td>4</td>\n",
       "      <td>1.15</td>\n",
       "      <td>7.65</td>\n",
       "      <td>5069</td>\n",
       "      <td>3704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222310</th>\n",
       "      <td>1</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4840</td>\n",
       "      <td>5069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222311</th>\n",
       "      <td>0</td>\n",
       "      <td>6.31</td>\n",
       "      <td>2</td>\n",
       "      <td>6.44</td>\n",
       "      <td>5.87</td>\n",
       "      <td>4840</td>\n",
       "      <td>5069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222312</th>\n",
       "      <td>0</td>\n",
       "      <td>24.61</td>\n",
       "      <td>0</td>\n",
       "      <td>3.97</td>\n",
       "      <td>0.84</td>\n",
       "      <td>4892</td>\n",
       "      <td>4391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222313</th>\n",
       "      <td>0</td>\n",
       "      <td>16.99</td>\n",
       "      <td>1</td>\n",
       "      <td>10.03</td>\n",
       "      <td>2.08</td>\n",
       "      <td>5013</td>\n",
       "      <td>4391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222314</th>\n",
       "      <td>1</td>\n",
       "      <td>7.19</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>4391</td>\n",
       "      <td>4892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222315</th>\n",
       "      <td>0</td>\n",
       "      <td>29.17</td>\n",
       "      <td>0</td>\n",
       "      <td>7.03</td>\n",
       "      <td>0.93</td>\n",
       "      <td>3704</td>\n",
       "      <td>5069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222316</th>\n",
       "      <td>0</td>\n",
       "      <td>13.86</td>\n",
       "      <td>7</td>\n",
       "      <td>2.26</td>\n",
       "      <td>9.62</td>\n",
       "      <td>3821</td>\n",
       "      <td>4840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222317</th>\n",
       "      <td>1</td>\n",
       "      <td>6.24</td>\n",
       "      <td>2</td>\n",
       "      <td>2.32</td>\n",
       "      <td>4.03</td>\n",
       "      <td>3704</td>\n",
       "      <td>3821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222317 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SHOT_RESULT  SHOT_DIST  DRIBBLES  CLOSE_DEF_DIST  TOUCH_TIME  \\\n",
       "1                 0      40.10         3            0.68        1.87   \n",
       "2                 0       2.43        16            2.90       16.96   \n",
       "3                 1      21.09         0            4.03        0.84   \n",
       "4                 1       4.60        13            0.97        9.88   \n",
       "5                 0      22.59         3            4.52        5.42   \n",
       "6                 1       5.22         1            1.88        2.45   \n",
       "7                 0      22.64         0            5.77        0.80   \n",
       "8                 0       9.66         1            3.12        1.80   \n",
       "9                 0       3.77        10            2.74        9.38   \n",
       "10                0      24.60         0            5.38        0.76   \n",
       "11                1       1.96         3            1.56        2.52   \n",
       "12                1       3.42         0            1.70        0.68   \n",
       "13                0      19.53         8            8.51        6.94   \n",
       "14                0      21.56         0            6.46        0.72   \n",
       "15                1       1.92         0            4.83        0.72   \n",
       "16                1       8.47         3            1.65        3.47   \n",
       "17                1       2.05         0            0.15        0.04   \n",
       "18                0       3.99         1            1.39        1.35   \n",
       "19                0      11.48         2            2.17        2.98   \n",
       "20                0       5.44         0            1.59        1.53   \n",
       "21                0       0.75         2            0.19        5.76   \n",
       "22                1       1.65         0            1.88        0.04   \n",
       "23                0      13.13        17            4.01       15.32   \n",
       "24                1      23.64         0            6.47        0.64   \n",
       "25                0      21.69         0           14.89        1.21   \n",
       "26                1      17.13         0            3.38        1.66   \n",
       "27                1      24.51         0            5.22        0.84   \n",
       "28                1      11.55         3            4.57        4.79   \n",
       "29                1       4.25         3            3.82        2.89   \n",
       "30                0      20.83         0            5.96        1.00   \n",
       "...             ...        ...       ...             ...         ...   \n",
       "222288            0       5.39        13            3.07       12.07   \n",
       "222289            0      25.75         0            6.81        0.94   \n",
       "222290            0      11.15         2            3.70        2.17   \n",
       "222291            0      22.41         0            3.30        0.81   \n",
       "222292            0       4.99        24            3.44       20.43   \n",
       "222293            0      15.55         2            3.24        3.90   \n",
       "222294            0      12.12         4            2.25        7.10   \n",
       "222295            0      27.82         0            8.80        1.08   \n",
       "222296            0      22.02        20            5.48       19.00   \n",
       "222297            1       5.18         2            5.46        2.73   \n",
       "222298            1      27.70        23            5.46       20.75   \n",
       "222299            1       1.18         0            1.58        0.04   \n",
       "222300            0       6.15         6            2.40        5.97   \n",
       "222301            0      17.56         0            2.85        0.81   \n",
       "222302            1      22.58         0            3.33        1.04   \n",
       "222303            0      24.87         1            5.50        1.72   \n",
       "222304            1      28.66         3            5.20        3.52   \n",
       "222305            1       6.58        21            3.24       18.32   \n",
       "222306            0      13.25        11            2.75        9.78   \n",
       "222307            0      24.87         0            5.48        0.84   \n",
       "222308            0      19.22         0            3.29        0.73   \n",
       "222309            1       2.29         4            1.15        7.65   \n",
       "222310            1       3.50         0            3.34        0.04   \n",
       "222311            0       6.31         2            6.44        5.87   \n",
       "222312            0      24.61         0            3.97        0.84   \n",
       "222313            0      16.99         1           10.03        2.08   \n",
       "222314            1       7.19         0            1.00        0.97   \n",
       "222315            0      29.17         0            7.03        0.93   \n",
       "222316            0      13.86         7            2.26        9.62   \n",
       "222317            1       6.24         2            2.32        4.03   \n",
       "\n",
       "        SV_PLAYER_ID  CLOSE_DEF_SV_PLAYER_ID  \n",
       "1               3741                    5359  \n",
       "2               5187                    4895  \n",
       "3               5359                    4895  \n",
       "4               5187                    4895  \n",
       "5               4895                    4245  \n",
       "6               5187                    5150  \n",
       "7               4175                    4895  \n",
       "8               4175                    5468  \n",
       "9               5187                    4895  \n",
       "10              4895                    4141  \n",
       "11              4141                    5015  \n",
       "12              5015                    4141  \n",
       "13              4141                    5159  \n",
       "14              5468                    5359  \n",
       "15              5187                    4906  \n",
       "16              4906                    5187  \n",
       "17              4175                    5015  \n",
       "18              4306                    5015  \n",
       "19              5159                    5359  \n",
       "20              4895                    4306  \n",
       "21              4895                    4306  \n",
       "22              4306                    3962  \n",
       "23              4624                    4906  \n",
       "24              5159                    3754  \n",
       "25              4141                    5015  \n",
       "26              4895                    4141  \n",
       "27              3962                    4175  \n",
       "28              4175                    3962  \n",
       "29              4895                    5102  \n",
       "30              4245                    5015  \n",
       "...              ...                     ...  \n",
       "222288          4840                    5069  \n",
       "222289          5069                    4391  \n",
       "222290          4391                    5013  \n",
       "222291          3826                    4391  \n",
       "222292          3704                    3826  \n",
       "222293          4892                    4840  \n",
       "222294          3704                    4612  \n",
       "222295          4612                    4840  \n",
       "222296          3704                    5064  \n",
       "222297          4892                    3835  \n",
       "222298          3704                    5064  \n",
       "222299          5069                    4884  \n",
       "222300          4612                    3523  \n",
       "222301          3523                    4612  \n",
       "222302          4892                    4898  \n",
       "222303          3835                    5013  \n",
       "222304          4612                    4884  \n",
       "222305          3704                    3826  \n",
       "222306          4840                    4892  \n",
       "222307          5013                    3523  \n",
       "222308          4391                    5069  \n",
       "222309          5069                    3704  \n",
       "222310          4840                    5069  \n",
       "222311          4840                    5069  \n",
       "222312          4892                    4391  \n",
       "222313          5013                    4391  \n",
       "222314          4391                    4892  \n",
       "222315          3704                    5069  \n",
       "222316          3821                    4840  \n",
       "222317          3704                    3821  \n",
       "\n",
       "[222317 rows x 7 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modify_player_stats(shot_df_in, master_df_in, off_stat, def_stat):\n",
    "    output_df = shot_df_in.copy()\n",
    "    col_names = list(output_df)\n",
    "    output_df = output_df.merge(master_df_in, how = 'left', on = 'SV_PLAYER_ID')\n",
    "    col_names.append(off_stat)\n",
    "    output_df = output_df[col_names]\n",
    "    \n",
    "    output_df = output_df.merge(master_df_in.drop(columns = [off_stat]), how = 'left', left_on = 'CLOSE_DEF_SV_PLAYER_ID', right_on = 'SV_PLAYER_ID')\n",
    "    output_df = output_df.rename(index=str, columns={\"SV_PLAYER_ID_x\": \"SV_PLAYER_ID\"})\n",
    "    output_df = output_df.drop(columns = 'SV_PLAYER_ID_y')\n",
    "    col_names.append(def_stat)\n",
    "    col_names.remove('SV_PLAYER_ID')\n",
    "    col_names.remove('CLOSE_DEF_SV_PLAYER_ID')\n",
    "    return output_df[col_names]\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use this to modify shooter and defender data\n",
    "basic_df = modify_player_stats(shot_df, master_df, 'eFG%', 'BLK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_df = basic_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#base split using eFG and blocks\n",
    "\n",
    "no_name_shot_df = basic_df.drop(columns = ['SV_PLAYER_ID', 'CLOSE_DEF_SV_PLAYER_ID'])\n",
    "X = no_name_shot_df.drop(columns = ['SHOT_RESULT'])\n",
    "Y = no_name_shot_df['SHOT_RESULT']\n",
    "\n",
    "X_train_basic, X_test_basic, Y_train_basic, Y_test_basic = train_test_split(no_name_shot_df.drop(columns = ['SHOT_RESULT']), \n",
    "                                                    no_name_shot_df['SHOT_RESULT'], test_size=0.3, \n",
    "                                                    random_state=101)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.23418654182192408"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train_basic,Y_train_basic)\n",
    "predictions = logmodel.predict_proba(X_test_basic)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.06232577  0.04035023  0.09693277 -0.08071813  2.51424647 -0.13649773]]\n"
     ]
    }
   ],
   "source": [
    "print(logmodel.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BLK</th>\n",
       "      <td>-0.136498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOUCH_TIME</th>\n",
       "      <td>-0.080718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOT_DIST</th>\n",
       "      <td>-0.062326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRIBBLES</th>\n",
       "      <td>0.040350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSE_DEF_DIST</th>\n",
       "      <td>0.096933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eFG%</th>\n",
       "      <td>2.514246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Coefficient\n",
       "BLK               -0.136498\n",
       "TOUCH_TIME        -0.080718\n",
       "SHOT_DIST         -0.062326\n",
       "DRIBBLES           0.040350\n",
       "CLOSE_DEF_DIST     0.096933\n",
       "eFG%               2.514246"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(logmodel.coef_[0],no_name_shot_df.drop('SHOT_RESULT',axis=1).columns,columns=['Coefficient'])\n",
    "coeff_df.sort_values(by=['Coefficient'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()\n",
    "\n",
    "clf = LogisticRegressionCV(cv=5, random_state=101,\n",
    "                          multi_class='multinomial', scoring = 'brier_score_loss').fit(X_train_basic, Y_train_basic)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:1926: ChangedBehaviorWarning: The long-standing behavior to use the accuracy score has changed. The scoring parameter is now used. This warning will disappear in version 0.22.\n",
      "  ChangedBehaviorWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-0.23418763337534576"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = clf.predict_proba(X_test_basic)\n",
    "clf.score(X_test_basic,Y_test_basic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3000726771710131\n"
     ]
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(X_train_basic,Y_train_basic)\n",
    "\n",
    "print(lm.intercept_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.014498    0.00941312  0.02132147 -0.01888836  0.60996841 -0.03184456]\n"
     ]
    }
   ],
   "source": [
    "print(lm.coef_)\n"
   ]
  },
  {
   "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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BLK</th>\n",
       "      <td>-0.031845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOUCH_TIME</th>\n",
       "      <td>-0.018888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHOT_DIST</th>\n",
       "      <td>-0.014498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRIBBLES</th>\n",
       "      <td>0.009413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSE_DEF_DIST</th>\n",
       "      <td>0.021321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eFG%</th>\n",
       "      <td>0.609968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Coefficient\n",
       "BLK               -0.031845\n",
       "TOUCH_TIME        -0.018888\n",
       "SHOT_DIST         -0.014498\n",
       "DRIBBLES           0.009413\n",
       "CLOSE_DEF_DIST     0.021321\n",
       "eFG%               0.609968"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(lm.coef_,no_name_shot_df.drop('SHOT_RESULT',axis=1).columns,columns=['Coefficient'])\n",
    "coeff_df.sort_values(by=['Coefficient'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.054349776815785344\n"
     ]
    }
   ],
   "source": [
    "print('R^2:', r2_score(Y_test_basic, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37946943483275664"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(X_train_basic,Y_train_basic)\n",
    "\n",
    "predictions = dtree.predict_proba(X_test_basic)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depth Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_depths = np.linspace(1, 32, 32, endpoint=True)\n",
    "test_results = []\n",
    "for max_depth in max_depths:\n",
    "    dt = DecisionTreeClassifier(max_depth=max_depth)\n",
    "    dt.fit(X_train_basic, Y_train_basic)\n",
    "    predictions = dt.predict_proba(X_test_basic)\n",
    "\n",
    "    predictions = predictions[:, 1]\n",
    "    brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "    test_results.append(brier_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.legend_handler import HandlerLine2D\n",
    "line2, = plt.plot(max_depths, test_results, 'r', label='Test AUC')\n",
    "plt.ylabel('Brier Score')\n",
    "plt.xlabel('Tree depth')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_results.index(min(test_results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22930273662826375"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(test_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22930273662826375"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_results[6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Min_Samples_Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_samples_splits = np.linspace(0.001, 0.05, 50, endpoint=True)\n",
    "test_results = []\n",
    "for min_samples_split in min_samples_splits:\n",
    "    dt = DecisionTreeClassifier(min_samples_split=min_samples_split)\n",
    "    dt.fit(X_train_basic, Y_train_basic)\n",
    "    predictions = dt.predict_proba(X_test_basic)\n",
    "\n",
    "    predictions = predictions[:, 1]\n",
    "    brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "    test_results.append(brier_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.legend_handler import HandlerLine2D\n",
    "line2, = plt.plot(min_samples_splits, test_results, 'r', label='Test AUC')\n",
    "plt.ylabel('Brier Score')\n",
    "plt.xlabel('Min_Sample_Split')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 0.22923668619263163\n"
     ]
    }
   ],
   "source": [
    "print(test_results.index(min(test_results)), min(test_results))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Min_samples_leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_samples_leafs = np.linspace(0.001, 0.01, 10, endpoint=True)\n",
    "test_results = []\n",
    "for min_samples_leaf in min_samples_leafs:\n",
    "    dt = DecisionTreeClassifier(min_samples_leaf=min_samples_leaf)\n",
    "    dt.fit(X_train_basic, Y_train_basic)\n",
    "    predictions = dt.predict_proba(X_test_basic)\n",
    "\n",
    "    predictions = predictions[:, 1]\n",
    "    brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "    test_results.append(brier_loss)\n",
    "    \n",
    "line2, = plt.plot(min_samples_leafs, test_results, 'r', label='Test AUC')\n",
    "plt.ylabel('Brier Score')\n",
    "plt.xlabel('min_samples_leafs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 0.2296105301668084\n"
     ]
    }
   ],
   "source": [
    "print(test_results.index(min(test_results)), min(test_results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2300880716380984"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_tuned = DecisionTreeClassifier(min_samples_leaf=0.004, min_samples_split = 0.021, max_depth = 6 )\n",
    "dt_tuned.fit(X_train_basic, Y_train_basic)\n",
    "predictions = dt.predict_proba(X_test_basic)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "brier_loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'max_depth': range(1, 10), \n",
    "              #'min_samples_split': np.arange(0.001, 0.05, 50),\n",
    "              #'min_samples_leaf':np. arange(0.001, 0.05, 50)\n",
    "              'min_samples_split': range(10, 50, 10),\n",
    "              'min_samples_leaf': range(10, 50, 10)\n",
    "             }\n",
    "\n",
    "tree_grid = GridSearchCV(cv=5, estimator = DecisionTreeClassifier(), param_grid = param_grid, scoring = 'brier_score_loss')\n",
    "\n",
    "tree_grid.fit(X_train_basic, Y_train_basic)\n",
    "tree_performance = tree_grid.score(X_test_basic,Y_test_basic)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.22885984073956228\n"
     ]
    }
   ],
   "source": [
    "print(tree_grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 7, 'min_samples_leaf': 20, 'min_samples_split': 10}\n"
     ]
    }
   ],
   "source": [
    "print(tree_grid.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22930671178441708"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_optimized = DecisionTreeClassifier(min_samples_leaf=20, min_samples_split = 10, max_depth = 7 )\n",
    "dt_optimized.fit(X_train_basic, Y_train_basic)\n",
    "predictions = dt_optimized.predict_proba(X_test_basic)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SHOT_DIST</td>\n",
       "      <td>0.614652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CLOSE_DEF_DIST</td>\n",
       "      <td>0.235833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TOUCH_TIME</td>\n",
       "      <td>0.108072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>eFG%</td>\n",
       "      <td>0.017610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BLK</td>\n",
       "      <td>0.016248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRIBBLES</td>\n",
       "      <td>0.007584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Feature  Importance\n",
       "0       SHOT_DIST    0.614652\n",
       "2  CLOSE_DEF_DIST    0.235833\n",
       "3      TOUCH_TIME    0.108072\n",
       "4            eFG%    0.017610\n",
       "5             BLK    0.016248\n",
       "1        DRIBBLES    0.007584"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = dict(zip(X_train_basic.columns, dt_optimized.feature_importances_))\n",
    "d_tree_features = pd.DataFrame(list(data.items()), columns=['Feature', 'Importance'])\n",
    "\n",
    "d_tree_features.sort_values(by='Importance', ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20622126233959612"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=200)\n",
    "rfc.fit(X_train_basic, Y_train_basic)\n",
    "predictions = rfc.predict_proba(X_test_basic)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rfc.predict_proba(X_test_basic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>SHOT_DIST</th>\n",
       "      <th>DRIBBLES</th>\n",
       "      <th>CLOSE_DEF_DIST</th>\n",
       "      <th>TOUCH_TIME</th>\n",
       "      <th>eFG%</th>\n",
       "      <th>BLK</th>\n",
       "      <th>SHOT_PROB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55524</th>\n",
       "      <td>4.50</td>\n",
       "      <td>11</td>\n",
       "      <td>3.81</td>\n",
       "      <td>10.21</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244296</th>\n",
       "      <td>3.77</td>\n",
       "      <td>0</td>\n",
       "      <td>2.42</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819</th>\n",
       "      <td>2.55</td>\n",
       "      <td>0</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153416</th>\n",
       "      <td>2.58</td>\n",
       "      <td>0</td>\n",
       "      <td>2.67</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202062</th>\n",
       "      <td>16.15</td>\n",
       "      <td>5</td>\n",
       "      <td>2.40</td>\n",
       "      <td>7.02</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98540</th>\n",
       "      <td>9.78</td>\n",
       "      <td>3</td>\n",
       "      <td>3.23</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143997</th>\n",
       "      <td>21.19</td>\n",
       "      <td>3</td>\n",
       "      <td>3.12</td>\n",
       "      <td>4.08</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232413</th>\n",
       "      <td>4.86</td>\n",
       "      <td>1</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147196</th>\n",
       "      <td>4.85</td>\n",
       "      <td>2</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175056</th>\n",
       "      <td>25.31</td>\n",
       "      <td>8</td>\n",
       "      <td>6.43</td>\n",
       "      <td>7.29</td>\n",
       "      <td>0.502</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66231</th>\n",
       "      <td>11.18</td>\n",
       "      <td>1</td>\n",
       "      <td>5.32</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181828</th>\n",
       "      <td>3.68</td>\n",
       "      <td>0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244206</th>\n",
       "      <td>3.90</td>\n",
       "      <td>4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.435</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252422</th>\n",
       "      <td>12.36</td>\n",
       "      <td>0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26603</th>\n",
       "      <td>3.28</td>\n",
       "      <td>0</td>\n",
       "      <td>2.23</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107651</th>\n",
       "      <td>27.07</td>\n",
       "      <td>0</td>\n",
       "      <td>3.70</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128601</th>\n",
       "      <td>3.21</td>\n",
       "      <td>0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49879</th>\n",
       "      <td>17.73</td>\n",
       "      <td>10</td>\n",
       "      <td>3.60</td>\n",
       "      <td>8.02</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143375</th>\n",
       "      <td>24.76</td>\n",
       "      <td>0</td>\n",
       "      <td>11.95</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182684</th>\n",
       "      <td>25.23</td>\n",
       "      <td>0</td>\n",
       "      <td>5.04</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195082</th>\n",
       "      <td>5.23</td>\n",
       "      <td>0</td>\n",
       "      <td>2.47</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213728</th>\n",
       "      <td>8.65</td>\n",
       "      <td>2</td>\n",
       "      <td>4.33</td>\n",
       "      <td>1.84</td>\n",
       "      <td>0.502</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258714</th>\n",
       "      <td>15.88</td>\n",
       "      <td>3</td>\n",
       "      <td>5.88</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207891</th>\n",
       "      <td>7.50</td>\n",
       "      <td>7</td>\n",
       "      <td>2.13</td>\n",
       "      <td>5.40</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145943</th>\n",
       "      <td>6.71</td>\n",
       "      <td>1</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19970</th>\n",
       "      <td>17.73</td>\n",
       "      <td>0</td>\n",
       "      <td>5.82</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.502</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85461</th>\n",
       "      <td>24.90</td>\n",
       "      <td>7</td>\n",
       "      <td>5.67</td>\n",
       "      <td>6.01</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164548</th>\n",
       "      <td>14.33</td>\n",
       "      <td>0</td>\n",
       "      <td>7.60</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118545</th>\n",
       "      <td>6.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2.74</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41172</th>\n",
       "      <td>5.63</td>\n",
       "      <td>3</td>\n",
       "      <td>4.98</td>\n",
       "      <td>4.48</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.21</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171669</th>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>2.59</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194233</th>\n",
       "      <td>2.01</td>\n",
       "      <td>3</td>\n",
       "      <td>2.27</td>\n",
       "      <td>4.40</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243164</th>\n",
       "      <td>19.14</td>\n",
       "      <td>0</td>\n",
       "      <td>4.42</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262675</th>\n",
       "      <td>22.36</td>\n",
       "      <td>0</td>\n",
       "      <td>5.50</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257222</th>\n",
       "      <td>24.49</td>\n",
       "      <td>12</td>\n",
       "      <td>6.63</td>\n",
       "      <td>10.34</td>\n",
       "      <td>0.489</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26577</th>\n",
       "      <td>23.76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99409</th>\n",
       "      <td>3.26</td>\n",
       "      <td>3</td>\n",
       "      <td>2.98</td>\n",
       "      <td>7.51</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229871</th>\n",
       "      <td>24.68</td>\n",
       "      <td>1</td>\n",
       "      <td>7.13</td>\n",
       "      <td>2.24</td>\n",
       "      <td>0.502</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119278</th>\n",
       "      <td>24.83</td>\n",
       "      <td>0</td>\n",
       "      <td>4.62</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210421</th>\n",
       "      <td>8.29</td>\n",
       "      <td>1</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.12</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4697</th>\n",
       "      <td>24.65</td>\n",
       "      <td>0</td>\n",
       "      <td>5.27</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151618</th>\n",
       "      <td>23.77</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18826</th>\n",
       "      <td>13.85</td>\n",
       "      <td>6</td>\n",
       "      <td>1.93</td>\n",
       "      <td>4.72</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69565</th>\n",
       "      <td>23.20</td>\n",
       "      <td>0</td>\n",
       "      <td>5.15</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70717</th>\n",
       "      <td>25.55</td>\n",
       "      <td>0</td>\n",
       "      <td>7.43</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209848</th>\n",
       "      <td>17.57</td>\n",
       "      <td>0</td>\n",
       "      <td>5.02</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230434</th>\n",
       "      <td>16.26</td>\n",
       "      <td>0</td>\n",
       "      <td>6.75</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159549</th>\n",
       "      <td>8.25</td>\n",
       "      <td>0</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.567</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101517</th>\n",
       "      <td>22.82</td>\n",
       "      <td>1</td>\n",
       "      <td>3.94</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45366</th>\n",
       "      <td>8.29</td>\n",
       "      <td>12</td>\n",
       "      <td>3.50</td>\n",
       "      <td>11.50</td>\n",
       "      <td>0.495</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152843</th>\n",
       "      <td>23.50</td>\n",
       "      <td>1</td>\n",
       "      <td>4.06</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187066</th>\n",
       "      <td>2.72</td>\n",
       "      <td>6</td>\n",
       "      <td>3.12</td>\n",
       "      <td>4.43</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157871</th>\n",
       "      <td>30.18</td>\n",
       "      <td>2</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.478</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53176</th>\n",
       "      <td>2.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0.19</td>\n",
       "      <td>4.68</td>\n",
       "      <td>0.509</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254805</th>\n",
       "      <td>25.25</td>\n",
       "      <td>0</td>\n",
       "      <td>2.89</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158232</th>\n",
       "      <td>1.13</td>\n",
       "      <td>4</td>\n",
       "      <td>1.95</td>\n",
       "      <td>5.76</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262159</th>\n",
       "      <td>16.07</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3447</th>\n",
       "      <td>6.23</td>\n",
       "      <td>4</td>\n",
       "      <td>2.88</td>\n",
       "      <td>4.29</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84346</th>\n",
       "      <td>27.33</td>\n",
       "      <td>5</td>\n",
       "      <td>3.22</td>\n",
       "      <td>4.52</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105075</th>\n",
       "      <td>17.97</td>\n",
       "      <td>8</td>\n",
       "      <td>4.57</td>\n",
       "      <td>8.67</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78897 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        SHOT_DIST  DRIBBLES  CLOSE_DEF_DIST  TOUCH_TIME   eFG%  BLK  SHOT_PROB\n",
       "55524        4.50        11            3.81       10.21  0.459  0.6       0.73\n",
       "244296       3.77         0            2.42        0.68  0.513  0.2       0.72\n",
       "5819         2.55         0            2.01        0.04  0.583  0.8       0.65\n",
       "153416       2.58         0            2.67        0.74  0.502  0.0       0.97\n",
       "202062      16.15         5            2.40        7.02  0.471  0.2       0.37\n",
       "98540        9.78         3            3.23        5.52  0.502  0.6       0.42\n",
       "143997      21.19         3            3.12        4.08  0.496  0.2       0.24\n",
       "232413       4.86         1            1.88        1.90  0.502  0.0       0.58\n",
       "147196       4.85         2            2.65        2.68  0.494  0.2       0.57\n",
       "175056      25.31         8            6.43        7.29  0.502  1.4       0.09\n",
       "66231       11.18         1            5.32        2.16  0.513  0.0       0.48\n",
       "181828       3.68         0            1.33        0.73  0.502  0.2       0.56\n",
       "244206       3.90         4            2.25        3.82  0.435  1.9       0.33\n",
       "252422      12.36         0            2.40        1.56  0.502  0.5       0.26\n",
       "26603        3.28         0            2.23        0.45  0.502  0.0       0.21\n",
       "107651      27.07         0            3.70        0.88  0.502  0.0       0.91\n",
       "128601       3.21         0            1.95        1.40  0.502  0.3       0.97\n",
       "49879       17.73        10            3.60        8.02  0.512  0.1       0.19\n",
       "143375      24.76         0           11.95        1.24  0.495  0.2       0.65\n",
       "182684      25.23         0            5.04        1.25  0.502  0.0       0.51\n",
       "195082       5.23         0            2.47        0.04  0.479  0.5       0.64\n",
       "213728       8.65         2            4.33        1.84  0.502  1.7       0.16\n",
       "258714      15.88         3            5.88        3.24  0.463  0.0       0.76\n",
       "207891       7.50         7            2.13        5.40  0.502  0.2       0.36\n",
       "145943       6.71         1            1.21        1.24  0.502  0.3       0.33\n",
       "19970       17.73         0            5.82        0.80  0.502  1.2       0.27\n",
       "85461       24.90         7            5.67        6.01  0.516  0.0       0.59\n",
       "164548      14.33         0            7.60        1.32  0.541  0.4       0.44\n",
       "118545       6.00         0            2.74        0.61  0.502  0.5       0.32\n",
       "41172        5.63         3            4.98        4.48  0.565  0.0       0.21\n",
       "...           ...       ...             ...         ...    ...  ...        ...\n",
       "171669       2.18         0            2.59        0.81  0.630  0.0       0.76\n",
       "194233       2.01         3            2.27        4.40  0.502  0.3       0.33\n",
       "243164      19.14         0            4.42        1.64  0.502  0.5       0.37\n",
       "262675      22.36         0            5.50        0.84  0.502  0.3       0.22\n",
       "257222      24.49        12            6.63       10.34  0.489  1.0       0.40\n",
       "26577       23.76         1            3.71        1.80  0.535  0.5       0.22\n",
       "99409        3.26         3            2.98        7.51  0.502  0.3       0.47\n",
       "229871      24.68         1            7.13        2.24  0.502  1.6       0.89\n",
       "119278      24.83         0            4.62        0.92  0.502  0.0       0.27\n",
       "210421       8.29         1            3.58        3.12  0.502  0.0       0.23\n",
       "4697        24.65         0            5.27        0.72  0.514  0.6       0.15\n",
       "151618      23.77         2            0.00        2.34  0.497  0.1       0.34\n",
       "18826       13.85         6            1.93        4.72  0.502  0.0       0.68\n",
       "69565       23.20         0            5.15        0.80  0.478  0.4       0.39\n",
       "70717       25.55         0            7.43        0.80  0.482  0.2       0.39\n",
       "209848      17.57         0            5.02        1.41  0.547  0.0       0.23\n",
       "230434      16.26         0            6.75        0.76  0.502  0.0       0.15\n",
       "159549       8.25         0            1.17        0.60  0.567  1.4       0.45\n",
       "101517      22.82         1            3.94        1.52  0.516  0.6       0.22\n",
       "45366        8.29        12            3.50       11.50  0.495  1.3       0.37\n",
       "152843      23.50         1            4.06        1.04  0.477  0.5       0.31\n",
       "187066       2.72         6            3.12        4.43  0.502  0.4       0.74\n",
       "157871      30.18         2            3.09        1.76  0.478  0.2       0.15\n",
       "53176        2.42         1            0.19        4.68  0.509  1.4       0.33\n",
       "254805      25.25         0            2.89        0.67  0.502  0.0       0.19\n",
       "158232       1.13         4            1.95        5.76  0.490  0.5       0.56\n",
       "262159      16.07         3            3.70        2.92  0.516  0.6       0.42\n",
       "3447         6.23         4            2.88        4.29  0.502  0.0       0.83\n",
       "84346       27.33         5            3.22        4.52  0.502  0.1       0.33\n",
       "105075      17.97         8            4.57        8.67  0.518  0.4       0.61\n",
       "\n",
       "[78897 rows x 7 columns]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shot_probability = []\n",
    "for i in predictions:\n",
    "    shot_probability.append(round(i[1], 4))\n",
    "X_test_basic['SHOT_PROB'] = shot_probability\n",
    "X_test_basic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20706315475075326"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(X_train_basic, Y_train_basic)\n",
    "predictions = rfc.predict_proba(X_test_basic)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20627702101966444"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=200, max_features = 'sqrt')\n",
    "rfc.fit(X_train_basic, Y_train_basic)\n",
    "predictions = rfc.predict_proba(X_test_basic)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21115120606428847"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=200, max_features = 0.2)\n",
    "rfc.fit(X_train_basic, Y_train_basic)\n",
    "predictions = rfc.predict_proba(X_test_basic)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(rf_grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(rf_grid.best_params_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24976136574060817"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abc = AdaBoostClassifier(n_estimators=200,\n",
    "                         learning_rate=1)\n",
    "abc_model = abc.fit(X_train_basic, Y_train_basic)\n",
    "predictions = abc_model.predict_proba(X_test_basic)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22743442026392496"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grad_boost = GradientBoostingClassifier()\n",
    "grad_boost_model = grad_boost.fit(X_train_basic, Y_train_basic)\n",
    "predictions = grad_boost_model.predict_proba(X_test_basic)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_basic, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_sampl...      subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': [50, 100, 200], 'learning_rate': [0.01, 0.1, 0.3, 1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='brier_score_loss', verbose=0)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_dist = {\n",
    " 'n_estimators': [50, 100, 200],\n",
    " 'learning_rate' : [0.01, 0.1, 0.3,1],\n",
    " }\n",
    "\n",
    "boost_grid = GridSearchCV(cv=5, estimator = GradientBoostingClassifier(), param_grid = param_dist, scoring = 'brier_score_loss')\n",
    "\n",
    "boost_grid.fit(X_train_basic, Y_train_basic)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.22651338424930192\n"
     ]
    }
   ],
   "source": [
    "print(boost_grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.3, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "print(boost_grid.best_params_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RPM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "rpm_df = modify_player_stats(shot_df, master_df, 'ORPM', 'DRPM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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>SHOT_RESULT</th>\n",
       "      <th>SHOT_DIST</th>\n",
       "      <th>DRIBBLES</th>\n",
       "      <th>CLOSE_DEF_DIST</th>\n",
       "      <th>TOUCH_TIME</th>\n",
       "      <th>ORPM</th>\n",
       "      <th>DRPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>40.10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.87</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>-0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2.43</td>\n",
       "      <td>16</td>\n",
       "      <td>2.90</td>\n",
       "      <td>16.96</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>21.09</td>\n",
       "      <td>0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-1.76</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4.60</td>\n",
       "      <td>13</td>\n",
       "      <td>0.97</td>\n",
       "      <td>9.88</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>22.59</td>\n",
       "      <td>3</td>\n",
       "      <td>4.52</td>\n",
       "      <td>5.42</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SHOT_RESULT  SHOT_DIST  DRIBBLES  CLOSE_DEF_DIST  TOUCH_TIME  ORPM  DRPM\n",
       "0            0      40.10         3            0.68        1.87 -1.33 -0.53\n",
       "1            0       2.43        16            2.90       16.96  0.88  0.51\n",
       "2            1      21.09         0            4.03        0.84 -1.76  0.51\n",
       "3            1       4.60        13            0.97        9.88  0.88  0.51\n",
       "4            0      22.59         3            4.52        5.42  0.85  2.08"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rpm_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "rpm_df.fillna(round(rpm_df.mean(), 3), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>SHOT_DIST</th>\n",
       "      <th>DRIBBLES</th>\n",
       "      <th>CLOSE_DEF_DIST</th>\n",
       "      <th>TOUCH_TIME</th>\n",
       "      <th>eFG%</th>\n",
       "      <th>BLK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55524</th>\n",
       "      <td>4.50</td>\n",
       "      <td>11</td>\n",
       "      <td>3.81</td>\n",
       "      <td>10.21</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244296</th>\n",
       "      <td>3.77</td>\n",
       "      <td>0</td>\n",
       "      <td>2.42</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.513</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819</th>\n",
       "      <td>2.55</td>\n",
       "      <td>0</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153416</th>\n",
       "      <td>2.58</td>\n",
       "      <td>0</td>\n",
       "      <td>2.67</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202062</th>\n",
       "      <td>16.15</td>\n",
       "      <td>5</td>\n",
       "      <td>2.40</td>\n",
       "      <td>7.02</td>\n",
       "      <td>0.471</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        SHOT_DIST  DRIBBLES  CLOSE_DEF_DIST  TOUCH_TIME   eFG%  BLK\n",
       "55524        4.50        11            3.81       10.21  0.459  0.6\n",
       "244296       3.77         0            2.42        0.68  0.513  0.2\n",
       "5819         2.55         0            2.01        0.04  0.583  0.8\n",
       "153416       2.58         0            2.67        0.74  0.502  0.0\n",
       "202062      16.15         5            2.40        7.02  0.471  0.2"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_basic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rpm, X_test_rpm, Y_train_rpm, Y_test_rpm = train_test_split(rpm_df.drop(columns = ['SHOT_RESULT']), \n",
    "                                                    rpm_df['SHOT_RESULT'], test_size=0.3, \n",
    "                                                    random_state=101)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23443292436252264"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_rpm = RandomForestClassifier(n_estimators=200)\n",
    "rfc_rpm.fit(X_train_rpm, Y_train_rpm)\n",
    "predictions = rfc_rpm.predict_proba(X_test_rpm)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_rpm, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BPM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "bpm_df = modify_player_stats(shot_df, master_df, 'OBPM', 'DBPM')\n",
    "bpm_df.fillna(round(bpm_df.mean(), 3), inplace=True)\n",
    "X_train_bpm, X_test_bpm, Y_train_bpm, Y_test_bpm = train_test_split(bpm_df.drop(columns = ['SHOT_RESULT']), \n",
    "                                                    bpm_df['SHOT_RESULT'], test_size=0.3, \n",
    "                                                    random_state=101)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23425293610288275"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_bpm = RandomForestClassifier(n_estimators=200)\n",
    "rfc_bpm.fit(X_train_bpm, Y_train_bpm)\n",
    "predictions = rfc_bpm.predict_proba(X_test_bpm)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_bpm, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Win Share"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws_df = modify_player_stats(shot_df, master_df, 'OWS', 'DWS')\n",
    "ws_df.fillna(round(ws_df.mean(), 3), inplace=True)\n",
    "X_train_ws, X_test_ws, Y_train_ws, Y_test_ws = train_test_split(ws_df.drop(columns = ['SHOT_RESULT']), \n",
    "                                                    ws_df['SHOT_RESULT'], test_size=0.3, \n",
    "                                                    random_state=101)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23552118641833905"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_ws = RandomForestClassifier(n_estimators=200)\n",
    "rfc_ws.fit(X_train_ws, Y_train_ws)\n",
    "predictions = rfc_ws.predict_proba(X_test_ws)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_ws, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_df = modify_player_stats(shot_df, master_df, 'OWS', 'DWS')\n",
    "per_df.fillna(round(per_df.mean(), 3), inplace=True)\n",
    "X_train_per, X_test_per, Y_train_per, Y_test_per = train_test_split(per_df.drop(columns = ['SHOT_RESULT']), \n",
    "                                                    per_df['SHOT_RESULT'], test_size=0.3, \n",
    "                                                    random_state=101)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23529483376737428"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_per = RandomForestClassifier(n_estimators=200)\n",
    "rfc_per.fit(X_train_per, Y_train_per)\n",
    "predictions = rfc_per.predict_proba(X_test_per)\n",
    "\n",
    "predictions = predictions[:, 1]\n",
    "    \n",
    "# for a visual skip to later in the qualitative look\n",
    "brier_loss = brier_score_loss(Y_test_per, predictions)\n",
    "brier_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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