NBAHallofFamers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VQbLCNGrdoNN"
   },
   "source": [
    "# Predicting which current NBA players will make the Hall of Fame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TwT6BEWqdoNS"
   },
   "source": [
    "<img src=\"goat.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OGMikXytdoNT"
   },
   "source": [
    "Authors: Patanjali Kumar, Vibhor Goel, David Abraham"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Iv_EjeD5doNU"
   },
   "source": [
    "## Project Abstract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WE0n5MItdoNV"
   },
   "source": [
    "### Introduction\n",
    "Every year, hundreds of thousands of people around the world watch the basketball games held by the **National Basketball Association** (NBA). The NBA is an American men's professional basketball league composed of 30 teams. Its regular season runs from October to April, with each team playing 82 games, and the league's playoff tournament extends into June. For most players in the NBA, being inducted into the Basketball Hall of Fame in Springfield, Massachusetts would be the crowning achievement of their careers. Thousands have been able to work hard enough and be at an elite enough level to have the opportunity to play in an NBA game, yet only a select handful of them, about 180, have been inducted into the Hall of Fame as a player. Each year’s Hall of Fame class is chosen by a committee and many factors are taken into account to determine whether one should be inducted, however, the criteria can be broken down into “those that have made a significant contribution to the game of basketball.” \n",
    " \n",
    "### Why its important\n",
    "Being able to identify those in today’s game whose resumes stack up well enough to match their peers in Springfield is a worthy task. This is the highest and most selective honor in the sport of basketball, and each year’s induction class effectively defines whose careers are deemed integral to telling the story of the sport. Additionally, the world of analytics has taken over many sports, and this is especially true for basketball. In the aftermath of stories like Moneyball, choosing players for a team’s roster is heavily dependent on what the analytics says one should do. This has manifested itself in ways such as the three-point shot going from somewhat of a gimmick that nobody really utilized several decades ago to becoming the most important shot in today’s game, to the point where a player who cannot shoot in today’s game is almost unplayable. Similarly, using these same advanced stats can be very effective in quantifying what is most important for one’s chances of induction into the Basketball Hall of Fame.\n",
    " \n",
    "### How our project works\n",
    "This tutorial aims to determine what data and which statistics specifically are important in determining a player’s chances of Hall of Fame induction. In our data set, we looked at the stats of players who are already in the Hall of Fame and based off of that we tried to predict who would make the Hall of Fame that is not already in it. Since Hall of Fame eligibility for players requires being retired for at least four full seasons, we looked at players who are still playing currently or who have retired recently. We used data from a very popular website called [Basketball Reference](https://www.basketball-reference.com/), which allows us to look at advanced statistics that go a bit beyond just per-game stats. Though these statistics that can greatly help predict chances of induction, it is important to note that it is impossible to have an all-encompassing prediction model, since off-the-court behavior can affect the voters’ decisions, or as the Board of Trustees puts it, anyone who has “damaged the integrity of the game of basketball.” Taking this into account, our model will attempt to make predictions based solely on a player’s performance on the court, and hopefully be able to help understand what measures of performance are most important in determining players’ chances.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Ac9Bm5godoNZ"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import scipy.stats as stats\n",
    "import requests\n",
    "import sklearn\n",
    "from bs4 import BeautifulSoup, SoupStrainer\n",
    "from sportsreference.nba.roster import Player\n",
    "from datetime import timedelta, datetime\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_selection import RFE, SelectFromModel, SelectKBest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.base import clone\n",
    "from sklearn.metrics import accuracy_score, brier_score_loss\n",
    "import sys, os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DMj3LPbBdoNa"
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qx3JFLfOdoNb"
   },
   "source": [
    "We have gotten both these datasets consisting of basketball players from 1950 (first one from Kaggle, second one from Basketball Referecnce), but are not sure which of the datasets is more exhaustive. To be sure of that, let's check the shape of both datasets to get which one has more players and do a merge accordingly to ensure that we don't miss out on any of the players from either table. We also want the player_ref_id column from the second dataset as that is the primary argument into the basketball reference API to get any information related to a player."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VQc8alOtdoNc"
   },
   "source": [
    "## Data Curation and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uW4RjBf7doNc"
   },
   "outputs": [],
   "source": [
    "players = pd.read_csv(\"Players.csv\")\n",
    "players2 = pd.read_csv(\"player_info.csv\")\n",
    "stats = pd.read_csv(\"Seasons_Stats(1).csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZL4lOiTmdoNc"
   },
   "outputs": [],
   "source": [
    "stats = stats[~stats.Player.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "ODBWezRIdoNd",
    "outputId": "8323419d-b865-4e79-baba-103e39e8023f"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>PER</th>\n",
       "      <th>TS%</th>\n",
       "      <th>3PAr</th>\n",
       "      <th>FTr</th>\n",
       "      <th>ORB%</th>\n",
       "      <th>DRB%</th>\n",
       "      <th>TRB%</th>\n",
       "      <th>AST%</th>\n",
       "      <th>STL%</th>\n",
       "      <th>BLK%</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>blanl</th>\n",
       "      <th>OWS</th>\n",
       "      <th>DWS</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/48</th>\n",
       "      <th>blank2</th>\n",
       "      <th>OBPM</th>\n",
       "      <th>DBPM</th>\n",
       "      <th>BPM</th>\n",
       "      <th>VORP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P</th>\n",
       "      <th>2PA</th>\n",
       "      <th>2P%</th>\n",
       "      <th>eFG%</th>\n",
       "      <th>FT</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "      <th>Champion</th>\n",
       "      <th>championships</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24624.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>24616.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>18233.000000</td>\n",
       "      <td>24138.000000</td>\n",
       "      <td>24101.000000</td>\n",
       "      <td>24538.000000</td>\n",
       "      <td>18839.000000</td>\n",
       "      <td>24525.000000</td>\n",
       "      <td>20792.000000</td>\n",
       "      <td>20792.000000</td>\n",
       "      <td>21571.000000</td>\n",
       "      <td>22555.000000</td>\n",
       "      <td>20792.000000</td>\n",
       "      <td>20792.000000</td>\n",
       "      <td>19582.000000</td>\n",
       "      <td>19640.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24585.000000</td>\n",
       "      <td>24585.000000</td>\n",
       "      <td>24585.000000</td>\n",
       "      <td>24101.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20797.000000</td>\n",
       "      <td>20797.000000</td>\n",
       "      <td>20797.000000</td>\n",
       "      <td>20797.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>24525.000000</td>\n",
       "      <td>18927.000000</td>\n",
       "      <td>18927.000000</td>\n",
       "      <td>15416.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>24496.000000</td>\n",
       "      <td>24525.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>23766.000000</td>\n",
       "      <td>20797.000000</td>\n",
       "      <td>20797.000000</td>\n",
       "      <td>24312.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>20797.000000</td>\n",
       "      <td>20797.000000</td>\n",
       "      <td>19645.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "      <td>24624.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12354.094989</td>\n",
       "      <td>1992.594989</td>\n",
       "      <td>26.664405</td>\n",
       "      <td>50.837110</td>\n",
       "      <td>23.593375</td>\n",
       "      <td>1209.720317</td>\n",
       "      <td>12.479071</td>\n",
       "      <td>0.493001</td>\n",
       "      <td>0.158604</td>\n",
       "      <td>0.325455</td>\n",
       "      <td>6.181565</td>\n",
       "      <td>13.708657</td>\n",
       "      <td>9.949210</td>\n",
       "      <td>13.009962</td>\n",
       "      <td>1.648269</td>\n",
       "      <td>1.410624</td>\n",
       "      <td>15.085099</td>\n",
       "      <td>18.906492</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.257307</td>\n",
       "      <td>1.227395</td>\n",
       "      <td>2.485796</td>\n",
       "      <td>0.065002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.778386</td>\n",
       "      <td>-0.548570</td>\n",
       "      <td>-2.326720</td>\n",
       "      <td>0.559879</td>\n",
       "      <td>195.325820</td>\n",
       "      <td>430.645752</td>\n",
       "      <td>0.430817</td>\n",
       "      <td>22.215037</td>\n",
       "      <td>63.604480</td>\n",
       "      <td>0.248796</td>\n",
       "      <td>178.250447</td>\n",
       "      <td>381.756782</td>\n",
       "      <td>0.445343</td>\n",
       "      <td>0.450658</td>\n",
       "      <td>102.389336</td>\n",
       "      <td>136.775219</td>\n",
       "      <td>0.719279</td>\n",
       "      <td>62.189210</td>\n",
       "      <td>147.199404</td>\n",
       "      <td>224.637381</td>\n",
       "      <td>114.852623</td>\n",
       "      <td>39.897052</td>\n",
       "      <td>24.470260</td>\n",
       "      <td>73.939832</td>\n",
       "      <td>116.339222</td>\n",
       "      <td>510.116350</td>\n",
       "      <td>0.038499</td>\n",
       "      <td>0.228720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7125.490095</td>\n",
       "      <td>17.429594</td>\n",
       "      <td>3.841892</td>\n",
       "      <td>26.496161</td>\n",
       "      <td>28.632387</td>\n",
       "      <td>941.146575</td>\n",
       "      <td>6.039014</td>\n",
       "      <td>0.094469</td>\n",
       "      <td>0.187495</td>\n",
       "      <td>0.218971</td>\n",
       "      <td>4.872685</td>\n",
       "      <td>6.636402</td>\n",
       "      <td>5.040283</td>\n",
       "      <td>9.191843</td>\n",
       "      <td>1.017024</td>\n",
       "      <td>1.773348</td>\n",
       "      <td>6.919170</td>\n",
       "      <td>5.448157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.136256</td>\n",
       "      <td>1.269613</td>\n",
       "      <td>3.058638</td>\n",
       "      <td>0.102471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.792947</td>\n",
       "      <td>2.253106</td>\n",
       "      <td>4.691619</td>\n",
       "      <td>1.336892</td>\n",
       "      <td>188.114361</td>\n",
       "      <td>397.624715</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>38.543366</td>\n",
       "      <td>102.442769</td>\n",
       "      <td>0.176683</td>\n",
       "      <td>179.478923</td>\n",
       "      <td>371.260335</td>\n",
       "      <td>0.099803</td>\n",
       "      <td>0.099200</td>\n",
       "      <td>113.373565</td>\n",
       "      <td>146.078918</td>\n",
       "      <td>0.141824</td>\n",
       "      <td>67.324881</td>\n",
       "      <td>145.921912</td>\n",
       "      <td>228.190203</td>\n",
       "      <td>135.863913</td>\n",
       "      <td>38.713053</td>\n",
       "      <td>36.935084</td>\n",
       "      <td>67.713803</td>\n",
       "      <td>84.791873</td>\n",
       "      <td>492.922981</td>\n",
       "      <td>0.192402</td>\n",
       "      <td>0.707748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-90.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.100000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-2.800000</td>\n",
       "      <td>-2.519000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.800000</td>\n",
       "      <td>-30.400000</td>\n",
       "      <td>-86.700000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6186.750000</td>\n",
       "      <td>1981.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>0.458000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>15.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.031000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>-1.700000</td>\n",
       "      <td>-4.200000</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.393000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>0.407000</td>\n",
       "      <td>0.414000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.657000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12357.500000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1053.000000</td>\n",
       "      <td>12.700000</td>\n",
       "      <td>0.506000</td>\n",
       "      <td>0.064000</td>\n",
       "      <td>0.296000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>12.700000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.500000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>0.439000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.292000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>0.456000</td>\n",
       "      <td>0.463000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>0.743000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18524.250000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1971.000000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>0.544000</td>\n",
       "      <td>0.288000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>17.700000</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.115000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>661.000000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>0.363000</td>\n",
       "      <td>268.000000</td>\n",
       "      <td>579.250000</td>\n",
       "      <td>0.496000</td>\n",
       "      <td>0.501000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>0.808000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>778.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24690.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>3882.000000</td>\n",
       "      <td>129.100000</td>\n",
       "      <td>1.136000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>77.800000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>2.123000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>46.800000</td>\n",
       "      <td>36.200000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>3159.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>402.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1597.000000</td>\n",
       "      <td>3159.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>840.000000</td>\n",
       "      <td>1363.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>587.000000</td>\n",
       "      <td>1111.000000</td>\n",
       "      <td>2149.000000</td>\n",
       "      <td>1164.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>464.000000</td>\n",
       "      <td>386.000000</td>\n",
       "      <td>4029.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0          Year           Age             G            GS  \\\n",
       "count  24624.000000  24624.000000  24616.000000  24624.000000  18233.000000   \n",
       "mean   12354.094989   1992.594989     26.664405     50.837110     23.593375   \n",
       "std     7125.490095     17.429594      3.841892     26.496161     28.632387   \n",
       "min        0.000000   1950.000000     18.000000      1.000000      0.000000   \n",
       "25%     6186.750000   1981.000000     24.000000     27.000000      0.000000   \n",
       "50%    12357.500000   1996.000000     26.000000     58.000000      8.000000   \n",
       "75%    18524.250000   2007.000000     29.000000     75.000000     45.000000   \n",
       "max    24690.000000   2017.000000     44.000000     88.000000     83.000000   \n",
       "\n",
       "                 MP           PER           TS%          3PAr           FTr  \\\n",
       "count  24138.000000  24101.000000  24538.000000  18839.000000  24525.000000   \n",
       "mean    1209.720317     12.479071      0.493001      0.158604      0.325455   \n",
       "std      941.146575      6.039014      0.094469      0.187495      0.218971   \n",
       "min        0.000000    -90.600000      0.000000      0.000000      0.000000   \n",
       "25%      340.000000      9.800000      0.458000      0.005000      0.208000   \n",
       "50%     1053.000000     12.700000      0.506000      0.064000      0.296000   \n",
       "75%     1971.000000     15.600000      0.544000      0.288000      0.400000   \n",
       "max     3882.000000    129.100000      1.136000      1.000000      6.000000   \n",
       "\n",
       "               ORB%          DRB%          TRB%          AST%          STL%  \\\n",
       "count  20792.000000  20792.000000  21571.000000  22555.000000  20792.000000   \n",
       "mean       6.181565     13.708657      9.949210     13.009962      1.648269   \n",
       "std        4.872685      6.636402      5.040283      9.191843      1.017024   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        2.600000      8.800000      5.900000      6.500000      1.100000   \n",
       "50%        5.400000     12.700000      9.200000     10.500000      1.500000   \n",
       "75%        9.000000     18.100000     13.500000     17.600000      2.100000   \n",
       "max      100.000000    100.000000    100.000000    100.000000     24.200000   \n",
       "\n",
       "               BLK%          TOV%          USG%  blanl           OWS  \\\n",
       "count  20792.000000  19582.000000  19640.000000    0.0  24585.000000   \n",
       "mean       1.410624     15.085099     18.906492    NaN      1.257307   \n",
       "std        1.773348      6.919170      5.448157    NaN      2.136256   \n",
       "min        0.000000      0.000000      0.000000    NaN     -5.100000   \n",
       "25%        0.300000     11.400000     15.400000    NaN     -0.100000   \n",
       "50%        0.900000     14.200000     18.600000    NaN      0.400000   \n",
       "75%        1.900000     17.700000     22.200000    NaN      1.900000   \n",
       "max       77.800000    100.000000    100.000000    NaN     18.300000   \n",
       "\n",
       "                DWS            WS         WS/48  blank2          OBPM  \\\n",
       "count  24585.000000  24585.000000  24101.000000     0.0  20797.000000   \n",
       "mean       1.227395      2.485796      0.065002     NaN     -1.778386   \n",
       "std        1.269613      3.058638      0.102471     NaN      3.792947   \n",
       "min       -1.000000     -2.800000     -2.519000     NaN    -73.800000   \n",
       "25%        0.200000      0.200000      0.031000     NaN     -3.400000   \n",
       "50%        0.800000      1.400000      0.075000     NaN     -1.500000   \n",
       "75%        1.800000      3.800000      0.115000     NaN      0.300000   \n",
       "max       16.000000     25.400000      2.123000     NaN     47.800000   \n",
       "\n",
       "               DBPM           BPM          VORP            FG           FGA  \\\n",
       "count  20797.000000  20797.000000  20797.000000  24624.000000  24624.000000   \n",
       "mean      -0.548570     -2.326720      0.559879    195.325820    430.645752   \n",
       "std        2.253106      4.691619      1.336892    188.114361    397.624715   \n",
       "min      -30.400000    -86.700000     -2.600000      0.000000      0.000000   \n",
       "25%       -1.700000     -4.200000     -0.200000     41.000000     99.000000   \n",
       "50%       -0.500000     -1.800000      0.000000    141.000000    321.000000   \n",
       "75%        0.700000      0.300000      0.900000    299.000000    661.000000   \n",
       "max       46.800000     36.200000     12.400000   1597.000000   3159.000000   \n",
       "\n",
       "                FG%            3P           3PA           3P%            2P  \\\n",
       "count  24525.000000  18927.000000  18927.000000  15416.000000  24624.000000   \n",
       "mean       0.430817     22.215037     63.604480      0.248796    178.250447   \n",
       "std        0.095921     38.543366    102.442769      0.176683    179.478923   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.393000      0.000000      1.000000      0.100000     35.000000   \n",
       "50%        0.439000      2.000000     11.000000      0.292000    122.000000   \n",
       "75%        0.480000     27.000000     84.000000      0.363000    268.000000   \n",
       "max        1.000000    402.000000    886.000000      1.000000   1597.000000   \n",
       "\n",
       "                2PA           2P%          eFG%            FT           FTA  \\\n",
       "count  24624.000000  24496.000000  24525.000000  24624.000000  24624.000000   \n",
       "mean     381.756782      0.445343      0.450658    102.389336    136.775219   \n",
       "std      371.260335      0.099803      0.099200    113.373565    146.078918   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       82.000000      0.407000      0.414000     18.000000     27.000000   \n",
       "50%      270.000000      0.456000      0.463000     63.000000     88.000000   \n",
       "75%      579.250000      0.496000      0.501000    149.000000    201.000000   \n",
       "max     3159.000000      1.000000      1.500000    840.000000   1363.000000   \n",
       "\n",
       "                FT%           ORB           DRB           TRB           AST  \\\n",
       "count  23766.000000  20797.000000  20797.000000  24312.000000  24624.000000   \n",
       "mean       0.719279     62.189210    147.199404    224.637381    114.852623   \n",
       "std        0.141824     67.324881    145.921912    228.190203    135.863913   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.657000     12.000000     33.000000     51.000000     19.000000   \n",
       "50%        0.743000     38.000000    106.000000    159.000000     68.000000   \n",
       "75%        0.808000     91.000000    212.000000    322.000000    160.000000   \n",
       "max        1.000000    587.000000   1111.000000   2149.000000   1164.000000   \n",
       "\n",
       "                STL           BLK           TOV            PF           PTS  \\\n",
       "count  20797.000000  20797.000000  19645.000000  24624.000000  24624.000000   \n",
       "mean      39.897052     24.470260     73.939832    116.339222    510.116350   \n",
       "std       38.713053     36.935084     67.713803     84.791873    492.922981   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        9.000000      3.000000     18.000000     39.000000    106.000000   \n",
       "50%       29.000000     11.000000     55.000000    109.000000    364.000000   \n",
       "75%       60.000000     29.000000    112.000000    182.000000    778.000000   \n",
       "max      301.000000    456.000000    464.000000    386.000000   4029.000000   \n",
       "\n",
       "           Champion  championships  \n",
       "count  24624.000000   24624.000000  \n",
       "mean       0.038499       0.228720  \n",
       "std        0.192402       0.707748  \n",
       "min        0.000000       0.000000  \n",
       "25%        0.000000       0.000000  \n",
       "50%        0.000000       0.000000  \n",
       "75%        0.000000       0.000000  \n",
       "max        1.000000      11.000000  "
      ]
     },
     "execution_count": 106,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "fQK8Gr2NdoNe",
    "outputId": "5bb324ad-0ec4-4e50-b798-e74f13ac5494"
   },
   "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>Unnamed: 0</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>born</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3922.000000</td>\n",
       "      <td>3921.000000</td>\n",
       "      <td>3921.000000</td>\n",
       "      <td>3921.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1960.500000</td>\n",
       "      <td>198.704922</td>\n",
       "      <td>94.783219</td>\n",
       "      <td>1962.37975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1132.328206</td>\n",
       "      <td>9.269761</td>\n",
       "      <td>12.039515</td>\n",
       "      <td>20.33491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1913.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>980.250000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1948.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1960.500000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>1964.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2940.750000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1979.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3921.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>1997.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0       height       weight        born\n",
       "count  3922.000000  3921.000000  3921.000000  3921.00000\n",
       "mean   1960.500000   198.704922    94.783219  1962.37975\n",
       "std    1132.328206     9.269761    12.039515    20.33491\n",
       "min       0.000000   160.000000    60.000000  1913.00000\n",
       "25%     980.250000   190.000000    86.000000  1948.00000\n",
       "50%    1960.500000   198.000000    95.000000  1964.00000\n",
       "75%    2940.750000   206.000000   102.000000  1979.00000\n",
       "max    3921.000000   231.000000   163.000000  1997.00000"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# players.head()\n",
    "players.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "ATI9-KogdoNe",
    "outputId": "61c9691a-43de-4b03-e7d3-213a7d358d05"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>Wt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4803.000000</td>\n",
       "      <td>4803.000000</td>\n",
       "      <td>4798.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1986.881532</td>\n",
       "      <td>1991.054757</td>\n",
       "      <td>209.302626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.803348</td>\n",
       "      <td>22.571265</td>\n",
       "      <td>26.170289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1947.000000</td>\n",
       "      <td>1947.000000</td>\n",
       "      <td>114.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1970.000000</td>\n",
       "      <td>1974.000000</td>\n",
       "      <td>190.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1989.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>210.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2006.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>225.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              From           To           Wt\n",
       "count  4803.000000  4803.000000  4798.000000\n",
       "mean   1986.881532  1991.054757   209.302626\n",
       "std      21.803348    22.571265    26.170289\n",
       "min    1947.000000  1947.000000   114.000000\n",
       "25%    1970.000000  1974.000000   190.000000\n",
       "50%    1989.000000  1994.000000   210.000000\n",
       "75%    2006.000000  2012.000000   225.000000\n",
       "max    2020.000000  2020.000000   360.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# players2.head()\n",
    "players2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3iJmfAnYdoNf"
   },
   "outputs": [],
   "source": [
    "combined_df = players2.merge(players, how=\"left\", left_on=\"player_name\", right_on=\"Player\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "T3WV042GdoNf"
   },
   "outputs": [],
   "source": [
    "combined_df = combined_df[['player_name','player_ref_id','From','To','height','weight','Birth Date','Pos','Colleges']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "4GebPeHZdoNf",
    "outputId": "04313ba9-e157-4f1f-a92a-b0a7680f4e29"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_ref_id</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>Birth Date</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Colleges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alaa Abdelnaby</td>\n",
       "      <td>abdelal01</td>\n",
       "      <td>1991</td>\n",
       "      <td>1995</td>\n",
       "      <td>208.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>June 24 1968</td>\n",
       "      <td>F-C</td>\n",
       "      <td>Duke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Zaid Abdul-Aziz</td>\n",
       "      <td>abdulza01</td>\n",
       "      <td>1969</td>\n",
       "      <td>1978</td>\n",
       "      <td>206.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>April 7 1946</td>\n",
       "      <td>C-F</td>\n",
       "      <td>Iowa State</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kareem Abdul-Jabbar*</td>\n",
       "      <td>abdulka01</td>\n",
       "      <td>1970</td>\n",
       "      <td>1989</td>\n",
       "      <td>218.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>April 16 1947</td>\n",
       "      <td>C</td>\n",
       "      <td>UCLA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mahmoud Abdul-Rauf</td>\n",
       "      <td>abdulma02</td>\n",
       "      <td>1991</td>\n",
       "      <td>2001</td>\n",
       "      <td>188.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>March 9 1969</td>\n",
       "      <td>G</td>\n",
       "      <td>LSU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tariq Abdul-Wahad</td>\n",
       "      <td>abdulta01</td>\n",
       "      <td>1998</td>\n",
       "      <td>2003</td>\n",
       "      <td>198.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>November 3 1974</td>\n",
       "      <td>F</td>\n",
       "      <td>Michigan San Jose State</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            player_name player_ref_id  From    To  height  weight  \\\n",
       "0        Alaa Abdelnaby     abdelal01  1991  1995   208.0   108.0   \n",
       "1       Zaid Abdul-Aziz     abdulza01  1969  1978   206.0   106.0   \n",
       "2  Kareem Abdul-Jabbar*     abdulka01  1970  1989   218.0   102.0   \n",
       "3    Mahmoud Abdul-Rauf     abdulma02  1991  2001   188.0    83.0   \n",
       "4     Tariq Abdul-Wahad     abdulta01  1998  2003   198.0   101.0   \n",
       "\n",
       "        Birth Date  Pos                 Colleges  \n",
       "0     June 24 1968  F-C                     Duke  \n",
       "1     April 7 1946  C-F               Iowa State  \n",
       "2    April 16 1947    C                     UCLA  \n",
       "3     March 9 1969    G                      LSU  \n",
       "4  November 3 1974    F  Michigan San Jose State  "
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "25Sv9NVxdoNg"
   },
   "source": [
    "We now have basic player information for all the players matched with their player ids. The Basketball Reference website tells us that from the list of active and inactive players, players with a * at the end of their name are part of the Hall of Fame.\n",
    "\n",
    "We decided to use Hall of Fame as a base to include all-time greats such as Kareem Abdul Jabbar and Bill Russell into our analysis. We also decided to include players that are active or recently retired. Active players because they shouldn't be left out just because they have not been inducted into the Hall of Fame, and recently retired players because a player can be on the Hall of Fame ballot for many years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TiIiA2ovdoNg"
   },
   "outputs": [],
   "source": [
    "analysis_data = combined_df[(combined_df.player_name.str.match(\".*[*]\")) | (combined_df['To']>=2005)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ucq8OhHgdoNh"
   },
   "source": [
    "Let's see how many Hall of Famers we have from this data to get a sense of whether our data has the necessary players and basic player information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OF1XDIHDdoNh",
    "outputId": "ae6c3be3-2b19-47fc-c6d1-ec70fbb9535d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(152, 9)"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df[(combined_df.player_name.str.match(\".*[*]\"))].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ReeVa9EhdoNi"
   },
   "source": [
    "We see that all the players are in this output are Hall of Famers (denoted by the star in their name according to Basketball Reference) and that all of the basic player information is available the way we need it.\n",
    "\n",
    "In order to scrape more detailed information from the Basketball Reference website, we use the sportsreference API to call it and get each player's [advanced statistics](https://www.basketball-reference.com/players/a/abdulka01.html)(For more information on how each of these advanced statistics are defined, visit the link and hover over the particular column to get the definition of that statistic).\n",
    "\n",
    "Each player's detailed statistics contains information that provides us with insight into the kind of player they were, which season's they played for. what were the statistics for each of those seasons, how did they perform throughout their career, what were their salaries through each season etc. For the purposes of our analysis, we are going to look at total career statistics and see averages and other trends in overall career statistics to carry out our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7qpne9jIdoNi",
    "outputId": "71c6323d-14ec-4e19-8102-d820557a851f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count           1851\n",
       "unique          1845\n",
       "top       Mike James\n",
       "freq               2\n",
       "Name: player_name, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_data['player_name'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "rTQCgbNRdoNj",
    "outputId": "04c35589-6795-4ecd-9afe-6ab7b30aa46c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_ref_id</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>Birth Date</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Colleges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kareem Abdul-Jabbar*</td>\n",
       "      <td>abdulka01</td>\n",
       "      <td>1970</td>\n",
       "      <td>1989</td>\n",
       "      <td>218.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>April 16 1947</td>\n",
       "      <td>C</td>\n",
       "      <td>UCLA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shareef Abdur-Rahim</td>\n",
       "      <td>abdursh01</td>\n",
       "      <td>1997</td>\n",
       "      <td>2008</td>\n",
       "      <td>206.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>December 11 1976</td>\n",
       "      <td>F</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Álex Abrines</td>\n",
       "      <td>abrinal01</td>\n",
       "      <td>2017</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>August 1 1993</td>\n",
       "      <td>G-F</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Alex Acker</td>\n",
       "      <td>ackeral01</td>\n",
       "      <td>2006</td>\n",
       "      <td>2009</td>\n",
       "      <td>196.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>January 21 1983</td>\n",
       "      <td>G</td>\n",
       "      <td>Pepperdine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Quincy Acy</td>\n",
       "      <td>acyqu01</td>\n",
       "      <td>2013</td>\n",
       "      <td>2019</td>\n",
       "      <td>201.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>October 6 1990</td>\n",
       "      <td>F-C</td>\n",
       "      <td>Baylor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             player_name player_ref_id  From    To  height  weight  \\\n",
       "2   Kareem Abdul-Jabbar*     abdulka01  1970  1989   218.0   102.0   \n",
       "5    Shareef Abdur-Rahim     abdursh01  1997  2008   206.0   102.0   \n",
       "9           Álex Abrines     abrinal01  2017  2019     NaN     NaN   \n",
       "10            Alex Acker     ackeral01  2006  2009   196.0    83.0   \n",
       "14            Quincy Acy       acyqu01  2013  2019   201.0   108.0   \n",
       "\n",
       "          Birth Date  Pos    Colleges  \n",
       "2      April 16 1947    C        UCLA  \n",
       "5   December 11 1976    F  California  \n",
       "9      August 1 1993  G-F         NaN  \n",
       "10   January 21 1983    G  Pepperdine  \n",
       "14    October 6 1990  F-C      Baylor  "
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8gbTgWwWdoNj"
   },
   "source": [
    "Below, we define a function to gather detailed player statistics by using the [basketball reference api](https://www.basketball-reference.com/) that we've imported above and see player statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Uzr-8awfdoNk"
   },
   "outputs": [],
   "source": [
    "def detailed_player_stats(ref_id):\n",
    "    player = Player(ref_id)\n",
    "    points = player.points\n",
    "    games = player.games_played\n",
    "    rebounds = player.total_rebounds\n",
    "    assists = player.assists\n",
    "    steals = player.steals\n",
    "    blocks = player.blocks\n",
    "    mins_played = player.minutes_played\n",
    "    ppg = points / games if points else None\n",
    "    rbg = rebounds / games if rebounds else None\n",
    "    astpg = assists / games if assists else None\n",
    "    efgp = player.effective_field_goal_percentage\n",
    "    per = player.player_efficiency_rating\n",
    "    ws = player.win_shares\n",
    "    vorp = player.value_over_replacement_player\n",
    "    tsp = player.true_shooting_percentage\n",
    "    spg = steals / games if steals else -1\n",
    "    bpg = blocks / games if blocks else -1\n",
    "    min_pg = mins_played / games if mins_played else None\n",
    "    box_plus_minus = player.box_plus_minus\n",
    "\n",
    "    player_entry = {'player_ref_id': ref_id,\n",
    "                    'points_per_game': ppg,\n",
    "                    'rebounds_per_game': rbg,\n",
    "                    'assists_per_game': astpg,\n",
    "                    'effective_fgp': efgp,\n",
    "                    'efficiency_rating' : per,\n",
    "                    'win_shares' : ws,\n",
    "                    'value_over_replacement' : vorp,\n",
    "                    'true_shooting_pct': tsp,\n",
    "                    'steals_per_game': spg,\n",
    "                    'blocks_per_game': bpg,\n",
    "                    'minutes_per_game': min_pg}\n",
    "\n",
    "    return player_entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zN_xbP8QdoNk",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "players_stats_det = []\n",
    "for i, idx in enumerate(analysis_data.player_ref_id):\n",
    "    try:\n",
    "        players_stats_det.append(detailed_player_stats(idx))\n",
    "    except:\n",
    "        print(idx)\n",
    "players_stats_df = pd.DataFrame(players_stats_det) # convert to dateframe "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xwqQFKLWdoNl"
   },
   "source": [
    "Let us use our detailed stats function to get  the detailed statistics of all players using their reference ids (for Basketball Reference) and save it in a dataframe to use later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DM8pEqIpdoNm"
   },
   "source": [
    "We ran this piece of code in Google Colab as it takes a while to create this advanced stats dataframe for all 1850 players. we saved that dataframe from running this piece of code in Google Colab as a csv that we import for further usage from below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0OsAYsP4doNm"
   },
   "outputs": [],
   "source": [
    "players_stats_df.to_csv('player_detailed_stats.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cQDQfoKpdoNm"
   },
   "outputs": [],
   "source": [
    "from google.colab import files\n",
    "files.download(\"player_detailed_stats.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FeM0WJn0doNn"
   },
   "source": [
    "Now that we have players' personal details such as height, weight, which college they attended and birth date in one dataframe, and players' advanced statistics like player efficiency rating, true shooting percentage, points per game, etc. in another dataframe, let's merge both dataframes together to create one huge dataframe of all player details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "d540WHKZdoNn"
   },
   "outputs": [],
   "source": [
    "players_stats_df = pd.read_csv(\"player_detailed_stats.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "cNRJ4xEqdoNo",
    "outputId": "61e074eb-fd9d-4144-b20b-f3ea8548896e",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_ref_id</th>\n",
       "      <th>points_per_game</th>\n",
       "      <th>rebounds_per_game</th>\n",
       "      <th>assists_per_game</th>\n",
       "      <th>effective_fgp</th>\n",
       "      <th>efficiency_rating</th>\n",
       "      <th>win_shares</th>\n",
       "      <th>value_over_replacement</th>\n",
       "      <th>true_shooting_pct</th>\n",
       "      <th>steals_per_game</th>\n",
       "      <th>blocks_per_game</th>\n",
       "      <th>minutes_per_game</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abdulka01</td>\n",
       "      <td>24.607051</td>\n",
       "      <td>11.179487</td>\n",
       "      <td>3.628205</td>\n",
       "      <td>0.559</td>\n",
       "      <td>24.6</td>\n",
       "      <td>273.4</td>\n",
       "      <td>85.7</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.743590</td>\n",
       "      <td>2.044231</td>\n",
       "      <td>36.824359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abdursh01</td>\n",
       "      <td>18.106024</td>\n",
       "      <td>7.516867</td>\n",
       "      <td>2.540964</td>\n",
       "      <td>0.479</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71.2</td>\n",
       "      <td>21.9</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.987952</td>\n",
       "      <td>0.768675</td>\n",
       "      <td>34.797590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abrinal01</td>\n",
       "      <td>5.310345</td>\n",
       "      <td>1.425287</td>\n",
       "      <td>0.505747</td>\n",
       "      <td>0.525</td>\n",
       "      <td>8.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.528736</td>\n",
       "      <td>0.126437</td>\n",
       "      <td>15.959770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ackeral01</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.413</td>\n",
       "      <td>8.5</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>7.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acyqu01</td>\n",
       "      <td>4.872404</td>\n",
       "      <td>3.498516</td>\n",
       "      <td>0.643917</td>\n",
       "      <td>0.513</td>\n",
       "      <td>11.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.415430</td>\n",
       "      <td>0.400593</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  player_ref_id  points_per_game  rebounds_per_game  assists_per_game  \\\n",
       "0     abdulka01        24.607051          11.179487          3.628205   \n",
       "1     abdursh01        18.106024           7.516867          2.540964   \n",
       "2     abrinal01         5.310345           1.425287          0.505747   \n",
       "3     ackeral01         2.700000           0.966667          0.533333   \n",
       "4       acyqu01         4.872404           3.498516          0.643917   \n",
       "\n",
       "   effective_fgp  efficiency_rating  win_shares  value_over_replacement  \\\n",
       "0          0.559               24.6       273.4                    85.7   \n",
       "1          0.479               19.0        71.2                    21.9   \n",
       "2          0.525                8.8         5.0                     0.1   \n",
       "3          0.413                8.5        -0.2                    -0.2   \n",
       "4          0.513               11.2         8.7                    -0.1   \n",
       "\n",
       "   true_shooting_pct  steals_per_game  blocks_per_game  minutes_per_game  \n",
       "0              0.592         0.743590         2.044231         36.824359  \n",
       "1              0.549         0.987952         0.768675         34.797590  \n",
       "2              0.552         0.528736         0.126437         15.959770  \n",
       "3              0.420         0.200000         0.133333          7.800000  \n",
       "4              0.553         0.415430         0.400593         16.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_stats_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JWTH_BzIdoNo"
   },
   "outputs": [],
   "source": [
    "players3 = analysis_data.merge(players_stats_df, how='inner', left_on='player_ref_id', right_on='player_ref_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "id": "RMDRrOFidoNp",
    "outputId": "aef867ad-a40c-43c2-eca8-844a7e817916"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_ref_id</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>Birth Date</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Colleges</th>\n",
       "      <th>points_per_game</th>\n",
       "      <th>rebounds_per_game</th>\n",
       "      <th>assists_per_game</th>\n",
       "      <th>effective_fgp</th>\n",
       "      <th>efficiency_rating</th>\n",
       "      <th>win_shares</th>\n",
       "      <th>value_over_replacement</th>\n",
       "      <th>true_shooting_pct</th>\n",
       "      <th>steals_per_game</th>\n",
       "      <th>blocks_per_game</th>\n",
       "      <th>minutes_per_game</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kareem Abdul-Jabbar*</td>\n",
       "      <td>abdulka01</td>\n",
       "      <td>1970</td>\n",
       "      <td>1989</td>\n",
       "      <td>218.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>April 16 1947</td>\n",
       "      <td>C</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>24.607051</td>\n",
       "      <td>11.179487</td>\n",
       "      <td>3.628205</td>\n",
       "      <td>0.559</td>\n",
       "      <td>24.6</td>\n",
       "      <td>273.4</td>\n",
       "      <td>85.7</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.743590</td>\n",
       "      <td>2.044231</td>\n",
       "      <td>36.824359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shareef Abdur-Rahim</td>\n",
       "      <td>abdursh01</td>\n",
       "      <td>1997</td>\n",
       "      <td>2008</td>\n",
       "      <td>206.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>December 11 1976</td>\n",
       "      <td>F</td>\n",
       "      <td>California</td>\n",
       "      <td>18.106024</td>\n",
       "      <td>7.516867</td>\n",
       "      <td>2.540964</td>\n",
       "      <td>0.479</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71.2</td>\n",
       "      <td>21.9</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.987952</td>\n",
       "      <td>0.768675</td>\n",
       "      <td>34.797590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Álex Abrines</td>\n",
       "      <td>abrinal01</td>\n",
       "      <td>2017</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>August 1 1993</td>\n",
       "      <td>G-F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.310345</td>\n",
       "      <td>1.425287</td>\n",
       "      <td>0.505747</td>\n",
       "      <td>0.525</td>\n",
       "      <td>8.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.528736</td>\n",
       "      <td>0.126437</td>\n",
       "      <td>15.959770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alex Acker</td>\n",
       "      <td>ackeral01</td>\n",
       "      <td>2006</td>\n",
       "      <td>2009</td>\n",
       "      <td>196.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>January 21 1983</td>\n",
       "      <td>G</td>\n",
       "      <td>Pepperdine</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.413</td>\n",
       "      <td>8.5</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>7.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quincy Acy</td>\n",
       "      <td>acyqu01</td>\n",
       "      <td>2013</td>\n",
       "      <td>2019</td>\n",
       "      <td>201.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>October 6 1990</td>\n",
       "      <td>F-C</td>\n",
       "      <td>Baylor</td>\n",
       "      <td>4.872404</td>\n",
       "      <td>3.498516</td>\n",
       "      <td>0.643917</td>\n",
       "      <td>0.513</td>\n",
       "      <td>11.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.415430</td>\n",
       "      <td>0.400593</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            player_name player_ref_id  From    To  height  weight  \\\n",
       "0  Kareem Abdul-Jabbar*     abdulka01  1970  1989   218.0   102.0   \n",
       "1   Shareef Abdur-Rahim     abdursh01  1997  2008   206.0   102.0   \n",
       "2          Álex Abrines     abrinal01  2017  2019     NaN     NaN   \n",
       "3            Alex Acker     ackeral01  2006  2009   196.0    83.0   \n",
       "4            Quincy Acy       acyqu01  2013  2019   201.0   108.0   \n",
       "\n",
       "         Birth Date  Pos    Colleges  points_per_game  rebounds_per_game  \\\n",
       "0     April 16 1947    C        UCLA        24.607051          11.179487   \n",
       "1  December 11 1976    F  California        18.106024           7.516867   \n",
       "2     August 1 1993  G-F         NaN         5.310345           1.425287   \n",
       "3   January 21 1983    G  Pepperdine         2.700000           0.966667   \n",
       "4    October 6 1990  F-C      Baylor         4.872404           3.498516   \n",
       "\n",
       "   assists_per_game  effective_fgp  efficiency_rating  win_shares  \\\n",
       "0          3.628205          0.559               24.6       273.4   \n",
       "1          2.540964          0.479               19.0        71.2   \n",
       "2          0.505747          0.525                8.8         5.0   \n",
       "3          0.533333          0.413                8.5        -0.2   \n",
       "4          0.643917          0.513               11.2         8.7   \n",
       "\n",
       "   value_over_replacement  true_shooting_pct  steals_per_game  \\\n",
       "0                    85.7              0.592         0.743590   \n",
       "1                    21.9              0.549         0.987952   \n",
       "2                     0.1              0.552         0.528736   \n",
       "3                    -0.2              0.420         0.200000   \n",
       "4                    -0.1              0.553         0.415430   \n",
       "\n",
       "   blocks_per_game  minutes_per_game  \n",
       "0         2.044231         36.824359  \n",
       "1         0.768675         34.797590  \n",
       "2         0.126437         15.959770  \n",
       "3         0.133333          7.800000  \n",
       "4         0.400593         16.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xq35dwzYdoNp"
   },
   "source": [
    "We decided to filter out all players that had data missing for their steals or blocks and that played less than 10 mins per game. An NBA game is 48 mins and any player that is in the discussion for **'Hall of Fame'** has played at least 800-1000 mins during every season in their career."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZnsUEpiRdoNq"
   },
   "outputs": [],
   "source": [
    "players3.drop(players3[players3['steals_per_game'] < 0].index, inplace = True)\n",
    "players3.drop(players3[players3['blocks_per_game'] < 0].index, inplace = True)\n",
    "players3.drop(players3[players3['minutes_per_game'] < 10].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XgghjbyodoNq"
   },
   "outputs": [],
   "source": [
    "players_df = players3[['player_name', 'player_ref_id', 'From','To','points_per_game', 'rebounds_per_game', 'assists_per_game', 'effective_fgp', 'efficiency_rating', 'win_shares', 'value_over_replacement', 'true_shooting_pct', 'steals_per_game', 'blocks_per_game', 'minutes_per_game']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tFmdq1qkdoNr"
   },
   "outputs": [],
   "source": [
    "def is_hof(x):\n",
    "    if '*' in x:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nNWa7NVjdoNs",
    "outputId": "ee61d1a9-948f-482a-ee8c-3185b8bcdc64"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "players_df['hof'] = players_df['player_name'].apply(is_hof)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V8b5g7ZIvdDa"
   },
   "source": [
    "In the following code blocks, we store the MVP winners for each year and get the number of MVPs each player has won. Interestingly, every player who has ever won the MVP award is either already in the Hall of Fame, or will almost certainly make it once they retire. The lone exception is Derrick Rose, who may not end up making it due to his career being shortened from injuries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hKMVNujYdoNt"
   },
   "outputs": [],
   "source": [
    "mvp_players = {'Bob Pettit*': [1956, 1959],\n",
    "              'Bob Cousy*': [1957],\n",
    "              'Bill Russell*': [1958, 1961, 1962, 1963, 1965],\n",
    "              'Wilt Chamberlain*': [1960, 1966, 1967, 1968],\n",
    "              'Oscar Robertson*': [1964],\n",
    "              'Wes Unseld*': [1969],\n",
    "              'Willis Reed*': [1970],\n",
    "              'Kareem Abdul-Jabbar*': [1971, 1972, 1974, 1976, 1977, 1980],\n",
    "              'Dave Cowens*': [1973],\n",
    "              'Bob McAdoo*': [1975],\n",
    "              'Bill Walton*': [1978],\n",
    "              'Moses Malone*': [1979, 1982, 1983],\n",
    "              'Julius Erving*': [1981],\n",
    "              'Larry Bird*': [1984, 1985, 1986],\n",
    "              'Magic Johnson*': [1987, 1989, 1990],\n",
    "              'Michael Jordan*': [1988, 1991, 1992, 1996, 1998],\n",
    "              'Charles Barkley*': [1993],\n",
    "              'Hakeem Olajuwon*': [1994],\n",
    "              'David Robinson*': [1995],\n",
    "              'Karl Malone*': [1997, 1999],\n",
    "              'Shaquille O\\'Neal*': [2000],\n",
    "              'Allen Iverson*': [2001],\n",
    "              'Tim Duncan': [2002, 2003],\n",
    "              'Kevin Garnett': [2004],\n",
    "              'Steve Nash': [2005, 2006],\n",
    "              'Dirk Nowitzki': [2007],\n",
    "              'Kobe Bryant': [2008],\n",
    "              'LeBron James': [2009, 2010, 2012, 2013],\n",
    "              'Derrick Rose': [2011],\n",
    "              'Kevin Durant': [2014],\n",
    "              'Stephen Curry': [2015, 2016],\n",
    "              'Russell Westbrook': [2017],\n",
    "              'James Harden': [2018],\n",
    "              'Giannis Antetokounmpo': [2019,2020]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ucHCdCZWdoNt",
    "outputId": "a23d6dbc-aabf-45d7-c879-5dd69486fc6a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:1763: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(loc, value)\n"
     ]
    }
   ],
   "source": [
    "players_df['MVP'] = 0\n",
    "for i, row in players_df.iterrows():  \n",
    "    for j, v in mvp_players.items():\n",
    "        if row['player_name'] == j:\n",
    "            players_df.loc[i, 'MVP'] = len(list(v))\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gkbzaSTqyj4t"
   },
   "source": [
    "Here, we get the champions for each season, and keep count of how many each team has won."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AFjHrYlxdoNu"
   },
   "outputs": [],
   "source": [
    "# Adding nba champions\n",
    "teams_champions = {'BOS': [1957, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1968, 1969, 1974, 1976, 1981, 1984, 1986, 2008],\n",
    "                  'LAL': [1972, 1980, 1982, 1985, 1987, 1988, 2000, 2001, 2002, 2009, 2010, 2020],\n",
    "                  'MNL': [1949, 1950, 1952, 1953, 1954],\n",
    "                  'CHI': [1991, 1992, 1993, 1996, 1997, 1998],\n",
    "                  'GSW': [1975, 2015, 2017, 2018],\n",
    "                  'PHW': [1947, 1956],\n",
    "                  'SAS': [1999, 2003, 2005, 2007, 2014],\n",
    "                  'DET': [1989, 1990, 2004],\n",
    "                  'MIA': [2006, 2012, 2013],\n",
    "                  'PHI': [1967, 1983],\n",
    "                  'SYR': [1955],\n",
    "                  'HOU': [1994, 1995],\n",
    "                  'NYK': [1970, 1973],\n",
    "                  'STL': [1958],\n",
    "                  'BLB': [1948],\n",
    "                  'CLE': [2016],\n",
    "                  'DAL': [2011],\n",
    "                  'MIL': [1971],\n",
    "                  'SEA': [1979],\n",
    "                  'POR': [1977],\n",
    "                  'ROC': [1951],\n",
    "                  'WSB': [1978],\n",
    "                  'TOR': [2019]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P4-LYVXaBr7o"
   },
   "source": [
    "We use that to go through the season-wise statistics of each player, decide whether they were a champion that season and aggregate that info to get how many championships each player has won."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qB4y-kK5doNu"
   },
   "outputs": [],
   "source": [
    "stats['Champion'] = 0\n",
    "for i, row in stats.iterrows():  \n",
    "    for k, v in teams_champions.items():\n",
    "        for year in v:\n",
    "            if row['Tm'] != k:\n",
    "                break\n",
    "            elif(row['Year'] == year) & (row['Tm'] == k):\n",
    "                stats.loc[i, 'Champion'] = 1\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CrKztfTWkCd-"
   },
   "outputs": [],
   "source": [
    "stats['championships'] = stats[['Champion','Player']].groupby('Player').cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PAwUPKV6vriD"
   },
   "outputs": [],
   "source": [
    "champions = stats[['Champion', 'Player', 'championships']].groupby(['Player'], sort=False)['championships'].max().reset_index()\n",
    "champions = champions.sort_values(by='championships', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gMMDfrLhvrt4"
   },
   "outputs": [],
   "source": [
    "players_df = players_df.merge(champions, how='left', left_on='player_name', right_on='Player')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KO2ofQBZdoNw"
   },
   "outputs": [],
   "source": [
    "players_df = players_df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "80I8tbp66sC_"
   },
   "source": [
    "Let's also take a look at how many All-Star selections each player has had.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kNsmMcc2doNw"
   },
   "outputs": [],
   "source": [
    "all_stars = pd.read_csv('all_star_game_counts.csv', encoding= 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "7Twv6HVn4e9l",
    "outputId": "a53692c2-d357-4623-bdb3-7bd50771cdd0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Name</th>\n",
       "      <th>Games</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abdulka01</td>\n",
       "      <td>Kareem Abdul-Jabbar</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>jamesle01</td>\n",
       "      <td>LeBron James</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bryanko01</td>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>duncati01</td>\n",
       "      <td>Tim Duncan</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>garneke01</td>\n",
       "      <td>Kevin Garnett</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                 Name  Games\n",
       "0  abdulka01  Kareem Abdul-Jabbar     18\n",
       "1  jamesle01         LeBron James     16\n",
       "2  bryanko01          Kobe Bryant     15\n",
       "3  duncati01           Tim Duncan     15\n",
       "4  garneke01        Kevin Garnett     14"
      ]
     },
     "execution_count": 93,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_stars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q3pcGhggCAzv"
   },
   "source": [
    "Merging this count of all-star selections for each player gives us the final list of statistics that we would continue our analysis on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LHprSHQ37-I4"
   },
   "outputs": [],
   "source": [
    "players_df = players_df.merge(all_stars, how='left', left_on='player_ref_id', right_on='id').drop(['id', 'Name'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "P6Q6Dr5B8Uw8"
   },
   "outputs": [],
   "source": [
    "players_df = players_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "icoBE07T8s9I"
   },
   "outputs": [],
   "source": [
    "players_df = players_df.rename(columns={\"Games\": 'count_all_stars'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 481
    },
    "id": "QHIbNCoE8gyq",
    "outputId": "7c3930f5-81b7-4846-f1d6-4d97abcc47b1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_ref_id</th>\n",
       "      <th>From</th>\n",
       "      <th>points_per_game</th>\n",
       "      <th>rebounds_per_game</th>\n",
       "      <th>assists_per_game</th>\n",
       "      <th>effective_fgp</th>\n",
       "      <th>efficiency_rating</th>\n",
       "      <th>win_shares</th>\n",
       "      <th>value_over_replacement</th>\n",
       "      <th>true_shooting_pct</th>\n",
       "      <th>steals_per_game</th>\n",
       "      <th>blocks_per_game</th>\n",
       "      <th>minutes_per_game</th>\n",
       "      <th>hof</th>\n",
       "      <th>MVP</th>\n",
       "      <th>championships</th>\n",
       "      <th>count_all_stars</th>\n",
       "      <th>box_plus_minus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kareem Abdul-Jabbar*</td>\n",
       "      <td>abdulka01</td>\n",
       "      <td>1970</td>\n",
       "      <td>24.607051</td>\n",
       "      <td>11.179487</td>\n",
       "      <td>3.628205</td>\n",
       "      <td>0.559</td>\n",
       "      <td>24.6</td>\n",
       "      <td>273.4</td>\n",
       "      <td>85.7</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.743590</td>\n",
       "      <td>2.044231</td>\n",
       "      <td>36.824359</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shareef Abdur-Rahim</td>\n",
       "      <td>abdursh01</td>\n",
       "      <td>1997</td>\n",
       "      <td>18.106024</td>\n",
       "      <td>7.516867</td>\n",
       "      <td>2.540964</td>\n",
       "      <td>0.479</td>\n",
       "      <td>19.0</td>\n",
       "      <td>71.2</td>\n",
       "      <td>21.9</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.987952</td>\n",
       "      <td>0.768675</td>\n",
       "      <td>34.797590</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Álex Abrines</td>\n",
       "      <td>abrinal01</td>\n",
       "      <td>2017</td>\n",
       "      <td>5.310345</td>\n",
       "      <td>1.425287</td>\n",
       "      <td>0.505747</td>\n",
       "      <td>0.525</td>\n",
       "      <td>8.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.528736</td>\n",
       "      <td>0.126437</td>\n",
       "      <td>15.959770</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Quincy Acy</td>\n",
       "      <td>acyqu01</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.872404</td>\n",
       "      <td>3.498516</td>\n",
       "      <td>0.643917</td>\n",
       "      <td>0.513</td>\n",
       "      <td>11.2</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.415430</td>\n",
       "      <td>0.400593</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jaylen Adams</td>\n",
       "      <td>adamsja01</td>\n",
       "      <td>2019</td>\n",
       "      <td>3.176471</td>\n",
       "      <td>1.764706</td>\n",
       "      <td>1.911765</td>\n",
       "      <td>0.459</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.474</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>12.588235</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Steven Adams</td>\n",
       "      <td>adamsst01</td>\n",
       "      <td>2014</td>\n",
       "      <td>9.794340</td>\n",
       "      <td>7.601887</td>\n",
       "      <td>1.160377</td>\n",
       "      <td>0.589</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.2</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.884906</td>\n",
       "      <td>1.001887</td>\n",
       "      <td>26.805660</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bam Adebayo</td>\n",
       "      <td>adebaba01</td>\n",
       "      <td>2018</td>\n",
       "      <td>10.547085</td>\n",
       "      <td>7.681614</td>\n",
       "      <td>2.928251</td>\n",
       "      <td>0.555</td>\n",
       "      <td>18.4</td>\n",
       "      <td>19.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.829596</td>\n",
       "      <td>0.892377</td>\n",
       "      <td>25.551570</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Deng Adel</td>\n",
       "      <td>adelde01</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.684211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.389</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>10.210526</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jeff Adrien</td>\n",
       "      <td>adrieje01</td>\n",
       "      <td>2011</td>\n",
       "      <td>4.633987</td>\n",
       "      <td>4.307190</td>\n",
       "      <td>0.653595</td>\n",
       "      <td>0.474</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.326797</td>\n",
       "      <td>0.516340</td>\n",
       "      <td>14.039216</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arron Afflalo</td>\n",
       "      <td>afflaar01</td>\n",
       "      <td>2008</td>\n",
       "      <td>10.758530</td>\n",
       "      <td>2.855643</td>\n",
       "      <td>1.818898</td>\n",
       "      <td>0.514</td>\n",
       "      <td>11.9</td>\n",
       "      <td>33.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.447507</td>\n",
       "      <td>0.185039</td>\n",
       "      <td>27.334646</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            player_name player_ref_id  From  points_per_game  \\\n",
       "0  Kareem Abdul-Jabbar*     abdulka01  1970        24.607051   \n",
       "1   Shareef Abdur-Rahim     abdursh01  1997        18.106024   \n",
       "2          Álex Abrines     abrinal01  2017         5.310345   \n",
       "3            Quincy Acy       acyqu01  2013         4.872404   \n",
       "4          Jaylen Adams     adamsja01  2019         3.176471   \n",
       "5          Steven Adams     adamsst01  2014         9.794340   \n",
       "6           Bam Adebayo     adebaba01  2018        10.547085   \n",
       "7             Deng Adel      adelde01  2019         1.684211   \n",
       "8           Jeff Adrien     adrieje01  2011         4.633987   \n",
       "9         Arron Afflalo     afflaar01  2008        10.758530   \n",
       "\n",
       "   rebounds_per_game  assists_per_game  effective_fgp  efficiency_rating  \\\n",
       "0          11.179487          3.628205          0.559               24.6   \n",
       "1           7.516867          2.540964          0.479               19.0   \n",
       "2           1.425287          0.505747          0.525                8.8   \n",
       "3           3.498516          0.643917          0.513               11.2   \n",
       "4           1.764706          1.911765          0.459                7.6   \n",
       "5           7.601887          1.160377          0.589               17.2   \n",
       "6           7.681614          2.928251          0.555               18.4   \n",
       "7           1.000000          0.263158          0.389                2.7   \n",
       "8           4.307190          0.653595          0.474               15.0   \n",
       "9           2.855643          1.818898          0.514               11.9   \n",
       "\n",
       "   win_shares  value_over_replacement  true_shooting_pct  steals_per_game  \\\n",
       "0       273.4                    85.7              0.592         0.743590   \n",
       "1        71.2                    21.9              0.549         0.987952   \n",
       "2         5.0                     0.1              0.552         0.528736   \n",
       "3         8.7                    -0.1              0.553         0.415430   \n",
       "4         0.1                    -0.2              0.474         0.411765   \n",
       "5        45.2                     8.6              0.596         0.884906   \n",
       "6        19.5                     5.7              0.599         0.829596   \n",
       "7        -0.2                    -0.3              0.424         0.052632   \n",
       "8         4.4                    -0.1              0.518         0.326797   \n",
       "9        33.5                     2.4              0.556         0.447507   \n",
       "\n",
       "   blocks_per_game  minutes_per_game  hof  MVP  championships  \\\n",
       "0         2.044231         36.824359    1    6            6.0   \n",
       "1         0.768675         34.797590    0    0            0.0   \n",
       "2         0.126437         15.959770    0    0            0.0   \n",
       "3         0.400593         16.000000    0    0            0.0   \n",
       "4         0.147059         12.588235    0    0            0.0   \n",
       "5         1.001887         26.805660    0    0            0.0   \n",
       "6         0.892377         25.551570    0    0            0.0   \n",
       "7         0.210526         10.210526    0    0            0.0   \n",
       "8         0.516340         14.039216    0    0            0.0   \n",
       "9         0.185039         27.334646    0    0            0.0   \n",
       "\n",
       "   count_all_stars  box_plus_minus  \n",
       "0             18.0             5.7  \n",
       "1              1.0             1.0  \n",
       "2              0.0            -1.9  \n",
       "3              0.0            -2.0  \n",
       "4              0.0            -4.3  \n",
       "5              0.0             0.4  \n",
       "6              1.0             2.0  \n",
       "7              0.0            -7.5  \n",
       "8              0.0            -2.2  \n",
       "9              0.0            -1.5  "
      ]
     },
     "execution_count": 105,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3jqDssa_jXtc"
   },
   "outputs": [],
   "source": [
    "players_df.to_csv('player_df_final.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_VdFBiANdoNx"
   },
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qEVtAkDMdoNy"
   },
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pN_78RvFas_3"
   },
   "source": [
    "We split the dataframe being used for analysis into Hall of Famers and Non-Hall of Famers. This not only helps Data Visualization, but is also used later for an even train test split for training our models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "i08RccGTKsX6"
   },
   "outputs": [],
   "source": [
    "hof = players_df[players_df['hof']==1]\n",
    "not_hof = players_df[players_df['hof']==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H3tLUJGg9LGd"
   },
   "source": [
    "Let's start off with the most basic distinction that we can make between players: the number of points they score. Below, we plot a distribution of points scored per game for Hall of Famers and non-Hall of Famers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 653
    },
    "id": "8C_887aJdoNy",
    "outputId": "eb961770-9154-41dd-ab89-75fbd69fcbb1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Points Per Game Distribution for all players')"
      ]
     },
     "execution_count": 250,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,10))\n",
    "sns.distplot(hof['points_per_game'], bins=25, label=\"Hall of Famer\")\n",
    "sns.distplot(not_hof['points_per_game'], bins=25, label=\"Not a Hall of Famer\")\n",
    "plt.xlabel(\"Points Per Game\")\n",
    "plt.ylabel(\"Distribution\")\n",
    "plt.yticks(fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.legend(loc=1, frameon=False, prop={'size': 12})\n",
    "plt.title('Points Per Game Distribution for all players', fontsize=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y2F7nYs690pr"
   },
   "source": [
    "As expected, Hall of Famers score significantly higher points per game on average as compared to Non-Hall of Famers. For our dataset, we see that Non-Hall of Famers score about 5 points-per-game on average while Hall of Famers score about 18 points per game on average. This shows us that points-per-game is a very predictive feature for whether or not a player is in the Hall of Fame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 533
    },
    "id": "ItkkAgNkjxkb",
    "outputId": "b5367da5-c145-4370-8ae4-51a3649361f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Normal Statistics separated by Hall of Fame Status')"
      ]
     },
     "execution_count": 251,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_by_hof = players_df.groupby('hof')['points_per_game', 'rebounds_per_game', 'assists_per_game'] # statistics grouped by hof status\n",
    "sort_by_hof = sort_by_hof.mean().transpose()  # average stats, transposed for graphing purposes\n",
    "sort_by_hof = sort_by_hof.rename({1: 'Hall of Famer', \n",
    "                                  0: 'Not Hall of Famer'}, \n",
    "                                  axis='columns')\n",
    "sort_by_hof = sort_by_hof.rename({'points_per_game': 'Points Per Game',\n",
    "                                  'rebounds_per_game': 'Rebounds Per Game',\n",
    "                                  'assists_per_game': 'Assists Per Game'},\n",
    "                                   axis='index')\n",
    "sort_by_hof = sort_by_hof[sort_by_hof.columns[::-1]] # reverse column order\n",
    "sort_by_hof.plot(kind='bar', figsize=(16,8), alpha=0.5, rot=0)\n",
    "\n",
    "plt.legend(prop={'size':15}, frameon=True, loc=1)\n",
    "plt.xticks(size = 15)\n",
    "plt.yticks(size = 15)\n",
    "plt.title('Normal Statistics separated by Hall of Fame Status', fontsize=28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TXq3eQKqC4L3"
   },
   "source": [
    "Here, we’ve separated points per game, rebounds per game, and assists per game for Hall-of-Famers and non-Hall-of-Famers. As we can see, the numbers are significantly higher for those in the Hall-of-Fame. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 533
    },
    "id": "XGgmw0cshSk1",
    "outputId": "8d89ed2f-22a8-46b3-8cf9-5ae531be63cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Advanced Statistics separated by Hall of Fame Status')"
      ]
     },
     "execution_count": 252,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_by_hof = players_df.groupby('hof')['value_over_replacement', 'efficiency_rating', 'win_shares'] # statistics grouped by hof status\n",
    "sort_by_hof = sort_by_hof.mean().transpose()  # average stats, transposed for graphing purposes\n",
    "sort_by_hof = sort_by_hof.rename({1: 'Hall of Famer', \n",
    "                                  0: 'Not Hall of Famer'}, \n",
    "                                  axis='columns')\n",
    "sort_by_hof = sort_by_hof.rename({'value_over_replacement': 'Value Over Replacement Player',\n",
    "                                  'efficiency_rating': 'Player Efficiency Rating',\n",
    "                                  'win_shares': 'Win Shares'},\n",
    "                                   axis='index')\n",
    "sort_by_hof = sort_by_hof[sort_by_hof.columns[::-1]] # reverse column order\n",
    "sort_by_hof.plot(kind='bar', figsize=(16,8), alpha=0.5, rot=0)\n",
    "\n",
    "plt.legend(prop={'size':15}, frameon=True, loc=1)\n",
    "plt.xticks(size = 15)\n",
    "plt.yticks(size = 15)\n",
    "plt.title('Advanced Statistics separated by Hall of Fame Status', fontsize=28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "epA75-G5DAsk"
   },
   "source": [
    "For the advanced statistics, we decided to choose VORP, PER, and win shares. VORP (value over replacement player) estimates how much value a player adds to their team, calculated as points added per 100 team possessions, where -2.0 is average. PER is an all-in-one rating, where all aspects of a player’s performance are taken into account to return one number, where 15 is average, 20+ is very good, and anything above 30 is all-time great. Win shares attempts to award each player a portion of the team’s success, based on how much they were responsible for their wins. As expected, the average for Hall-of-Famers is distinctly higher in each category. However, the margin between them for cumulative statistics (VORP, win shares) was much higher than for the rate statistic (PER). This suggests that longevity is valued highly for Hall-of-Fame consideration.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 653
    },
    "id": "fzllzERfKY3K",
    "outputId": "f3ec3d5d-3528-46f8-d322-019081c7f3cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of all star games - distribution for all players')"
      ]
     },
     "execution_count": 253,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,10))\n",
    "sns.distplot(hof['count_all_stars'], bins=25, label=\"Hall of Famer\")\n",
    "sns.distplot(not_hof['count_all_stars'], bins=25, label=\"Not a Hall of Famer\")\n",
    "plt.xlabel(\"Number of All Star Games\")\n",
    "plt.ylabel(\"Distribution\")\n",
    "plt.yticks(fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.legend(loc=1, frameon=False, prop={'size': 12})\n",
    "plt.title('Number of all star games - distribution for all players', fontsize=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CX8Lmuh7ZKfb"
   },
   "source": [
    "Very interesting. Looking at All-Star selections for Hall-of-Famers vs non-Hall-of-Famers shows a stark contrast. The overwhelming majority of non-Hall-of-Famers have never been selected to an All-Star game, with a few having been selected to one or two. For Hall-of-Famers, the bulk of them have been selected to around 5-10, with outliers on either side. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "38Zs3b-KdoN0"
   },
   "source": [
    "Let us try to visualize the correlations between the different features that we \n",
    "have (as stats) in order to move ahead with our analysis. We will plot a heatmap of all the features and annotate it with the pairwise correlations between features to identify the most important features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 708
    },
    "id": "-DHMi4PvdoN0",
    "outputId": "02d41d63-d2e0-42fb-fa4d-ba9f6bafed97"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise correlation matrix\n",
    "corMatrix = players_df.iloc[:,2:].corr()\n",
    "fig, ax = plt.subplots(figsize = (19,10))\n",
    "sns.heatmap(corMatrix, annot=True, ax = ax, vmin = -1, vmax = 1, mask = np.zeros_like(corMatrix, dtype = np.bool),\n",
    "            cmap = sns.diverging_palette(220, 20, as_cmap = True), square = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ejy4-_MF-1YA"
   },
   "source": [
    "From the heatmap, it is clear that win shares, player efficiency rating, points per game, number of all star games played, and number of MVP awards and almost all the other stats are strongly, positively correlated to being in the Hall of Fame. With these features in mind, let's move ahead to see how we can use them to predict future Hall-of-Famers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hv9arYIYukSW"
   },
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3KWpxX_R4p1U"
   },
   "source": [
    "Let's first write a function that can show us the most important metrics after testing each of our models. We use scikit-learn's brier_score_loss as that type of loss helps us predict probabilities of players to make the Hall of Fame without too many high errors as it specifically eliminates them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kmUkuKEpdoN1"
   },
   "outputs": [],
   "source": [
    "def show_metrics(model, features):\n",
    "    accuracy_train = accuracy_score(model.predict(X_train[features]), y_train)\n",
    "    accuracy_valid = accuracy_score(model.predict(X_val[features]), y_val)\n",
    "    \n",
    "    loss_train = brier_score_loss(y_train, model.predict_proba(X_train[features])[:, 1])\n",
    "    loss_valid = brier_score_loss(y_val, model.predict_proba(X_val[features])[:, 1])\n",
    "    \n",
    "    print('Training Accuracy: \\n'+str(accuracy_train), '\\n Validation Accuracy: \\n'+str(accuracy_valid), '\\n Training Loss: \\n'+str(loss_train), '\\n Validation Loss: \\n'+str(loss_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8VuEuYhC3Dcl"
   },
   "source": [
    "### Hypothesis Test\n",
    "\n",
    "At the start of the project, during data curation and cleaning, we had picked certain features that we thought would be extremely predictive of whether or not a player makes the Hall of Fame. Let us test whether we made the right choice in picking those features and how important/predictive each of those features are. To do this, let's start with the null hypothesis that all of the features are equally important in determining whether or not a player will make the Hall of Fame. \n",
    "\n",
    "In order to test this hypothesis, we are going to try out a Logistic Regression model on our data and use the [scikit-learn's](https://scikit-learn.org/stable/user_guide.html) recursive feature elimination, which recursively removes out features at every step of the training process using the coefficients of the features gained from the previous step. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "c0rcktJ1qz4V"
   },
   "outputs": [],
   "source": [
    "train_hof, valid_hof = sklearn.model_selection.train_test_split(hof[hof['To']<=2016], test_size=0.4)\n",
    "train_not_hof, valid_not_hof = sklearn.model_selection.train_test_split(not_hof[not_hof['To']<=2016], test_size=0.4)\n",
    "train = pd.concat([train_hof, train_not_hof], ignore_index=True).sample(frac=1)\n",
    "valid = pd.concat([valid_hof, valid_not_hof], ignore_index=True).sample(frac=1)\n",
    "test = players_df[(players_df.To > 2016)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hFC2uogE7ycn",
    "outputId": "c841ad8d-a323-45e3-8b86-607bd9bbf06c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(401, 269, 675)"
      ]
     },
     "execution_count": 210,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train), len(valid), len(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kH2Wif8_IoHN"
   },
   "source": [
    "We can see that our validation set is extremely small, which might cause inaccuracies in terms of validating our training predictions, but since the number of players that are currently in the Hall of Fame is only 150-170 players, it makes sense that our data to train and predict on is going to be relatively small in comparison to traditional datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WBvMG2iHqz1T"
   },
   "outputs": [],
   "source": [
    "features = ['points_per_game', 'rebounds_per_game', 'assists_per_game', \n",
    "            'effective_fgp', 'efficiency_rating', 'win_shares', \n",
    "            'value_over_replacement', 'true_shooting_pct', 'steals_per_game', \n",
    "            'blocks_per_game', 'minutes_per_game', 'box_plus_minus', 'MVP', \n",
    "            'championships', 'count_all_stars']\n",
    "\n",
    "X_train = train[features]\n",
    "y_train = train[\"hof\"]\n",
    "\n",
    "X_val = valid[features]\n",
    "y_val = valid[\"hof\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_mTeyGAeq0U-",
    "outputId": "4749eae4-adc1-4ac8-8480-219131638ea0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 best features according to RFE:\n",
      "['points_per_game', 'rebounds_per_game', 'assists_per_game', 'effective_fgp', 'efficiency_rating', 'win_shares', 'value_over_replacement', 'true_shooting_pct', 'steals_per_game', 'minutes_per_game', 'box_plus_minus', 'MVP', 'championships', 'count_all_stars']\n",
      "Training Accuracy: \n",
      "0.9775561097256857 \n",
      " Validation Accuracy: \n",
      "0.9442379182156134 \n",
      " Training Loss: \n",
      "0.018325233343505023 \n",
      " Validation Loss: \n",
      "0.04220760680413179\n"
     ]
    }
   ],
   "source": [
    "# start with max loss to iteratively reduce it\n",
    "min_loss = 1\n",
    "selector_best = None\n",
    "\n",
    "# Loop to go through all 15 possible features and select best combination \n",
    "# and best number of features for Logistic Regression Model\n",
    "for i in range(1, 15):\n",
    "    selector = RFE(estimator = LogisticRegression(max_iter=150), n_features_to_select=i)\n",
    "\n",
    "    rfe_pipe = make_pipeline(StandardScaler(), selector)\n",
    "    \n",
    "    rfe_pipe.fit(X_train[features], y_train)\n",
    "    loss = brier_score_loss(y_val, rfe_pipe.predict_proba(X_val)[:, 1])\n",
    "    \n",
    "    if loss < min_loss:\n",
    "        min_loss = loss\n",
    "        selector_best = clone(selector)\n",
    "\n",
    "rfe_pipe = make_pipeline(StandardScaler(), selector_best)\n",
    "\n",
    "rfe_pipe.fit(X_train, y_train)\n",
    "rfe_support = selector_best.get_support()\n",
    "rfe_features = X_train.loc[:, rfe_support].columns\n",
    "\n",
    "print(str(len(rfe_features)), 'best features according to RFE:')\n",
    "print(rfe_features.tolist())\n",
    "show_metrics(rfe_pipe, features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h_QdzW7XAwil"
   },
   "source": [
    "Nice! Recursive Feature Elimination just selected 6 of the most important features using a Logistic Regression model, that it thought were the biggest predictors for a player being in the Hall of Fame. And it validated that pretty well on our validation set (although of small size). Now that we have this logistic regression model trained, let's try and see what the exact coefficients of these features were to understand whether our model works as intended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "id": "03carO0Wq0Y3",
    "outputId": "1b43a93f-3bf5-4223-ec02-6029ff50b351"
   },
   "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>Coefs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>points_per_game</th>\n",
       "      <td>1.359051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rebounds_per_game</th>\n",
       "      <td>0.619154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assists_per_game</th>\n",
       "      <td>0.886474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>effective_fgp</th>\n",
       "      <td>-0.671963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>efficiency_rating</th>\n",
       "      <td>0.258912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>win_shares</th>\n",
       "      <td>0.563947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value_over_replacement</th>\n",
       "      <td>-0.926240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>true_shooting_pct</th>\n",
       "      <td>0.805020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>steals_per_game</th>\n",
       "      <td>-0.306677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minutes_per_game</th>\n",
       "      <td>-0.430463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>box_plus_minus</th>\n",
       "      <td>0.209310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MVP</th>\n",
       "      <td>0.053101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>championships</th>\n",
       "      <td>0.311789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_all_stars</th>\n",
       "      <td>1.665613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Coefs\n",
       "points_per_game         1.359051\n",
       "rebounds_per_game       0.619154\n",
       "assists_per_game        0.886474\n",
       "effective_fgp          -0.671963\n",
       "efficiency_rating       0.258912\n",
       "win_shares              0.563947\n",
       "value_over_replacement -0.926240\n",
       "true_shooting_pct       0.805020\n",
       "steals_per_game        -0.306677\n",
       "minutes_per_game       -0.430463\n",
       "box_plus_minus          0.209310\n",
       "MVP                     0.053101\n",
       "championships           0.311789\n",
       "count_all_stars         1.665613"
      ]
     },
     "execution_count": 215,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit = LogisticRegression()\n",
    "logreg_pipe = make_pipeline(StandardScaler(), logit)\n",
    "logreg_pipe.fit(X_train[rfe_features], y_train)\n",
    "\n",
    "pd.DataFrame({\"Coefs\": logit.coef_.reshape((-1))}, index=rfe_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "czIqKe7qB0VX"
   },
   "source": [
    "Interesting. We see that a number of features are negative. This is interesting because all of these stats are advanced stats which measure how a player contributes to the game. For example:\n",
    "- Value Over Replacement Player measures the total number of points per 100 team possessions the player contributed compared to a replacement-level player (who has an average score of -2.0). This value is prorated for an 82-game season.\n",
    "- Effective Field Goal % measures the player’s field goal percentage while giving extra weight to 3-point field goals. Percentage ranges from 0-1.\n",
    "\n",
    "Any basketball fan would assume and often base their argument about why a player should be in the Hall of Fame using these stats, so it is surprising that they have negative coefficients. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cfWFSwcqEuO4"
   },
   "source": [
    "Let's try the same set of steps using a different feature selection tool called Select From Model and use a Random Forest Classifier to see if we get different results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "id": "I7ot8RbqdoN1",
    "outputId": "12924890-ea4c-4c68-a2d5-ea9662b1e7fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 best features according to SelectFromModel:\n",
      "['points_per_game', 'efficiency_rating', 'count_all_stars']\n",
      "Training Accuracy: \n",
      "0.9950124688279302 \n",
      " Validation Accuracy: \n",
      "0.9144981412639405 \n",
      " Training Loss: \n",
      "0.0060180279802119815 \n",
      " Validation Loss: \n",
      "0.056349568791407297\n"
     ]
    },
    {
     "data": {
      "image/png": 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pymvIWXm5koiIpMWSIyIiabHkiIhIWiw5IiKSFkuOiIikxZIjIiJpseSIiEhaLDkiIpIWS46IiKTFkiMiImmx5IiISFosOSIikha/hcDA8FsIiEgf6vJNAPr+Bc38FoKniEJhhOdmxeo7BhH9xaQtDkSRvkM0AF6uJCIiabHkiIhIWiw5IiKSFkuOiIikxZIjIiJpseSIiEhaLDkiIpIWS46IiKTFkiMiImmx5IiISFosOSIikhZLjoiIpMWSIyIiabHkiIhIWiw5IiKSFkuOiIikxZIjIiJpseSIiEhaLDkiIpIWS46IiKTFkiMiImmx5IiISFosOSIikhZLjoiIpFXnkjt06BAGDRqEUaNGAQD+/ve/Y8iQIdiwYQNWrFiBhISEasdevHgR8+fPr2uEBnf58mUcP35ce//o0aOIiYnRYyIiItKFSV0n2LlzJz799FO4u7sjJycHFy9exI8//qjT2K5du6Jr1651jVAvKisrYWLy6MNx+fJlJCUloX///gAAHx8f+Pj4NGY8IiKqBSMhhNB153379mHTpk2oqKhA9+7dYWtri3Xr1sHW1hbe3t44efIkbty4AQcHB8ydOxc7d+7EgAEDoFKpkJiYiIULF6KkpARKpRIbNmzApUuX8PXXX+PLL79ESUkJPv30U6SkpKCyshITJ06Er68vdu/ejbi4OJSWliIjIwO+vr6YOXMmAODEiROIjIyEWq2GlZUV1q9fD5VKhW3btsHa2hoajQb+/v749ttvYW1t/dDzmTVrFpRKJS5fvgw3NzcEBgZiwYIFKCsrQ9OmTbFw4UK0a9cOfn5+uH//Puzs7PDOO+/g/v37SEpKwkcffYRZs2ahefPmSEpKQk5ODmbMmAGVSgWNRoNPPvkEp0+fhr29PUxMTBASEgKVSvXY4/zcrNgn+CMkIqq7tMWBqKhQ12qssbECarWmnhPpztTUuNptOp/Jpaam4tChQ9i6dStMTU3x8ccfo127dujSpQtmzpyJrl274o033sD48eOxb98+AA/O8gCgvLwcU6dORWRkJLp164aioiI0bdq0yvxr1qyBp6cnFi1ahIKCAoSGhuLll18G8OBMau/evVAqlVCpVBg1ahSaNGmCuXPnYvPmzWjfvj3y8/OhUCgQFBSE7777DuHh4UhISICLi8sjC+4P2dnZ2LZtG4yNjVFUVIQtW7bAxMQECQkJiIyMRFRUFCZNmqQtNQDYvXt3lTl+//13fPPNN/jvf/+LCRMmQKVS4YcffkBWVhYOHjyIO3fuICAgACEhIboebiKiRpefX1KrcZaW5rUeWx9sbCyq3aZzyZ06dQpJSUl47bXXAAD3799Hq1atdBp7/fp12NjYoFu3bgCA5s2bP7TPyZMnERcXh6+//hoAUFZWhlu3bgEAevfuDQuLB0/ihRdeQFZWFgoKCuDu7o727dsDACwtLQEAISEhiIiIQHh4OHbt2oVXX321xmwqlQrGxg9+CigsLMT777+PGzduwMjICBUVFTo9P19fXygUCjg6OiI3NxcAcOHCBahUKigUCtjY2MDDw0OnuYiIqP7oXHJCCAQHB2PatGlVHv/jAyf1YeXKlXj++eerPPbbb79BqVRq7xsbG0Otrv6U2t7eHq1atcKpU6eQmJiI5cuX17immZmZ9vaKFSvg4eGBL774ApmZmQgLC9Mp95/zERGR4dD505W9e/fG999/jzt37gAA8vPzkZWVpdNYBwcH5OTkIDExEQBQVFSEysrKKvt4eXlh8+bN+OMtwuTk5Brn7NGjB86fP4+MjAxtnj+EhoZq3xv74yxNF4WFhbCzswMA7NmzR/t4s2bNUFxcrPM8AODm5oYffvgBGo0Gubm5OHv27BONJyKiutO55BwdHTFlyhSMHTsWQ4YMwdixY5GTk6PTWKVSicjISMyfPx9BQUEYO3YsysrKquwTERGByspKBAUFITAwECtWrKhxTmtra3zyySd47733EBQUhKlTp2q3eXt7o6Sk5LGXKv/XuHHj8Nlnn2HYsGFVStjDwwPXrl3D0KFDcfDgQZ3m8vf3h52dHQICAjBjxgx06tRJe8mViIgaxxN9uvJpcfHiRSxatAjffPONXnMUFxejWbNmyMvLQ2hoKLZu3QobG5vHjuOnK4mosaUtDkROTmGtxkrxwZOnRUxMDLZu3Yply5bpOwrGjx+PgoICVFRUICIiQqeCIyKi+iPlmdz/io6OxuHDh6s8plKpMGHCBD0lqhnP5IiosfFM7ik2YcIEgy00IiJqOPwFzUREJC2WHBERSYslR0RE0mLJERGRtFhyREQkLZYcERFJiyVHRETSYskREZG0WHJERCQtlhwREUmLJUdERNJiyRERkbRYckREJC2WHBERSYslR0RE0mLJERGRtFhyREQkLZYcERFJy0gIIfQdgv6fRiOgUBjpOwYR/cWUllWiqKC0VmMtLc2Rn19Sz4l0Z2NjUe02k0bMQTrKySnUdwSd6Psv9pNg1obBrA3nactrqHi5koiIpMWSIyIiabHkiIhIWiw5IiKSFkuOiIikxZIjIiJpseSIiEhaLDkiIpIWS46IiKTFkiMiImmx5IiISFosOSIikha/hcDA8FsIiP66/vxNAE/TL2jWd1Z+C8FTRKEwwnOzYvUdg4j0IG1xIIr0HUIyvFxJRETSYskREZG0WHJERCQtlhwREUmLJUdERNJiyRERkbRYckREJC2WHBERSYslR0RE0mLJERGRtFhyREQkLZYcERFJiyVHRETSYskREZG0WHJERCQtlhwREUmLJUdERNJiyRERkbRYckREJC2WHBERSYslR0RE0mLJERGRtFhyREQkLZYcERFJq95Lbs6cObh27VqN+xw5cuSx+xAREdVVvZfcggUL4OjoWOM++ig5IQQ0Gk2jrklERPpl8rgdMjMzMW7cOHTu3BnJycno2LEjlixZgl9//RVLliyBWq1Gly5d8I9//ANKpRKjRo3CzJkz0bVrV7i6uiIsLAzHjh1D06ZNsXr1aqSnpyMuLg5nz55FdHQ0oqKiEB8fj23btsHY2BiOjo6IjIx8ZJaoqCikp6cjPT0deXl5GDduHIYPHw4AWLt2LQ4dOoTy8nIMHDgQkyZNQmZmJt566y10794dly5dQkxMDJ555pmH5t2xYwfWrl0LCwsLuLi4QKlU4qOPPkJcXByio6NRUVEBS0tLLF++HK1bt0ZUVBQyMzORkZGBW7du4YMPPsCvv/6Kn376Cba2tlizZg1MTU2RlJSExYsXo6SkBFZWVli0aBFsbW3r+EdGRES6emzJAcD169exYMECvPTSS/jggw+wfv16fPvtt9iwYQMcHBwwc+ZMfPPNNwgPD68yrqSkBN27d8fUqVOxdOlSbN++HREREfD29saAAQOgUqkAADExMYiLi4NSqURBQUGNWf7zn/9g+/btKCkpQXBwMPr374+UlBTcuHEDO3fuhBACEyZMwLlz52Bvb48bN25gyZIl6NGjxyPny87ORnR0NHbv3o1mzZph9OjRcHFxAQC89NJL2L59O4yMjLRFOGvWLABAeno6Nm7ciNTUVPztb3/DypUrMXPmTLz77rs4fvw4+vfvj/nz52P16tWwtrbGwYMHERkZiUWLFulyyInoL8rS0hwAYGys0N42dIacVaeSs7e3x0svvQQACAoKwurVq9GuXTs4ODgAAIKDg7Fly5aHSs7U1BSvvPIKAKBLly74+eefHzm/s7Mzpk+fDh8fH/j6+taYxcfHB02bNkXTpk3h4eGBixcv4sKFC/j5558xbNgwAA/KNS0tDfb29mjbtm21BQcAFy9eRM+ePWFpaQkAUKlUSEtLAwDcvn0bU6dORU5ODsrLy9GuXTvtuH79+sHU1BROTk5Qq9Xo168fAMDJyQmZmZm4fv06rl69ijFjxgAANBoNbGxsanxuRET5+SUAHpTdH7cNnb6z2thYVLtNp5IzMjKqcr9FixbIz89/7DhTU1PtWIVCAbVa/cj9YmJicO7cORw7dgxr1qzB/v37YWLy6Gj/mwV48H7b22+/jREjRlR5PDMzE+bmtf/pYv78+QgPD4ePjw/OnDmDVatWabcplUoAD57Xo56nEAIdO3bEt99+W+v1iYiobnT64MnNmzfxyy+/AAAOHDiALl26ICsrCzdu3AAA7Nu3Dz179tR50WbNmqG4uBjAgzOcW7duwdPTE9OnT0dhYSFKSqr/ieDo0aMoKytDXl4ezp49i65du8LLywu7du3SzpmdnY07d+7olKVr1644d+4c7t27h8rKSvzwww/abYWFhbCzswMA7N27V+fnBwAODg64e/eu9rhVVFQgJSXlieYgIqK60elMzsHBAVu2bMHs2bPh6OiI8PBw9OjRA5MnT9Z+8GTkyJE6LxoQEIC5c+di06ZN+OyzzzBnzhwUFRVBCIGwsDC0aNGi2rHOzs4ICwtDXl4eIiIiYGdnBzs7O6SmpmrP5MzNzbFs2TIoFI/vcDs7O7zzzjsIDQ1Fy5Yt8fzzz8PC4sGp78SJEzF58mS0bNkSHh4eyMzM1Pk5KpVKrFy5EvPnz0dhYSHUajVGjx6Njh076jwHERHVjZEQQtS0Q2ZmJsaPH48DBw40VqZqRUVFwdzcHG+99Va9zltcXIxmzZqhsrISEydOREhICAYOHFivazyJ52bF6m1tItKftMWByMkpBKD/97mehL6z1vk9OdmtWrUKCQkJKCsrg5eX12M//EJERE+Hx5Zcu3btGv0sbteuXdi4cWOVx9zc3DBv3rw6zRsaGory8vIqjy1duhTvv/9+neYlIiLD9NjLldT4eLmS6K+Jlytrp6bLlfwFzUREJC2WHBERSYslR0RE0mLJERGRtFhyREQkLZYcERFJiyVHRETSYskREZG0WHJERCQtlhwREUmLJUdERNJiyRERkbRYckREJC2WHBERSYslR0RE0mLJERGRtFhyREQkLZYcERFJiyVHRETSMtF3AKpKoxFIWxyo7xhEpAelZZX6jiAdlpwByskp1HcEnVhamiM/v0TfMXTCrA2DWcnQ8XIlERFJiyVHRETSYskREZG0WHJERCQtlhwREUmLJUdERNJiyRERkbRYckREJC0jIYTQdwgiIqKGwDM5IiKSFkuOiIikxZIjIiJpseSIiEhaLDkiIpIWS46IiKTFkiMiImmx5BrJiRMn4O/vj4EDByImJuah7eXl5ZgyZQoGDhyI0NBQZGZmard9+eWXGDhwIPz9/fHTTz8ZbNbMzEx069YNQ4cOxdChQ/HRRx/pPeu5c+cQHByMTp064fDhw1W27dmzB35+fvDz88OePXsaPGtd87744ovaYzt+/Hi9Z12/fj0CAgIwZMgQjB49GllZWdptjX1s65LV0I7r1q1bMWTIEAwdOhQjR47EtWvXtNsM7bWguqz6eC2olqAGV1lZKXx8fER6erooKysTQ4YMESkpKVX22bx5s5g7d64QQogDBw6IyZMnCyGESElJEUOGDBFlZWUiPT1d+Pj4iMrKSoPMmpGRIQIDAxssW22yZmRkiMuXL4sZM2aIQ4cOaR/Py8sT3t7eIi8vT+Tn5wtvb2+Rn59vsHmFEKJHjx4Nmu9Js546dUqUlJQIIYTYsmWL9u9BYx/bumQVwvCOa2Fhofb2kSNHxNixY4UQhvlaUF3Wxn4tqAnP5BpBYmIiOnTogPbt20OpVCIwMBBHjx6tsk9cXByCg4MBAP7+/jh16hSEEDh69CgCAwOhVCrRvn17dOjQAYmJiQaZtbHpkrVdu3ZwcXGBQlH1r/rJkyfRp08fWFpaomXLlujTp0+D/2Rcl7yNTZesnp6eMDMzAwD06NEDt2/fBtD4x7YuWRubLlmbN2+uvV1aWgojIyMAMMjXguqyGhKWXCPIzs5GmzZttPft7OyQnZ390D729vYAABMTE1hYWCAvL0+nsYaSFXhwmWLYsGF48803cf78+QbLqWvWhhhbW3Vds6ysDK+++iqGDx+OI0eONERErSfNunPnTvTr169WY/WZFTDM47plyxb4+vpi2bJl+PDDD59orCFkBRr3taAmJnpbmaRja2uLY8eOwcrKCklJSXj33XcRGxtb5ac9qr1jx47Bzs4OGRkZGD16NJycnKHqvxAAAAJ6SURBVPDss8/qOxb27duHpKQkbN68Wd9RHutRWQ3xuL7xxht44403sH//fkRHR2PJkiV6zVOTR2U1pNcCnsk1Ajs7uyqXR7Kzs2FnZ/fQPrdu3QIAVFZWorCwEFZWVjqNNZSsSqUSVlZWAIAuXbrg2WefxfXr1/WatSHG1lZd1/xj3/bt26NXr15ITk6u94x/XkuXrAkJCVizZg2io6OhVCqfaKwhZP1jPGBYx/UPgYGB2rNLQz2uj8ra2K8FNWHJNYKuXbsiLS0NGRkZKC8vR2xsLLy9vavs4+3trf0U2vfffw9PT08YGRnB29sbsbGxKC8vR0ZGBtLS0tCtWzeDzHr37l2o1WoA0GZt3769XrNWx8vLCydPnsS9e/dw7949nDx5El5eXg2Wta557927h/LycgDA3bt38e9//xuOjo56zZqcnIyPPvoI0dHRaNWqlfbxxj62dclqiMc1LS1Nezs+Ph4dOnQAAIN8Lagua2O/FtRI3598+auIj48Xfn5+wsfHR6xevVoIIcTnn38ujhw5IoQQ4v79++K9994Tvr6+IiQkRKSnp2vHrl69Wvj4+Ag/Pz8RHx9vsFkPHz4sAgICRFBQkBg2bJg4evSo3rP+9ttvom/fvqJ79+6iV69eIiAgQDt2x44dwtfXV/j6+oqdO3c2eNa65L1w4YIYPHiwGDJkiBg8eLDYvn273rOOHj1a9O7dWwQFBYmgoCDxzjvvaMc29rGtbVZDPK6ffvqp9v+jN998U1y9elU71tBeC6rLqo/Xgurw++SIiEhavFxJRETSYskREZG0WHJERCQtlhwREUmLJUdERNJiyRERkbRYckREJK3/AyX1Y03nTfGjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_loss = 1\n",
    "selector_best = None\n",
    "\n",
    "#Loop over all possible number of features to get the right amount\n",
    "for i in range(1, 15):\n",
    "    rf = RandomForestClassifier(max_depth=7, max_features=0.5,\n",
    "                                random_state=7, oob_score=False)\n",
    "    \n",
    "    rf_selector = SelectFromModel(rf, max_features=i)\n",
    "    rf_selector.fit(X_train, y_train)\n",
    "    \n",
    "    rf_support = rf_selector.get_support()\n",
    "    rf_features = X_train.loc[:, rf_support].columns\n",
    "\n",
    "    rf.fit(X_train[rf_features], y_train)\n",
    "    \n",
    "    loss = brier_score_loss(y_val, rf.predict_proba(X_val[rf_features])[:, 1])\n",
    "    \n",
    "    if loss < min_loss:\n",
    "        min_loss = loss\n",
    "        best_features = rf_features\n",
    "\n",
    "\n",
    "rf_features = best_features\n",
    "rf.fit(X_train[rf_features], y_train)\n",
    "\n",
    "print(str(len(rf_features)), 'best features according to SelectFromModel:')\n",
    "print(rf_features.tolist())\n",
    "show_metrics(rf, rf_features)\n",
    "\n",
    "index = np.argsort(rf.feature_importances_)\n",
    "features = X_train.columns[index]\n",
    "plt.barh(width=rf.feature_importances_[index], y=best_features);\n",
    "plt.title(\"Feature Importances\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8_6NlXPrJoST"
   },
   "source": [
    "Great! We can see that our validation accuracy fell slightly, but our training accuracy went up **nearly 5%**, which is a significant improvement from the Logistic Regression model that we tried previously. Although this model seems to perform slightly better than our logistic regression model, we thought that the logistic regression model chose better features (as visible above, Random Forest Classifier chose minutes_per_game as an important feature. This might be true, but it is almost a given that a player who has won an MVP award is extremely likely to make the Hall of Fame. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mVeueSWwK5J8"
   },
   "source": [
    "This leads us to our second and final hypothesis. We suppose that the Logistic Regression model will perform better than the Random Forest Classifier at prediciting potential Hall of Famers. Let us test this hypothesis by running both models on our test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2mzfw2X5doN2"
   },
   "outputs": [],
   "source": [
    "X_test = test[features]\n",
    "y_test = test[\"hof\"].astype(int)\n",
    "\n",
    "def test_model(model, features):\n",
    "    proba = model.predict_proba(test[features])[:, 1]\n",
    "    pred = (proba > 0.65).astype(int)\n",
    "    \n",
    "    acc = accuracy_score(y_test, pred)\n",
    "    loss = brier_score_loss(y_test, proba)\n",
    "    \n",
    "    return proba, pred\n",
    "  \n",
    "df = train.append(valid, ignore_index = True)\n",
    "\n",
    "X, y = df.drop(\"hof\", axis=1), df[\"hof\"]\n",
    "\n",
    "logreg_pipe.fit(X[rfe_features], y);\n",
    "rf.fit(X[rf_features], y);\n",
    "\n",
    "logreg_test = test_model(logreg_pipe, rfe_features)\n",
    "rf_test = test_model(rf, best_features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8JtmtadBdoN2",
    "outputId": "8e0d5042-ead5-4acf-c729-b352969c85f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Model: \n",
      "\n",
      "Accuracy: \n",
      "0.9496296296296296\n",
      " Loss: \n",
      "0.0531083090388698\n",
      "\n",
      "Random Forest Classifier: \n",
      "\n",
      "Accuracy: \n",
      "0.9437037037037037\n",
      " Loss: \n",
      "0.06075255446227478\n"
     ]
    }
   ],
   "source": [
    "print(\"Logistic Regression Model: \\n\", sep=\"\")\n",
    "test_probability_lr = logreg_test[0]\n",
    "test_prediction_lr = test_probability_lr > 0.65\n",
    "\n",
    "acc_logreg = accuracy_score(y_test, test_prediction_lr)\n",
    "loss_logreg = brier_score_loss(y_test, test_probability_lr)\n",
    "\n",
    "print('Accuracy: \\n'+str(acc_logreg)+ '\\n Loss: \\n'+str(loss_logreg))\n",
    "\n",
    "print(\"\\nRandom Forest Classifier: \\n\", sep=\"\")\n",
    "test_probability_rf = rf_test[0]\n",
    "test_prediction_rf = test_probability_rf > 0.65\n",
    "\n",
    "acc_rf = accuracy_score(y_test, test_prediction_rf)\n",
    "loss_rf = brier_score_loss(y_test, test_probability_rf)\n",
    "\n",
    "print('Accuracy: \\n'+str(acc_rf)+ '\\n Loss: \\n'+str(loss_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AHl4rYM_mHDb"
   },
   "outputs": [],
   "source": [
    "y_pred_proba = []\n",
    "for i in enumerate(logreg_test[0]):\n",
    "    y_pred_proba.append(i[1])\n",
    "y_pred_proba = np.asarray(y_pred_proba)\n",
    "\n",
    "results_hof = pd.DataFrame({\n",
    "    \"player_name\": test[\"player_name\"],\n",
    "    \"probability_hof\": y_pred_proba\n",
    "    })\n",
    "\n",
    "top_hof_chances = test[['player_name']]\n",
    "top_hof_results = results_hof.merge(top_hof_chances, on='player_name').sort_values(by='probability_hof', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 669
    },
    "id": "Gg52VVOYnpqZ",
    "outputId": "e9172a0f-7a77-4eac-c14f-6d8955f6a29f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>player_name</th>\n",
       "      <th>probability_hof</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>0.999655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>0.999409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>0.999399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>Dirk Nowitzki</td>\n",
       "      <td>0.999346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>0.999163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>0.996218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>Dwyane Wade</td>\n",
       "      <td>0.995806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>0.991506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Luka Dončić</td>\n",
       "      <td>0.990406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Joel Embiid</td>\n",
       "      <td>0.987553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Carmelo Anthony</td>\n",
       "      <td>0.980620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Anthony Davis</td>\n",
       "      <td>0.973034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>Paul Pierce</td>\n",
       "      <td>0.972006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>0.962833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Kyrie Irving</td>\n",
       "      <td>0.959273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Dwight Howard</td>\n",
       "      <td>0.958648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Blake Griffin</td>\n",
       "      <td>0.955362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>Trae Young</td>\n",
       "      <td>0.953845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Karl-Anthony Towns</td>\n",
       "      <td>0.952909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               player_name  probability_hof\n",
       "307           LeBron James         0.999999\n",
       "183           Kevin Durant         0.999655\n",
       "152          Stephen Curry         0.999409\n",
       "246           James Harden         0.999399\n",
       "459          Dirk Nowitzki         0.999346\n",
       "640      Russell Westbrook         0.999163\n",
       "21   Giannis Antetokounmpo         0.996218\n",
       "624            Dwyane Wade         0.995806\n",
       "488             Chris Paul         0.991506\n",
       "170            Luka Dončić         0.990406\n",
       "186            Joel Embiid         0.987553\n",
       "22         Carmelo Anthony         0.980620\n",
       "154          Anthony Davis         0.973034\n",
       "495            Paul Pierce         0.972006\n",
       "372         Damian Lillard         0.962833\n",
       "298           Kyrie Irving         0.959273\n",
       "285          Dwight Howard         0.958648\n",
       "240          Blake Griffin         0.955362\n",
       "673             Trae Young         0.953845\n",
       "605     Karl-Anthony Towns         0.952909"
      ]
     },
     "execution_count": 242,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_hof_results.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8gL6eWBQQuxR"
   },
   "source": [
    "### Results\n",
    "As we hypothesized, the logistic regression model performed better than the random forest classifier model on the test data set, as seen in the accuracy of the validation results, where the logistic regression model was 94.4% accurate and the random forest model was 91.4% accurate. Additionally, if we look at the proportion of non-Hall-of-Famers to total players in the years we include for our data, we get about 3350/3500, or 95.7%, and we see that the logistic regression model predicts this value to be 94.9% for the test data, compared to 94.4% for the random forest model. It is not necessarily the case that this proportion in the test data will match that of previous years, but the fact that logistic regression returns a figure closer to this proportion may be another indicator of superior performance. Also, looking at the list of players that our model has given the 20 highest chances of Hall-of-Fame induction, almost all players there are surely a lock to make it, and the very top of the list is nearly the same as the consensus top players to make the Hall-of-Fame, namely LeBron James, Kevin Durant, Stephen Curry, James Harden, Dirk Nowitzki, Dwyane Wade, and Chris Paul. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zFJvNcooQu6H"
   },
   "source": [
    "## Conclusion\n",
    "We were able to use prediction models to determine which statistics are important in determining an NBA player's chances of Hall of Fame induction. Our logistic regression model proved superior, and based on the validation data, it was able to accurately predict the Hall-of-Fame status for 94.4% of the players. Also, both our models agreed that points per game, player efficiency rating, and total all-star selections were extremely important indicators of Hall-of-Fame chances. \n",
    "\n",
    "The model worked well and looks like it could be a good predictor of Hall-of-Fame chances. Though it was not perfect, a model that predicts with 100% accuracy using solely analytics is not possible, as oftentimes narratives and off-the-court behavior can override on-court performance. As of now there is no concrete, objective criteria given to voters for what determines a Hall-of-Famer, but perhaps the statistics the model marked above are good stats to keep in mind for voters when they choose their Hall-of-Fame ballots.\n",
    "\n",
    "Here is an additional interesting article that debate the potential Hall-of-Fame candidacy of current NBA players:\n",
    "https://bleacherreport.com/articles/2898627-predicting-how-many-future-hall-of-famers-are-on-every-nba-roster\n",
    "\n",
    "Lastly, here is a link to the top 100 players basketball-reference.com has determined have the highest probabilities of making the Hall-of-Fame:\n",
    "https://www.basketball-reference.com/leaders/hof_prob.html\n"
   ]
  }
 ],
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