Scripts/2021_22_season/real_fake_assists.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import requests\n",
    "import tqdm\n",
    "from time import sleep\n",
    "import time\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "from adjustText import adjust_text\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nba_api.stats.static.players import find_players_by_full_name\n",
    "from nba_api.stats.static.teams import find_teams_by_full_name, get_teams, find_team_name_by_id\n",
    "from nba_api.stats.endpoints.playbyplayv2 import PlayByPlayV2\n",
    "from nba_api.stats.endpoints.leaguegamefinder import LeagueGameFinder\n",
    "from nba_api.stats.endpoints.shotchartdetail import ShotChartDetail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nba_api.stats.endpoints.leaguedashplayerstats import LeagueDashPlayerStats\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Fake\" Assisted Shots\n",
      "[\n",
      "    \"Driving Bank Hook Shot\",\n",
      "    \"Driving Bank shot\",\n",
      "    \"Driving Dunk Shot\",\n",
      "    \"Driving Finger Roll Layup Shot\",\n",
      "    \"Driving Finger Roll Shot\",\n",
      "    \"Driving Floating Bank Jump Shot\",\n",
      "    \"Driving Floating Jump Shot\",\n",
      "    \"Driving Hook Shot\",\n",
      "    \"Driving Jump shot\",\n",
      "    \"Driving Layup Shot\",\n",
      "    \"Driving Reverse Dunk Shot\",\n",
      "    \"Driving Reverse Layup Shot\",\n",
      "    \"Driving Slam Dunk Shot\",\n",
      "    \"Fadeaway Bank shot\",\n",
      "    \"Fadeaway Jump Shot\",\n",
      "    \"Floating Jump shot\",\n",
      "    \"Pullup Bank shot\",\n",
      "    \"Pullup Jump shot\",\n",
      "    \"Putback Dunk Shot\",\n",
      "    \"Putback Layup Shot\",\n",
      "    \"Putback Slam Dunk Shot\",\n",
      "    \"Step Back Bank Jump Shot\",\n",
      "    \"Step Back Jump shot\",\n",
      "    \"Turnaround Bank Hook Shot\",\n",
      "    \"Turnaround Bank shot\",\n",
      "    \"Turnaround Fadeaway Bank Jump Shot\",\n",
      "    \"Turnaround Fadeaway shot\",\n",
      "    \"Turnaround Finger Roll Shot\",\n",
      "    \"Turnaround Hook Shot\",\n",
      "    \"Turnaround Jump Shot\"\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "fake_shots = sorted(list(set([\n",
    "    'Driving Dunk Shot',\n",
    "    'Driving Finger Roll Layup Shot',\n",
    "    'Driving Floating Bank Jump Shot',\n",
    "    'Driving Floating Jump Shot',\n",
    "    'Driving Hook Shot',\n",
    "    'Driving Layup Shot',\n",
    "    'Fadeaway Jump Shot',\n",
    "    'Floating Jump shot',\n",
    "    'Pullup Jump shot',\n",
    "    'Step Back Jump shot',\n",
    "    'Turnaround Fadeaway Bank Jump Shot',\n",
    "    'Turnaround Hook Shot',\n",
    "    'Turnaround Jump Shot',    \n",
    "    \"Turnaround Fadeaway shot\",\n",
    "    \"Driving Reverse Layup Shot\",\n",
    "    \"Driving Bank Hook Shot\",\n",
    "    \"Step Back Bank Jump Shot\",\n",
    "    \"Driving Reverse Dunk Shot\",\n",
    "    \"Turnaround Bank Hook Shot\",\n",
    "    \"Driving Finger Roll Shot\",\n",
    "    \"Turnaround Finger Roll Shot\",\n",
    "    'Turnaround Bank shot',\n",
    "    'Putback Dunk Shot',\n",
    "    'Driving Jump shot',\n",
    "    'Putback Layup Shot',\n",
    "    'Driving Slam Dunk Shot',\n",
    "    'Putback Slam Dunk Shot',\n",
    "    'Fadeaway Bank shot',\n",
    "    'Driving Bank shot',\n",
    "    'Pullup Bank shot',\n",
    "])))\n",
    "\n",
    "print('\"Fake\" Assisted Shots')\n",
    "print(json.dumps(fake_shots, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Real\" Assisted Shots\n",
      "[\n",
      "    \"Alley Oop Dunk Shot\",\n",
      "    \"Alley Oop Layup shot\",\n",
      "    \"Cutting Dunk Shot\",\n",
      "    \"Cutting Finger Roll Layup Shot\",\n",
      "    \"Cutting Layup Shot\",\n",
      "    \"Dunk Shot\",\n",
      "    \"Finger Roll Layup Shot\",\n",
      "    \"Finger Roll Shot\",\n",
      "    \"Follow Up Dunk Shot\",\n",
      "    \"Hook Bank Shot\",\n",
      "    \"Hook Shot\",\n",
      "    \"Jump Bank Hook Shot\",\n",
      "    \"Jump Bank Shot\",\n",
      "    \"Jump Hook Shot\",\n",
      "    \"Jump Shot\",\n",
      "    \"Layup Shot\",\n",
      "    \"Reverse Dunk Shot\",\n",
      "    \"Reverse Layup Shot\",\n",
      "    \"Reverse Slam Dunk Shot\",\n",
      "    \"Running Alley Oop Dunk Shot\",\n",
      "    \"Running Alley Oop Layup Shot\",\n",
      "    \"Running Bank Hook Shot\",\n",
      "    \"Running Bank shot\",\n",
      "    \"Running Dunk Shot\",\n",
      "    \"Running Finger Roll Layup Shot\",\n",
      "    \"Running Finger Roll Shot\",\n",
      "    \"Running Hook Shot\",\n",
      "    \"Running Jump Shot\",\n",
      "    \"Running Layup Shot\",\n",
      "    \"Running Pull-Up Jump Shot\",\n",
      "    \"Running Reverse Dunk Shot\",\n",
      "    \"Running Reverse Layup Shot\",\n",
      "    \"Running Slam Dunk Shot\",\n",
      "    \"Running Tip Shot\",\n",
      "    \"Slam Dunk Shot\"\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "real_shots = sorted(list(set(['Alley Oop Dunk Shot',\n",
    "'Alley Oop Layup shot',\n",
    "'Cutting Dunk Shot',\n",
    "'Cutting Finger Roll Layup Shot',\n",
    "'Cutting Layup Shot',\n",
    "'Dunk Shot',\n",
    "'Jump Bank Shot',\n",
    "'Jump Shot',\n",
    "'Layup Shot',\n",
    "'Hook Shot',\n",
    "'Reverse Dunk Shot',\n",
    "'Reverse Layup Shot',\n",
    "'Running Alley Oop Dunk Shot',\n",
    "'Running Alley Oop Layup Shot',\n",
    "'Running Dunk Shot',\n",
    "'Running Finger Roll Layup Shot',\n",
    "'Running Jump Shot',\n",
    "'Running Layup Shot',\n",
    "'Running Pull-Up Jump Shot',\n",
    "'Running Reverse Layup Shot',\n",
    "\"Hook Bank Shot\",\n",
    "\"Finger Roll Layup Shot\",\n",
    "\"Running Reverse Dunk Shot\",\n",
    "\"Slam Dunk Shot\",\n",
    "\"Jump Hook Shot\",\n",
    "\"Running Hook Shot\",\n",
    "\"Follow Up Dunk Shot\",\n",
    "\"Finger Roll Shot\",\n",
    "\"Running Finger Roll Shot\",\n",
    "\"Running Tip Shot\",\n",
    "\"Jump Shot\",\n",
    "\"Layup Shot\",\n",
    "\"Dunk Shot\",\n",
    "\"Hook Shot\",\n",
    "'Running Slam Dunk Shot',\n",
    "'Running Bank Hook Shot',\n",
    "'Running Bank shot',\n",
    "'Reverse Slam Dunk Shot',\n",
    "'Jump Bank Hook Shot',])))\n",
    "\n",
    "print(f'\"Real\" Assisted Shots')\n",
    "print(json.dumps(real_shots, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_shots = [\n",
    "    'Driving Dunk Shot',\n",
    "    'Driving Finger Roll Layup Shot',\n",
    "    'Driving Floating Bank Jump Shot',\n",
    "    'Driving Floating Jump Shot',\n",
    "    'Driving Hook Shot',\n",
    "    'Driving Layup Shot',\n",
    "    'Fadeaway Jump Shot',\n",
    "    'Floating Jump shot',\n",
    "    'Pullup Jump shot',\n",
    "    'Step Back Jump shot',\n",
    "    'Turnaround Fadeaway Bank Jump Shot',\n",
    "    'Turnaround Hook Shot',\n",
    "    'Turnaround Jump Shot',    \n",
    "    \"Turnaround Fadeaway shot\",\n",
    "    \"Driving Reverse Layup Shot\",\n",
    "    \"Driving Bank Hook Shot\",\n",
    "    \"Step Back Bank Jump Shot\",\n",
    "    \"Driving Reverse Dunk Shot\",\n",
    "    \"Turnaround Bank Hook Shot\",\n",
    "    \"Driving Finger Roll Shot\",\n",
    "    \"Turnaround Finger Roll Shot\",\n",
    "    'Turnaround Bank shot',\n",
    "    'Putback Dunk Shot',\n",
    "    'Driving Jump shot',\n",
    "    'Putback Layup Shot',\n",
    "    'Driving Slam Dunk Shot',\n",
    "    'Putback Slam Dunk Shot',\n",
    "    'Fadeaway Bank shot',\n",
    "    'Driving Bank shot',\n",
    "    'Pullup Bank shot',\n",
    "    'Putback Reverse Dunk Shot',\n",
    "    \"Tip Shot\",\n",
    "]\n",
    "\n",
    "\n",
    "real_shots = [\n",
    "    'Alley Oop Dunk Shot',\n",
    "    'Alley Oop Layup shot',\n",
    "    'Cutting Dunk Shot',\n",
    "    'Cutting Finger Roll Layup Shot',\n",
    "    'Cutting Layup Shot',\n",
    "    'Dunk Shot',\n",
    "    'Jump Bank Shot',\n",
    "    'Jump Shot',\n",
    "    'Layup Shot',\n",
    "    'Hook Shot',\n",
    "    'Reverse Dunk Shot',\n",
    "    'Reverse Layup Shot',\n",
    "    'Running Alley Oop Dunk Shot',\n",
    "    'Running Alley Oop Layup Shot',\n",
    "    'Running Dunk Shot',\n",
    "    'Running Finger Roll Layup Shot',\n",
    "    'Running Jump Shot',\n",
    "    'Running Layup Shot',\n",
    "    'Running Pull-Up Jump Shot',\n",
    "    'Running Reverse Layup Shot',\n",
    "    \"Hook Bank Shot\",\n",
    "    \"Finger Roll Layup Shot\",\n",
    "    \"Running Reverse Dunk Shot\",\n",
    "    \"Slam Dunk Shot\",\n",
    "    \"Jump Hook Shot\",\n",
    "    \"Running Hook Shot\",\n",
    "    \"Follow Up Dunk Shot\",\n",
    "    \"Finger Roll Shot\",\n",
    "    \"Running Finger Roll Shot\",\n",
    "    \"Running Tip Shot\",\n",
    "    \"Jump Shot\",\n",
    "    \"Layup Shot\",\n",
    "    \"Dunk Shot\",\n",
    "    \"Hook Shot\",\n",
    "    'Running Slam Dunk Shot',\n",
    "    'Running Bank Hook Shot',\n",
    "    'Running Bank shot',\n",
    "    'Reverse Slam Dunk Shot',\n",
    "    'Jump Bank Hook Shot',\n",
    "\n",
    "]\n",
    "\n",
    "type_map = {'Jump Shot': 1,\n",
    "    'Pullup Jump shot': 79,\n",
    "    'Alley Oop Dunk Shot': 52,\n",
    "    'Hook Shot': 3,\n",
    "    'Step Back Jump shot': 80,\n",
    "    'Turnaround Jump Shot': 47,\n",
    "    'Layup Shot': 5,\n",
    "    'Running Pull-Up Jump Shot': 103,\n",
    "    'Driving Layup Shot': 6,\n",
    "    'Running Dunk Shot': 50,\n",
    "    'Cutting Dunk Shot': 108,\n",
    "    'Alley Oop Layup shot': 43,\n",
    "    'Driving Floating Jump Shot': 101,\n",
    "    'Running Jump Shot': 2,\n",
    "    'Turnaround Fadeaway shot': 86,\n",
    "    'Fadeaway Jump Shot': 63,\n",
    "    'Floating Jump shot': 78,\n",
    "    'Cutting Layup Shot': 98,\n",
    "    'Running Layup Shot': 41,\n",
    "    'Dunk Shot': 7,\n",
    "    'Driving Dunk Shot': 9,\n",
    "    'Cutting Finger Roll Layup Shot': 99,\n",
    "    'Running Finger Roll Layup Shot': 76,\n",
    "    'Turnaround Hook Shot': 58,\n",
    "    'Running Alley Oop Dunk Shot': 106,\n",
    "    'Driving Floating Bank Jump Shot': 102,\n",
    "    'Running Alley Oop Layup Shot': 100,\n",
    "    'Driving Reverse Layup Shot': 73,\n",
    "    'Turnaround Fadeaway Bank Jump Shot': 105,\n",
    "    'Driving Finger Roll Layup Shot': 75,\n",
    "    'Reverse Layup Shot': 44,\n",
    "    'Running Reverse Layup Shot': 74,\n",
    "    'Jump Bank Shot': 66,\n",
    "    'Driving Hook Shot': 57,\n",
    "    'Hook Bank Shot': 67,\n",
    "    'Finger Roll Layup Shot': 71,\n",
    "    'Driving Bank Hook Shot': 93,\n",
    "    'Reverse Dunk Shot': 51,\n",
    "    'Running Reverse Dunk Shot': 110,\n",
    "    'Step Back Bank Jump Shot': 104,\n",
    "    'Driving Reverse Dunk Shot': 109,\n",
    "    'Turnaround Bank Hook Shot': 96\n",
    "}\n",
    "\n",
    "reverse_type_map = {1: 'Jump Shot',\n",
    " 79: 'Pullup Jump shot',\n",
    " 52: 'Alley Oop Dunk Shot',\n",
    " 3: 'Hook Shot',\n",
    " 80: 'Step Back Jump shot',\n",
    " 47: 'Turnaround Jump Shot',\n",
    " 5: 'Layup Shot',\n",
    " 103: 'Running Pull-Up Jump Shot',\n",
    " 6: 'Driving Layup Shot',\n",
    " 50: 'Running Dunk Shot',\n",
    " 108: 'Cutting Dunk Shot',\n",
    " 43: 'Alley Oop Layup shot',\n",
    " 101: 'Driving Floating Jump Shot',\n",
    " 2: 'Running Jump Shot',\n",
    " 86: 'Turnaround Fadeaway shot',\n",
    " 63: 'Fadeaway Jump Shot',\n",
    " 78: 'Floating Jump shot',\n",
    " 98: 'Cutting Layup Shot',\n",
    " 41: 'Running Layup Shot',\n",
    " 7: 'Dunk Shot',\n",
    " 9: 'Driving Dunk Shot',\n",
    " 99: 'Cutting Finger Roll Layup Shot',\n",
    " 76: 'Running Finger Roll Layup Shot',\n",
    " 58: 'Turnaround Hook Shot',\n",
    " 106: 'Running Alley Oop Dunk Shot',\n",
    " 102: 'Driving Floating Bank Jump Shot',\n",
    " 100: 'Running Alley Oop Layup Shot',\n",
    " 73: 'Driving Reverse Layup Shot',\n",
    " 105: 'Turnaround Fadeaway Bank Jump Shot',\n",
    " 75: 'Driving Finger Roll Layup Shot',\n",
    " 44: 'Reverse Layup Shot',\n",
    " 74: 'Running Reverse Layup Shot',\n",
    " 66: 'Jump Bank Shot',\n",
    " 57: 'Driving Hook Shot',\n",
    " 67: 'Hook Bank Shot',\n",
    " 71: 'Finger Roll Layup Shot',\n",
    " 93: 'Driving Bank Hook Shot',\n",
    " 51: 'Reverse Dunk Shot',\n",
    " 110: 'Running Reverse Dunk Shot',\n",
    " 104: 'Step Back Bank Jump Shot',\n",
    " 109: 'Driving Reverse Dunk Shot',\n",
    " 96: 'Turnaround Bank Hook Shot',\n",
    " 8: 'Slam Dunk Shot',\n",
    " 42: 'Driving Layup Shot',\n",
    " 4: 'Tip Shot',\n",
    " 65: 'Jump Hook Shot',\n",
    " 46: 'Running Jump Shot',\n",
    " 49: 'Driving Dunk Shot',\n",
    " 56: 'Running Hook Shot',\n",
    " 64: 'Follow Up Dunk Shot',\n",
    " 59: 'Finger Roll Shot',\n",
    " 61: 'Driving Finger Roll Shot',\n",
    " 60: 'Running Finger Roll Shot',\n",
    " 62: 'Turnaround Finger Roll Shot',\n",
    " 54: 'Running Tip Shot',\n",
    " 53: 'Tip Shot',\n",
    " 45: 'Jump Shot',\n",
    " 40: 'Layup Shot',\n",
    " 48: 'Dunk Shot',\n",
    " 55: 'Hook Shot',\n",
    " 85: 'Turnaround Bank shot',\n",
    " 87: 'Putback Dunk Shot',\n",
    " 77: 'Driving Jump shot',\n",
    " 72: 'Putback Layup Shot',\n",
    " 90: 'Running Slam Dunk Shot',\n",
    " 88: 'Driving Slam Dunk Shot',\n",
    " 92: 'Putback Slam Dunk Shot',\n",
    " 83: 'Fadeaway Bank shot',\n",
    " 95: 'Running Bank Hook Shot',\n",
    " 84: 'Running Bank shot',\n",
    " 89: 'Reverse Slam Dunk Shot',\n",
    " 82: 'Driving Bank shot',\n",
    " 81: 'Pullup Bank shot',\n",
    " 91: 'Putback Reverse Dunk Shot',\n",
    " 94: 'Jump Bank Hook Shot',\n",
    "                   }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df = pd.read_csv('pbp_data/2021-22.csv')\n",
    "\n",
    "full_df = full_df.fillna({\n",
    "    \"HOMEDESCRIPTION\": \"\",\n",
    "    \"NEUTRALDESCRIPTION\": \"\",\n",
    "    \"VISITORDESCRIPTION\": \"\"\n",
    "})\n",
    "full_df.loc[:, 'GAME_ID'] = [f\"00{item}\" for item in full_df.GAME_ID.astype(str)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "season = '2021-22'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots = ShotChartDetail(team_id=0, player_id=0, context_measure_simple='FGM', season_nullable=season)\n",
    "\n",
    "shots_df = shots.get_data_frames()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "league_stats = LeagueDashPlayerStats(per_mode_detailed='PerGame', season=season, season_type_all_star='Regular Season')\n",
    "\n",
    "league_df = league_stats.get_data_frames()[0]\n",
    "\n",
    "top_passers = league_df.loc[league_df.GP > 5].sort_values(by='AST_RANK').iloc[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "types = shots_df.ACTION_TYPE.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_assist_info = {}\n",
    "for i in range(len(top_passers)):\n",
    "    cur_row = top_passers.iloc[i]\n",
    "    player_assists = full_df.loc[(full_df.PLAYER2_ID == cur_row.PLAYER_ID) & (full_df.EVENTMSGTYPE == 1)]\n",
    "    merged_df = pd.merge(left=shots_df, right=player_assists, left_on=(\"GAME_ID\", \"GAME_EVENT_ID\"), right_on=(\"GAME_ID\", \"EVENTNUM\"))\n",
    "        \n",
    "    assist_info = Counter()\n",
    "    value_counts = merged_df.ACTION_TYPE.value_counts().to_dict()\n",
    "    for key in value_counts:\n",
    "        if key in fake_shots:\n",
    "            assist_info['created'] += value_counts[key]\n",
    "        else:\n",
    "            assist_info['non_created'] += value_counts[key]\n",
    "    player_assist_info[cur_row.PLAYER_ID] = {\n",
    "        'PLAYER_NAME': cur_row.PLAYER_NAME,\n",
    "        'FAKE': assist_info['created'],\n",
    "        'REAL': assist_info['non_created'],\n",
    "        'AST_G': cur_row.AST \n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "created_list = []\n",
    "real_assists = []\n",
    "player_names = []\n",
    "assists_list = []\n",
    "for player_id in player_assist_info:\n",
    "    total_assists = player_assist_info[player_id]['FAKE'] + player_assist_info[player_id]['REAL']\n",
    "    player_names.append(player_assist_info[player_id]['PLAYER_NAME'])\n",
    "    created_list.append(round(player_assist_info[player_id]['FAKE'] / total_assists * 100))\n",
    "    real_assists.append(round(player_assist_info[player_id]['REAL'] / total_assists * 100))\n",
    "    assists_list.append(total_assists)\n",
    "\n",
    "assist_counts = pd.DataFrame(player_assist_info).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "assist_df = pd.DataFrame(data={\n",
    "    'PLAYER_NAME': player_names,\n",
    "    'FAKE': created_list,\n",
    "    'REAL': real_assists,\n",
    "    'AST_G': assists_list\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "sorted_assist = assist_df.sort_values(by='REAL', ascending=True)\n",
    "\n",
    "ax_labels = [f\"{sorted_assist.iloc[i].PLAYER_NAME} ({sorted_assist.iloc[i].AST_G})\" for i in range(len(sorted_assist))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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      "c:\\users\\danie\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  if __name__ == '__main__':\n"
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     "execution_count": 26,
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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 14))\n",
    "\n",
    "\n",
    "ax.barh(y=ax_labels, width=sorted_assist.FAKE.tolist(), label='\"Fake\" Assists')\n",
    "ax.barh(y=ax_labels, width=sorted_assist.REAL.tolist(), left=sorted_assist.FAKE.tolist(), label='Real Assists')\n",
    "ax.set_title('Ratio of Real Assists and \"Fake Assists\"')\n",
    "ax.set_xlabel(\"Percentage of Total Assists\")\n",
    "#ax.set_xticks([tick for tick in range(0, 110, 20)])\n",
    "ax.set_xticklabels(f\"{item}%\" for item in range(0, 105, 20))\n",
    "ax.text(x=106, y=26, s='Number in brackets shows\\ntotal assists in 2021/22')\n",
    "ax.legend(loc='upper left', bbox_to_anchor=(1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Year by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_assists = full_df.loc[(full_df.EVENTMSGTYPE == 1) & (full_df.PLAYER2_ID != 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "assist_total = all_assists.groupby('EVENTMSGACTIONTYPE').EVENTNUM.count().to_frame().reset_index().rename(columns={'EVENTNUM': 'COUNT'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "type_list = [reverse_type_map[key] for key in assist_total.EVENTMSGACTIONTYPE.tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "assist_total.loc[:, 'TYPE'] = type_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'real' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-a2dfc78ddc1b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0massist_total\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0massist_total\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTYPE\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreal\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOUNT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'real' is not defined"
     ]
    }
   ],
   "source": [
    "assist_total.loc[assist_total.TYPE.isin(real)].COUNT.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fake' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-2e4174aaa5c2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0massist_total\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0massist_total\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTYPE\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfake\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOUNT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'fake' is not defined"
     ]
    }
   ],
   "source": [
    "assist_total.loc[assist_total.TYPE.isin(fake)].COUNT.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "seasons = [f\"{season}-{str(season+1)[-2:]}\" for season in range(2001, 2022)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2021-22'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots_df = ShotChartDetail(player_id=0, team_id=0, season_nullable=season).get_data_frames()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_cur = pd.merge(left=shots_df, right=all_assists, left_on=(\"GAME_ID\", \"GAME_EVENT_ID\"), right_on=(\"GAME_ID\", \"EVENTNUM\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "types = merged_cur.EVENTMSGACTIONTYPE.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52: \"Alley Oop Dunk Shot\",\n",
      "1: \"Jump Shot\",\n",
      "47: \"Turnaround Jump Shot\",\n",
      "50: \"Running Dunk Shot\",\n",
      "43: \"Alley Oop Layup shot\",\n",
      "79: \"Pullup Jump shot\",\n",
      "108: \"Cutting Dunk Shot\",\n",
      "103: \"Running Pull-Up Jump Shot\",\n",
      "2: \"Running Jump Shot\",\n",
      "6: \"Driving Layup Shot\",\n",
      "98: \"Cutting Layup Shot\",\n",
      "105: \"Turnaround Fadeaway Bank Jump Shot\",\n",
      "100: \"Running Alley Oop Layup Shot\",\n",
      "101: \"Driving Floating Jump Shot\",\n",
      "75: \"Driving Finger Roll Layup Shot\",\n",
      "80: \"Step Back Jump shot\",\n",
      "3: \"Hook Shot\",\n",
      "78: \"Floating Jump shot\",\n",
      "41: \"Running Layup Shot\",\n",
      "99: \"Cutting Finger Roll Layup Shot\",\n",
      "44: \"Reverse Layup Shot\",\n",
      "86: \"Turnaround Fadeaway shot\",\n",
      "5: \"Layup Shot\",\n",
      "66: \"Jump Bank Shot\",\n",
      "7: \"Dunk Shot\",\n",
      "58: \"Turnaround Hook Shot\",\n",
      "73: \"Driving Reverse Layup Shot\",\n",
      "9: \"Driving Dunk Shot\",\n",
      "106: \"Running Alley Oop Dunk Shot\",\n",
      "63: \"Fadeaway Jump Shot\",\n",
      "74: \"Running Reverse Layup Shot\",\n",
      "71: \"Finger Roll Layup Shot\",\n",
      "102: \"Driving Floating Bank Jump Shot\",\n",
      "76: \"Running Finger Roll Layup Shot\",\n",
      "51: \"Reverse Dunk Shot\",\n",
      "57: \"Driving Hook Shot\",\n",
      "109: \"Driving Reverse Dunk Shot\",\n",
      "110: \"Running Reverse Dunk Shot\",\n",
      "67: \"Hook Bank Shot\",\n",
      "93: \"Driving Bank Hook Shot\",\n",
      "104: \"Step Back Bank Jump Shot\",\n",
      "96: \"Turnaround Bank Hook Shot\",\n"
     ]
    }
   ],
   "source": [
    "new_dict = {}\n",
    "for t in types:\n",
    "    print(f'{t}: \"{merged_cur.loc[merged_cur.EVENTMSGACTIONTYPE == t].iloc[0].ACTION_TYPE}\",')\n",
    "    new_dict[t] = merged_cur.loc[merged_cur.EVENTMSGACTIONTYPE == t].iloc[0].ACTION_TYPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in new_dict:\n",
    "    if key not in reverse_type_map:\n",
    "        print(key, new_dict[key])\n",
    "        #reverse_type_map[key] = new_dict[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EVENTMSGACTIONTYPE</th>\n",
       "      <th>EVENTMSGTYPE</th>\n",
       "      <th>EVENTNUM</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>HOMEDESCRIPTION</th>\n",
       "      <th>NEUTRALDESCRIPTION</th>\n",
       "      <th>PCTIMESECONDS</th>\n",
       "      <th>PCTIMESTRING</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>PERSON1TYPE</th>\n",
       "      <th>...</th>\n",
       "      <th>PLAYER3_NAME</th>\n",
       "      <th>PLAYER3_TEAM_ABBREVIATION</th>\n",
       "      <th>PLAYER3_TEAM_CITY</th>\n",
       "      <th>PLAYER3_TEAM_ID</th>\n",
       "      <th>PLAYER3_TEAM_NICKNAME</th>\n",
       "      <th>SCORE</th>\n",
       "      <th>SCOREMARGIN</th>\n",
       "      <th>VIDEO_AVAILABLE_FLAG</th>\n",
       "      <th>VISITORDESCRIPTION</th>\n",
       "      <th>WCTIMESTRING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [EVENTMSGACTIONTYPE, EVENTMSGTYPE, EVENTNUM, GAME_ID, HOMEDESCRIPTION, NEUTRALDESCRIPTION, PCTIMESECONDS, PCTIMESTRING, PERIOD, PERSON1TYPE, PERSON2TYPE, PERSON3TYPE, PLAYER1_ID, PLAYER1_NAME, PLAYER1_TEAM_ABBREVIATION, PLAYER1_TEAM_CITY, PLAYER1_TEAM_ID, PLAYER1_TEAM_NICKNAME, PLAYER2_ID, PLAYER2_NAME, PLAYER2_TEAM_ABBREVIATION, PLAYER2_TEAM_CITY, PLAYER2_TEAM_ID, PLAYER2_TEAM_NICKNAME, PLAYER3_ID, PLAYER3_NAME, PLAYER3_TEAM_ABBREVIATION, PLAYER3_TEAM_CITY, PLAYER3_TEAM_ID, PLAYER3_TEAM_NICKNAME, SCORE, SCOREMARGIN, VIDEO_AVAILABLE_FLAG, VISITORDESCRIPTION, WCTIMESTRING]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 35 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_assists.loc[all_assists.EVENTMSGACTIONTYPE == 87 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EVENTMSGACTIONTYPE</th>\n",
       "      <th>EVENTMSGTYPE</th>\n",
       "      <th>EVENTNUM</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>HOMEDESCRIPTION</th>\n",
       "      <th>NEUTRALDESCRIPTION</th>\n",
       "      <th>PCTIMESECONDS</th>\n",
       "      <th>PCTIMESTRING</th>\n",
       "      <th>PERIOD</th>\n",
       "      <th>PERSON1TYPE</th>\n",
       "      <th>...</th>\n",
       "      <th>PLAYER3_NAME</th>\n",
       "      <th>PLAYER3_TEAM_ABBREVIATION</th>\n",
       "      <th>PLAYER3_TEAM_CITY</th>\n",
       "      <th>PLAYER3_TEAM_ID</th>\n",
       "      <th>PLAYER3_TEAM_NICKNAME</th>\n",
       "      <th>SCORE</th>\n",
       "      <th>SCOREMARGIN</th>\n",
       "      <th>VIDEO_AVAILABLE_FLAG</th>\n",
       "      <th>VISITORDESCRIPTION</th>\n",
       "      <th>WCTIMESTRING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [EVENTMSGACTIONTYPE, EVENTMSGTYPE, EVENTNUM, GAME_ID, HOMEDESCRIPTION, NEUTRALDESCRIPTION, PCTIMESECONDS, PCTIMESTRING, PERIOD, PERSON1TYPE, PERSON2TYPE, PERSON3TYPE, PLAYER1_ID, PLAYER1_NAME, PLAYER1_TEAM_ABBREVIATION, PLAYER1_TEAM_CITY, PLAYER1_TEAM_ID, PLAYER1_TEAM_NICKNAME, PLAYER2_ID, PLAYER2_NAME, PLAYER2_TEAM_ABBREVIATION, PLAYER2_TEAM_CITY, PLAYER2_TEAM_ID, PLAYER2_TEAM_NICKNAME, PLAYER3_ID, PLAYER3_NAME, PLAYER3_TEAM_ABBREVIATION, PLAYER3_TEAM_CITY, PLAYER3_TEAM_ID, PLAYER3_TEAM_NICKNAME, SCORE, SCOREMARGIN, VIDEO_AVAILABLE_FLAG, VISITORDESCRIPTION, WCTIMESTRING]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 35 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_assists.loc[all_assists.EVENTMSGACTIONTYPE == 40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2001-02\n",
      "2002-03\n",
      "2003-04\n",
      "2004-05\n",
      "2005-06\n",
      "2006-07\n",
      "2007-08\n",
      "2008-09\n",
      "2009-10\n",
      "2010-11\n",
      "2011-12\n",
      "2012-13\n",
      "2013-14\n",
      "2014-15\n",
      "2015-16\n",
      "2016-17\n",
      "2017-18\n",
      "2018-19\n",
      "2019-20\n",
      "2020-21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-22\n"
     ]
    }
   ],
   "source": [
    "real = []\n",
    "fake = []\n",
    "total = []\n",
    "\n",
    "ignore_keys = [0, 97, 107]\n",
    "\n",
    "for season in seasons:\n",
    "    print(season)\n",
    "    full_df = pd.read_csv(f'pbp_data/{season}.csv')\n",
    "\n",
    "    full_df = full_df.fillna({\n",
    "        \"HOMEDESCRIPTION\": \"\",\n",
    "        \"NEUTRALDESCRIPTION\": \"\",\n",
    "        \"VISITORDESCRIPTION\": \"\"\n",
    "    })\n",
    "    full_df.loc[:, 'GAME_ID'] = [f\"00{item}\" for item in full_df.GAME_ID.astype(str)]\n",
    "    all_assists = full_df.loc[(full_df.EVENTMSGTYPE == 1) & (full_df.PLAYER2_ID != 0)]\n",
    "    \n",
    "    assist_total = all_assists.groupby('EVENTMSGACTIONTYPE').EVENTNUM.count().to_frame().reset_index().rename(columns={'EVENTNUM': 'COUNT'})\n",
    "    assist_total = assist_total.loc[~assist_total.EVENTMSGACTIONTYPE.isin(ignore_keys)]\n",
    "    type_list = [reverse_type_map[key] for key in assist_total.EVENTMSGACTIONTYPE.tolist() if key not in ignore_keys]\n",
    "\n",
    "    assist_total.loc[:, 'TYPE'] = type_list\n",
    "\n",
    "    real.append(assist_total.loc[assist_total.TYPE.isin(real_shots)].COUNT.sum())\n",
    "    fake.append(assist_total.loc[assist_total.TYPE.isin(fake_shots)].COUNT.sum())\n",
    "    total.append(assist_total.loc[assist_total.TYPE.isin(real_shots)].COUNT.sum() + assist_total.loc[assist_total.TYPE.isin(fake_shots)].COUNT.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_fake_breakdown = pd.DataFrame(\n",
    "    data={\n",
    "        'SEASON': seasons,\n",
    "        'REAL': real,\n",
    "        'FAKE': fake,\n",
    "        'TOTAL': total\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "real_fake_breakdown.loc[:, 'REAL_PCT'] = (real_fake_breakdown.REAL / real_fake_breakdown.TOTAL * 100).round(2)\n",
    "real_fake_breakdown.loc[:, 'FAKE_PCT'] = (real_fake_breakdown.FAKE / real_fake_breakdown.TOTAL * 100).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>REAL</th>\n",
       "      <th>FAKE</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>REAL_PCT</th>\n",
       "      <th>FAKE_PCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-02</td>\n",
       "      <td>47774</td>\n",
       "      <td>4357</td>\n",
       "      <td>52131</td>\n",
       "      <td>91.64</td>\n",
       "      <td>8.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002-03</td>\n",
       "      <td>46665</td>\n",
       "      <td>4468</td>\n",
       "      <td>51133</td>\n",
       "      <td>91.26</td>\n",
       "      <td>8.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003-04</td>\n",
       "      <td>46250</td>\n",
       "      <td>4408</td>\n",
       "      <td>50658</td>\n",
       "      <td>91.30</td>\n",
       "      <td>8.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-05</td>\n",
       "      <td>48155</td>\n",
       "      <td>4187</td>\n",
       "      <td>52342</td>\n",
       "      <td>92.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-06</td>\n",
       "      <td>46353</td>\n",
       "      <td>4358</td>\n",
       "      <td>50711</td>\n",
       "      <td>91.41</td>\n",
       "      <td>8.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006-07</td>\n",
       "      <td>48290</td>\n",
       "      <td>4075</td>\n",
       "      <td>52365</td>\n",
       "      <td>92.22</td>\n",
       "      <td>7.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007-08</td>\n",
       "      <td>48277</td>\n",
       "      <td>5229</td>\n",
       "      <td>53506</td>\n",
       "      <td>90.23</td>\n",
       "      <td>9.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008-09</td>\n",
       "      <td>46025</td>\n",
       "      <td>5580</td>\n",
       "      <td>51605</td>\n",
       "      <td>89.19</td>\n",
       "      <td>10.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009-10</td>\n",
       "      <td>45710</td>\n",
       "      <td>6548</td>\n",
       "      <td>52258</td>\n",
       "      <td>87.47</td>\n",
       "      <td>12.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010-11</td>\n",
       "      <td>46477</td>\n",
       "      <td>6410</td>\n",
       "      <td>52887</td>\n",
       "      <td>87.88</td>\n",
       "      <td>12.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2011-12</td>\n",
       "      <td>36806</td>\n",
       "      <td>4727</td>\n",
       "      <td>41533</td>\n",
       "      <td>88.62</td>\n",
       "      <td>11.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>47264</td>\n",
       "      <td>7151</td>\n",
       "      <td>54415</td>\n",
       "      <td>86.86</td>\n",
       "      <td>13.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>46590</td>\n",
       "      <td>7541</td>\n",
       "      <td>54131</td>\n",
       "      <td>86.07</td>\n",
       "      <td>13.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>46225</td>\n",
       "      <td>8057</td>\n",
       "      <td>54282</td>\n",
       "      <td>85.16</td>\n",
       "      <td>14.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>44891</td>\n",
       "      <td>9976</td>\n",
       "      <td>54867</td>\n",
       "      <td>81.82</td>\n",
       "      <td>18.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-17</td>\n",
       "      <td>45470</td>\n",
       "      <td>10223</td>\n",
       "      <td>55693</td>\n",
       "      <td>81.64</td>\n",
       "      <td>18.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>44783</td>\n",
       "      <td>12397</td>\n",
       "      <td>57180</td>\n",
       "      <td>78.32</td>\n",
       "      <td>21.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-19</td>\n",
       "      <td>44802</td>\n",
       "      <td>15747</td>\n",
       "      <td>60549</td>\n",
       "      <td>73.99</td>\n",
       "      <td>26.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-20</td>\n",
       "      <td>38083</td>\n",
       "      <td>13598</td>\n",
       "      <td>51681</td>\n",
       "      <td>73.69</td>\n",
       "      <td>26.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-21</td>\n",
       "      <td>38311</td>\n",
       "      <td>14227</td>\n",
       "      <td>52538</td>\n",
       "      <td>72.92</td>\n",
       "      <td>27.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2021-22</td>\n",
       "      <td>8729</td>\n",
       "      <td>3073</td>\n",
       "      <td>11802</td>\n",
       "      <td>73.96</td>\n",
       "      <td>26.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SEASON   REAL   FAKE  TOTAL  REAL_PCT  FAKE_PCT\n",
       "0   2001-02  47774   4357  52131     91.64      8.36\n",
       "1   2002-03  46665   4468  51133     91.26      8.74\n",
       "2   2003-04  46250   4408  50658     91.30      8.70\n",
       "3   2004-05  48155   4187  52342     92.00      8.00\n",
       "4   2005-06  46353   4358  50711     91.41      8.59\n",
       "5   2006-07  48290   4075  52365     92.22      7.78\n",
       "6   2007-08  48277   5229  53506     90.23      9.77\n",
       "7   2008-09  46025   5580  51605     89.19     10.81\n",
       "8   2009-10  45710   6548  52258     87.47     12.53\n",
       "9   2010-11  46477   6410  52887     87.88     12.12\n",
       "10  2011-12  36806   4727  41533     88.62     11.38\n",
       "11  2012-13  47264   7151  54415     86.86     13.14\n",
       "12  2013-14  46590   7541  54131     86.07     13.93\n",
       "13  2014-15  46225   8057  54282     85.16     14.84\n",
       "14  2015-16  44891   9976  54867     81.82     18.18\n",
       "15  2016-17  45470  10223  55693     81.64     18.36\n",
       "16  2017-18  44783  12397  57180     78.32     21.68\n",
       "17  2018-19  44802  15747  60549     73.99     26.01\n",
       "18  2019-20  38083  13598  51681     73.69     26.31\n",
       "19  2020-21  38311  14227  52538     72.92     27.08\n",
       "20  2021-22   8729   3073  11802     73.96     26.04"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_fake_breakdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_fake_breakdown = real_fake_breakdown.sort_values(by='SEASON', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\danie\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1dedeca9148>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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itVptRTcDUJjJkydr6tSpxv2ixgxJTk7WI488YrzxrVq1qn766acCL2c5f/68ypQpY2pchP3796tly5bGN58rV65UixYt8m1b0BgKRTE7RsqCBQv04osvGveHDRumV155Jd+2Fy5cUPfu3Y0PwhaLRZs3b9Z9991nqqaCZGdn6/7779fhw4clSZ6envrvf/9bYOARFxenbt26GR8QO3XqpIULF+bbtrjXq5eE3Ne5N2vWzPhmtiDLli3T4MGDjfuFdTGfNWuWXn31VUmXP0jHxMQU2m0+KSlJjz76qHHeL1iwQJ07d87TrrSd7zdbTk6Ozp07l++YCFdLT09XixYtjPN59uzZeuaZZ/Jtm/t4r5g/f74ef/zxfNtbrVY98MADOn78uCTp3//+d55ZnK7o06ePVq1aJely2LZmzRoFBQXl23bnzp167LHHdO7cOWOZmXPx73L183Str+OsrCw5OzsX2e706dMKCwvTDz/8IEkaMWKEXn755XzbFvW3duHChRo+fLjx96p169ZauHBhgeOv3KzXbEnK/ftzc3PTnj178vztGDNmjHF52cMPP6yYmJhCt5n7Mq3Cfj+5Xbp0Kc/YWtnZ2apWrZrOnz8vqfC/T0VtSzL3PychIUGhoaGSLo+R8/PPPxc4romZfV7L383ly5dr0KBBki5P2bxjx4484XBxagCA0qh0fa0A/MNdunRJq1evVocOHYw3ptLlb34KGxOkfPnypj5USpdnBBowYIBxv6Bvyf4OH374ofFz3bp19Z///KfAtuXKldOcOXOMD5M2m83u8ddq7dq1xodOSZoyZUqhH5JatGhhXP5w5fEpKSnXXUdpd+rUKc2aNcuuO7iTk5PdN5u5nT9/Xu+9955x//XXXy9y7IH69evbBWsF/X5v1fP9WpUpU8ZUiCJd/kb4pZdeMu4X53i7d+9eYIgiXb4s5+mnnzbuJyQk5Nvu2LFjWrt2rXE/IiKiwBBFkh544AE9//zzpussaQ0aNJCLi0uRtzFjxtg9zkyIIl1+XeUOlK/1nJ08ebKGDh1qhCi9evXSsmXLCgxRbuZrtiTlvqynoIGYc/dSiY+P18GDBwvd5pEjR4yfH3zwQVN15BcAHD9+3AhRrndbZuWuPSAgwFSIcr37LKyGxo0bmwpRbnQNAHCzEaQAN0HXrl3tbo8//rhat26tGjVq6JlnnjGuCS5Tpoxee+21G3KNfG5NmjQxfv7+++9v6LbN2r9/v9315YMHDy5yANnq1aurS5cuxv0b8Y117g981atXN/Vt6nPPPWd0Mb506ZK++uqr666jNPjll1/ynJudOnXSQw89JD8/P7366qvGuCN33XWXlixZUuBlPV9//bXS0tIkXf5wf3V3+oLkbrdjxw67QPFalYbz/e90rcdrZiDJ3OMfHDhwIN9BIr/66itjgFKLxWIXZBW2bz4k/Z/cv8MrM5mYdenSJQ0ZMsSuF+SwYcP0/vvvF/o3tjS9Zm+U3377zS7wK+iYrkyHLF0O6XPP8JOf3GOo5B5LpriuHovlerZ1Lfvcv3+/MYbL3yl3Db/88ovdOEoA8E/BGCnATfDtt98W2aZLly4aMWKE6tWrV6xtnzt3Ths3blRSUpJ+//13nTp1SufOnTMGjJOkzMxM4+c///yzWNu/URITE+3uX+lqXJT27dsbb3IzMjL022+/qWbNmjekjnbt2pm6BtvX11f16tUzujDv2LFDAwcOvOYaSgur1VrkuVm+fHkNHjxYzz//vNzd3Qtsl3smi4ceesj0LEve3t666667dOLECWVnZ+unn35ScHBwge1vlfP9RsnOztbmzZv1ww8/aP/+/Tp58qTOnDljd7y5x0/IzMzU2bNnixwnpWzZsvmOkXC13IMj22w2nTx5Ms9YG1fGUZGke++919QApF5eXqpfv7527dpVZNuSlt+g0fkpaJYom82m7du3a+fOnUZIcvr06QIH0rTZbDp27JjuuuuuIvd55swZhYeHa/369ZIuh/FTpkwx9ffp73rN/p1y90a577778p2C/oonn3zS6GG3dOlSRUZGFjjeS6NGjfTzzz9LkqZOnSpXV1f17Nmz2AOgu7i4yM/PzxgnrH///nr77bfVvHnzYm2nOBo2bCiLxSKbzSar1aonn3xSb775Zr7jk9wsuf/W7Nu3z5iNr3r16n9bDQBwsxGkACUkMTEx32l4C3L69Gm9+eabmj9/fp6pSwtTnG86b6TcA8x6enoW+qE8t6vHRDlw4MA1BynZ2dnGVKCSihVa3XfffUaQkvtY/unOnz+vLVu2FNnL4JdffjF+/v7779W1a9di7eOKK+NxXO1WO9+v18WLF/X+++/r7bffLvA5KciJEyeK/OBfpUoVU5dLXX2JUX69D67MsiXlfb0Wpm7durdEkHKtg0bbbDYtXrxYU6dOtbuc0Ayz523Hjh2NIKt8+fL66KOP1KlTJ1OPvdmv2b/bpUuXtHTpUuN+UT1snnjiCb366qvKzs7W4cOHFRcXl2dWtisGDx6sZcuW6cKFCzp//ryGDh2ql19+WS1btlRISIiaNGmi+vXrm+plNXToUA0dOlTS5UGaO3furOrVq6tVq1Z68MEH1aRJk+v6suBq1apVU7du3fTFF19Ikr777jsFBQUpMDBQLVq0UFBQkIKCgm7qdNZNmzZVcHCwtm/fLunyjGErV65UkyZN9K9//UvBwcFq0qSJ6cuOAKA0IkgBboKrBwS0Wq1KSUlRdHS05s2bp5MnT+rIkSPq0aOHvvjiC/3rX/8qdHsZGRnq1KmT8Q1ZceTXNf/vkPuDQe6pKIty9bffRQ3ca7aG66njemooTa4e4NNmsyktLU2//fabFixYoKioKOXk5Gjnzp0KDQ3Vt99+W+Cb7dxTJP/xxx92gVVx5BeS3Irn+/U4e/asevXqpY0bN17T43N/yC2I2TEKrpa7J8wVuV8PlStXNr2tKlWqXFMNt4KcnBw9++yzWrZs2TU93szvULLvDXTl0jyzbuZrtiT897//NS6TdXR0VI8ePQpt7+HhoVatWhm9eRYvXlxgkFK3bl3NmzdPERERxow7J0+e1KpVq4xBlu+880498sgjxmxbBfV2fOaZZ3To0CHNnDnTeD0dPnxYCxcuNAYyr1q1qkJDQ/XUU0+pYcOGxXsi8jFjxgz99ddfdj0Qf/rpJ7uBYu+991517NhRTz/99E2Z1nrhwoXq2bOnMahyTk6Otm/fboQrZcqUUYMGDdSlSxc99dRTxfpbAgClAWOkAH8DFxcXBQYG6pVXXtGGDRuMD+nnzp3TgAEDivyGb8iQIXYfKh966CHNmjVLGzduVHJyso4eParMzExZrVZZrdZSMeBm7g8GxfkQd3XX6dwzfVxPDddTh9kPObcai8UiT09PhYSE6KOPPtInn3xidHVPSUnRoEGD8v0gLeXfU+Fa5He5w614vl+PCRMm2IUoDRo00JQpU/T1119r7969+vPPP/XXX38Zx/vjjz+WXLGyD6uu57X9T/Lhhx/ahSg1a9bUK6+8oi+//FI///yzjhw5Yvc7vNZwtl27dsbPn332md544w3Tj72Zr9mSkPuynpycHAUHB8vPz6/Q26ZNm4zHfPnll3aXBV6tQ4cO2rlzp4YMGZLv9NQnT57UmjVr1Lt3bz388MPau3dvgdsaP3684uLi9MQTT+Q7KPHRo0c1f/58tWjRQn369Lnu8L5SpUpasWKFFi1apObNm+d7GdeePXs0depUNWrUSFOmTLnhv1cvLy99++23evfdd9WkSZM8QVNOTo527dql8ePHq379+po/f/4N3T8A3Gz0SAH+ZrVq1dLcuXONQVWPHTumV199tcBpRnfv3m3Xi2D8+PEaPnx4ofu48g1aScp9vX9x6jl16lSB27meGopbR+5vXe+8885rruFW0qlTJw0fPlzTp0+XdHkq6OXLl6tnz5552uZ+bgcNGmQ38OX1uFXP92tltVr18ccfG/fDw8M1Y8aMQsfyKenjrVSpkvHztb6m/klycnI0Y8YM4367du20aNGiQkOmq//OmbVo0SKFh4cbr5Fp06YpOztb48ePL/KxN+s1WxLS0tKMniXS5d9B7h43Zpw/f17Lly+3m+79al5eXpowYYImTJigvXv3atu2bdq6davi4+N19OhRo91PP/2ksLAwbdy4Ud7e3vlu6/7779dHH32kixcv6n//+5+2b9+urVu3asuWLXbnw6pVq/Tnn3/qq6++uu4Bmjt06KAOHTro1KlT2rZtm7Zv364tW7Zox44dxoDR58+f15QpU3T27Fm99tpr17W/qzk4OOjf//63/v3vf+uvv/7S1q1btX37dmMcqCtB/alTpzR8+HDZbDa7WfMAoDSjRwpQAlq0aKFu3boZ9z/77DO769dzy90118fHR8OGDSty+7mnHiwpucdEOXz4sPGmrSi///57gdspLicnJ7sxH67edmFyT495PTXcal566SX5+PgY9ydNmpRvr6Dcl/ykp6ffsP3fquf7tfruu+908eJFSZfHJ5k0aVKRAyKX9PHm/t0fOnTI9OOK0/ZW8uOPP9q9BqZOnVpkT51r/R2WK1dOCxcutJt9bMaMGQVOU57bzXrNloRly5aZ/p9SmNy9WooSEBCgPn366KOPPtLu3bv13//+166HUEZGhhFCF6Zs2bIKDg7WkCFDtGzZMv3222/69NNP7QYvTkxMVHR0dPEOphCVKlVSmzZtNG7cOH311Vfav3+/pkyZYjdGyZw5c5SSknLD9nm1KlWq6LHHHtPEiRMVFxenPXv2KDIy0m6Gn9dee61UzQoFAIUhSAFKyLhx44zBH3NycjRp0qR82+W+jv3KaPxF2bZtm6kacs9YUNAlHNcq9+wJ586dM305wpXrp6XL32YFBgbesDqunkmoINnZ2XbTyt6Ia9ZvFeXLl9d//vMf4/7hw4c1b968PO1yz9ph9nk142ae76VR7uOtU6eOnJycinxMSR9v7tfUDz/8YOoD7ZVu/P9EuX+Hrq6uqlGjRpGPuZ7foaOjo+bNm6cnnnjCWPbuu+9q9OjRhT7uZr1mS0LuAKRXr152l0wVdYuLizMe+/PPPxtjeBSHxWJRUFCQli5dqpCQEGO5mRn7rlauXDl17NhRa9assRtH6Fq2ZZaLi4sGDx6sBQsWGMuys7P13Xff5Wl7s94neHl5afTo0Zo2bZqx7OTJk3bjAAFAaUaQApSQGjVq6MknnzTuf/XVV/m+oSvut25//fWX3aURhcndW+N6xiLJT+PGje2+aTI7CGPudvfff3++15MXR7NmzYyfN27cqNTU1CIfs379ertu4rm3cTvo3r273VSZs2fPzvMtYevWrY2fr8yAcSPczPO9NCru8V68ePGaBzS9UR566CHj58zMTG3YsKHIx2zcuFFpaWk3s6wScy09IxYtWnRd+3RwcNAHH3xg9z/kww8/1IgRIwr8sHuzXrN/t+3bt+vXX3817hdn9iHpckCbO+y6nt9FmTJlFBYWZty/nnPcw8PDLuz6O14vzZs3t7t0Nb993sz3CdLlmahy+6f+nQDwz0OQApSgkSNH2nUBz2/gQC8vL+Pn3Nc1FyQyMlJnz541tf/c2z5+/PgNnTrW2dnZ7g3uwoUL7d785mfZsmVKSkoy7j/zzDPXXcdTTz1lfKN28eJFvfLKK4W2P3/+vCZMmGDc9/b2VsuWLa+7jltJmTJl7L7dTktLsxvHQ5Lq16+v5s2bG/dHjRp1Q86fm3m+l0a5j3f37t1FDjI5bdo0/fnnnze5qsI9/PDDduNATJw4sdDfU3Z2tiZOnPh3lFYicv8OMzIytG/fvkLbL1q0yK7H27UqU6aM5syZY/d3ct68eRo6dGi+A4ferNfs3y13b5QqVarYHZNZjz/+uPFzVFRUnoCgOD0vTp8+bfx89cwzxe3BUdi2zCrOPs+fP2/32s1vn9fyPqE4NVw9zhKz9wC4VRCkACXI29tb//73v437//3vf/N0a3344YeNn//8888CLwHKzs7WuHHjtHz5ctP7v++++4zLi6TL10jfSC+++KLRK+XChQvq0aOH3dgjucXFxdmNh1GzZk27ruvXytfXV7169TLuL1u2TNOmTcv3jd7p06cVHh5u90HopZdeuu4B/25FXbp00b333mvcnz17dp43vBMmTDB+v8nJyQoLCysyLJMuXwoxceJEu0uIrriZ53tp9NBDDxmXL50/f14vvfSSLl26lKedzWbTnDlzTI3BcLOVKVPGbgDgn376SREREfnObnX+/Hk999xz/9jLeqTLve9yX5I1YsSIAsO96OhojRgx4obt22KxaNasWerfv7+x7NNPP9Wzzz6bb5hyM16zf6esrCytXLnSuN+xY8d8Z6QpSu4g5cSJE4qJibFb36xZMy1ZssQu2MjPwYMH7S59/Ne//mW3fsuWLerWrZs2bNiQ7+s6t6+++kqbN28ucFtmzZo1S0OHDi1w3LXc3njjDbvehvnt81reJ4wZM0Yvv/xygf/vr8jOztarr75q3K9QoYKaNGlS5PYBoDRg1h6ghI0YMUJLliwxPoS8/vrrdm8UH3zwQTVu3Nj4BnPWrFnauXOnevXqpRo1aujcuXP6+eeftWTJEuMNcd++fU1NJejs7Kz27dtr9erVki5/271kyRLVqVPH7rKcrl27Frv7tCT5+/trwoQJGjVqlKTLg702a9ZMvXv31iOPPCIXFxcdO3ZM69at08qVK41wo1y5cvroo4/sargekydP1pYtW4w3dW+88YbWr1+vJ598UrVq1VJ2drZ+/PFHLViwwG68g7CwMD399NM3pIZbzZVeKX369JF0+Zv2K5cOXFG/fn298847GjRokHJycvTzzz+radOmateunVq2bKl77rlHzs7OysrKUmpqqn7++Wdt3brVGC8nd8B1xc0830sjb29vde7c2XjNf/HFF9q3b5/69Omj2rVrKzs7W/v27dPy5cv1v//9T1LpON4+ffpo5cqVxnSyX3zxhXbt2qVnnnlG9erVk8Vi0c8//6xPP/1Uv/76qypUqKDWrVtr7dq1JVr3zVChQgX1799fs2fPliRt3rxZzZo1U9++fRUYGCiLxaLff/9d0dHRxhgUN/J3aLFYNH36dJUtW1bvv/++pMuB8aVLl/TBBx/YBcE34zX7d1q5cqVdoHtl9rviqlevnurUqWOE5osXL7YL7nfv3q3nnntOL730kpo3b64HHnhAtWvXNnpLHDt2TFu3btXy5cuNesqVK5dncGybzaZvvvlG33zzjTw8PNSqVSs1bNhQvr6+uvPOO3Xx4kUdPHhQ//3vf/Xll18a4dc999xzzV8knDt3TgsXLtTChQtVp04dPfLII6pfv76qVq0qJycnnT59Wnv37lV0dLRdz6iuXbuqVq1aebZ3Le8TTp48qaVLl+qdd95Rw4YN1axZMwUGBsrDw0MVK1bUiRMn9Msvv2j58uV2X1xERETcNrPkAbj1EaQAJaxatWp6+umnNXfuXEmXe2YkJCTowQcfNNrMnTtXbdu21fHjxyVdfqOe+5urKywWi0aPHq1mzZqZfpP+xhtvaNeuXUaAcOTIkTwzSlzPgK8DBw7U+fPnNX78eNlsNp0+fVofffSRPvroo3zbV6pUSYsXL1bjxo2veZ9Xu/POO7Vu3Tp169ZNe/bskSR9//33hXav79ChQ57LWW43nTp10n333Wd8s/nuu+9qwIABdm90u3fvrrvuuksDBgzQiRMnlJOTo6+++kpfffXVNe/3Zp7vpdFbb72lpKQk/fbbb5KkpKSkAqd8/ve//62hQ4eW+PFaLBYtXrxYXbp0MV5H+/fv18svv5ynraOjo2bNmqXff//9HxmkSJcvMbsyvawkHThwoMCZdFq3bq0pU6bc8N/h5MmTVa5cOSPQ+eKLL5Sdna25c+fa9dq4Ga/Zv8uSJUuMnz09Pa+514Z0OYSZMmWKJCk+Pl4HDx7MM1DwmTNn9OWXX+rLL78sdFsVKlTQRx99pPvuu6/ANmlpaVq6dKmWLl1a6LaqV6+u5cuX241Ncq327dtX5KVmkvTII49o1qxZBa6/nvcJu3btMtUj7YknntDYsWOLbAcApQWX9gClwIgRI+y+2bl6rBQ/Pz/FxcWpbdu2BW6jbt26+vzzzzVmzJhi7btatWravHmzJk2apEceeUReXl43rCfIFS+88IK+/fZbPfLII3YzAORWoUIF9erVS9u2bdMjjzxyQ/cvXT7ODRs2aNy4cXJzcyuwXa1atTR37lx9+umnKl++/A2v41ZisVjszqfMzEzjG+/c2rZtq//9738aPnx4kVNFly9fXg8//LCmT5+e75hA0s0930ujKlWq6JtvvtETTzxR4GVkNWrU0Hvvvad33333b66uYHfeeae+/PJLjRw5ssBBoQMDA7V27Vr17Nnzb67u71WhQgWtXr1agwYNKvDvhpeXlyZOnKgvvviiyOmRr9Vrr72mkSNHGvdXrlypPn36GFNsX3GjX7N/h+TkZLvZjjp27Fjg/xMzcl/eY7PZ7EKa6dOnq23btqpUqVKh26hQoYK6deumrVu35hk0Vbp8WczYsWPVpEkTu8tj8uPm5qahQ4dq69atdoN9F1fnzp01ePBg+fv7F9m2du3amjlzplauXFnosRb3fUJ4eLieeeYZ+fj4FFlDgwYNtGDBAn300UdFPkcAUJpYrFbrjZ3zFMBNdejQIW3dulWpqalycHCQl5eXAgMDFRAQUNKlmXL8+HFt2bJFx44dU1ZWlipXriwfHx+FhITckG/gzMjJydH//vc/7dmzRxkZGXJwcJC7u7saNWp0XW9gcfkDyS+//KJffvlFGRkZOn36tO644w65ubnJ399f9957rypWrGh6e7f6+V5cx44d05YtW4xvez08PFSnTp1SPwX32bNn9d133+ngwYM6d+6cvLy8VL9+fdWtW7ekS/vbZWZmKj4+XocOHVJ2drY8PDzk5+en4ODg6/rgf7Pc6NfsP8mlS5e0d+9e7d+/X3/++adOnz4tBwcHubi4qFatWmrYsKHpmeXOnj2rn3/+WQcOHFB6errOnj2rChUqqEqVKqpbt64CAwOvabyXwvz111/6+eefdfDgQWVmZurChQu64447VLVqVdWvXz/fS3lutGPHjmn37t36448/ZLValZ2dLScnJ1WvXl0NGjQwFbYAQGlEkAIAAAAAAGBS6ftqBAAAAAAAoJQiSAEAAAAAADCJIAUAAAAAAMAkghQAAAAAAACTCFIAAAAAAABMIkgBAAAAAAAwiSAFuIUkJyeXdAlAvjg3UVpxbqK04txEacW5CRSNIAUAAAAAAMAkghQAAAAAAACTCFIAAAAAAABMIkgBAAAAAAAwybGkCwAAAAAA4EbKzs7W6dOnS7oM3MKcnJzk6Jh/ZEKQAgAAAAD4x8jOztapU6fk4uIii8VS0uXgFmSz2WS1WlWpUqV8wxQu7QEAAAAA/GOcPn2aEAXXxWKxyMXFpcBeTQQpAAAAAIB/FEIUXK/CziGCFAAAAAAAAJMIUgAAAAAAAEwiSAEAAAAAAAoLC9NLL71U0mXka8mSJapWrVpJlyGJWXsAAAAAAP9wLp8c+Vv3Zw0v/gf+iIgI+fj4KDIyUi4uLvrxxx/l6+urQ4cOqUGDBka7O++8U7Vr19aIESMUGhp6I8vOIywsTE8++aQeeughNWjQQFartcjHxMTEqE+fPuratavmzp17w2p5/PHH1bZtW1NtrzxncXFxatiw4Q2r4Qp6pAAAAAAAUMqtWLFC+/bt0zfffKNGjRrp6aef1u7du0u6rDwWLVqkoUOHat26daaCF7MqVqwod3f3G7a960GQAgAAAABAKVelShV5enqqdu3aevnll3Xx4kXFx8cb6//880/17dtXvr6+8vX11RNPPKHffvvNWP/777+rV69eql27tu6++249/PDDio2NvaE1HjlyRPHx8XrhhRf0wAMP6PPPP7dbf/HiRY0aNUoBAQHy8PDQfff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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 14))\n",
    "\n",
    "\n",
    "ax.barh(y=real_fake_breakdown.SEASON, width=real_fake_breakdown.REAL_PCT.tolist(), label='\"Real\" Assists')\n",
    "ax.barh(y=real_fake_breakdown.SEASON, width=real_fake_breakdown.FAKE_PCT.tolist(), left=real_fake_breakdown.REAL_PCT.tolist(), label='Fake Assists')\n",
    "ax.set_title('Ratio of \"Real\" and \"Fake\" Assists', size=36)\n",
    "ax.set_xlabel(\"Percentage of Total Assists\")\n",
    "#ax.set_xticks([tick for tick in range(0, 110, 20)])\n",
    "ax.set_xticklabels(f\"{item}%\" for item in range(0, 105, 20))\n",
    "#ax.text(x=106, y=36, s='Number in brackets shows\\ntotal assists in 2019/20')\n",
    "ax.legend(loc='upper left', bbox_to_anchor=(1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_df = pd.read_csv(f'pbp_data/2001-02.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "shot_df = ShotChartDetail(team_id=0, player_id=0, season_nullable='2001-02', timeout=300).get_data_frames()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Jump Shot',\n",
       " 'Slam Dunk Shot',\n",
       " 'Layup Shot',\n",
       " 'Driving Layup Shot',\n",
       " 'Tip Shot',\n",
       " 'Turnaround Hook Shot',\n",
       " 'Jump Hook Shot',\n",
       " 'Turnaround Jump Shot',\n",
       " 'Hook Shot',\n",
       " 'Running Jump Shot',\n",
       " 'Driving Dunk Shot',\n",
       " 'Alley Oop Dunk Shot',\n",
       " 'Running Hook Shot',\n",
       " 'Reverse Layup Shot',\n",
       " 'Running Dunk Shot',\n",
       " 'Fadeaway Jump Shot',\n",
       " 'Dunk Shot',\n",
       " 'Running Layup Shot',\n",
       " 'Reverse Dunk Shot',\n",
       " 'Follow Up Dunk Shot',\n",
       " 'Alley Oop Layup shot',\n",
       " 'Finger Roll Shot',\n",
       " 'Driving Finger Roll Shot',\n",
       " 'Running Finger Roll Shot',\n",
       " 'Driving Hook Shot',\n",
       " 'Turnaround Finger Roll Shot',\n",
       " 'Running Tip Shot']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(shot_df.ACTION_TYPE.unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(shot_df.ACTION_TYPE.unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "shot_df = ShotChartDetail(team_id=0, player_id=0, season_nullable='2021-22', timeout=300).get_data_frames()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(shot_df.ACTION_TYPE.unique().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_game = pd.read_csv('per_game_avg.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = pd.merge(left=avg_game, right=real_fake_breakdown, left_on='Season', right_on='SEASON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.ticker import FormatStrFormatter\n",
    "import matplotlib.ticker as mtick\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation between assists per game and ratio of real assists')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 20))\n",
    "\n",
    "ax.scatter(x=merged_df.AST, y=merged_df.REAL_PCT, s=100)\n",
    "\n",
    "texts = []\n",
    "for i in range(len(merged_df)):\n",
    "    row = merged_df.iloc[i]\n",
    "    texts.append(ax.text(x=row.AST, y=row.REAL_PCT, s=row.Season))\n",
    "\n",
    "adjust_text(texts)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "ax.set_xlabel(\"Assists per game\", size=28)\n",
    "ax.set_ylabel(\"Real assists%\", size=28)\n",
    "ax.set_title(\"Correlation between assists per game and ratio of real assists\", size=36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9325756731493218"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.AST.corr(merged_df.REAL_PCT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Age</th>\n",
       "      <th>Wt</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>FT</th>\n",
       "      <th>...</th>\n",
       "      <th>eFG%</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>ORB%</th>\n",
       "      <th>FT/FGA</th>\n",
       "      <th>ORtg</th>\n",
       "      <th>REAL</th>\n",
       "      <th>FAKE</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>REAL_PCT</th>\n",
       "      <th>FAKE_PCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rk</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.802695</td>\n",
       "      <td>0.163577</td>\n",
       "      <td>0.423625</td>\n",
       "      <td>0.507136</td>\n",
       "      <td>-0.907791</td>\n",
       "      <td>-0.906016</td>\n",
       "      <td>-0.940914</td>\n",
       "      <td>-0.942686</td>\n",
       "      <td>0.696691</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.904133</td>\n",
       "      <td>0.792789</td>\n",
       "      <td>0.964123</td>\n",
       "      <td>0.849982</td>\n",
       "      <td>-0.743285</td>\n",
       "      <td>0.550807</td>\n",
       "      <td>-0.717070</td>\n",
       "      <td>0.210476</td>\n",
       "      <td>0.935911</td>\n",
       "      <td>-0.935911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.802695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.099075</td>\n",
       "      <td>0.183097</td>\n",
       "      <td>0.243127</td>\n",
       "      <td>-0.777221</td>\n",
       "      <td>-0.649716</td>\n",
       "      <td>-0.766785</td>\n",
       "      <td>-0.749120</td>\n",
       "      <td>0.258078</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.873215</td>\n",
       "      <td>0.641425</td>\n",
       "      <td>0.791458</td>\n",
       "      <td>0.445725</td>\n",
       "      <td>-0.826778</td>\n",
       "      <td>0.319203</td>\n",
       "      <td>-0.667855</td>\n",
       "      <td>0.022307</td>\n",
       "      <td>0.734939</td>\n",
       "      <td>-0.734939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wt</th>\n",
       "      <td>0.163577</td>\n",
       "      <td>0.099075</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.578406</td>\n",
       "      <td>0.494945</td>\n",
       "      <td>-0.364562</td>\n",
       "      <td>-0.441060</td>\n",
       "      <td>-0.454942</td>\n",
       "      <td>-0.463012</td>\n",
       "      <td>0.164325</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.324049</td>\n",
       "      <td>0.292040</td>\n",
       "      <td>0.262314</td>\n",
       "      <td>0.276416</td>\n",
       "      <td>-0.336667</td>\n",
       "      <td>0.555989</td>\n",
       "      <td>-0.073758</td>\n",
       "      <td>0.469253</td>\n",
       "      <td>0.417029</td>\n",
       "      <td>-0.417029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>0.423625</td>\n",
       "      <td>0.183097</td>\n",
       "      <td>0.578406</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.448495</td>\n",
       "      <td>-0.288734</td>\n",
       "      <td>-0.433103</td>\n",
       "      <td>-0.462273</td>\n",
       "      <td>-0.501800</td>\n",
       "      <td>0.613528</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.268221</td>\n",
       "      <td>0.179953</td>\n",
       "      <td>0.336369</td>\n",
       "      <td>0.576608</td>\n",
       "      <td>-0.121039</td>\n",
       "      <td>0.985686</td>\n",
       "      <td>0.196562</td>\n",
       "      <td>0.961222</td>\n",
       "      <td>0.444323</td>\n",
       "      <td>-0.444323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MP</th>\n",
       "      <td>0.507136</td>\n",
       "      <td>0.243127</td>\n",
       "      <td>0.494945</td>\n",
       "      <td>0.448495</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.598336</td>\n",
       "      <td>-0.629591</td>\n",
       "      <td>-0.619287</td>\n",
       "      <td>-0.616316</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.549162</td>\n",
       "      <td>0.676231</td>\n",
       "      <td>0.562981</td>\n",
       "      <td>0.625529</td>\n",
       "      <td>-0.517184</td>\n",
       "      <td>0.497135</td>\n",
       "      <td>-0.361765</td>\n",
       "      <td>0.302723</td>\n",
       "      <td>0.618067</td>\n",
       "      <td>-0.618067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FG</th>\n",
       "      <td>-0.907791</td>\n",
       "      <td>-0.777221</td>\n",
       "      <td>-0.364562</td>\n",
       "      <td>-0.288734</td>\n",
       "      <td>-0.598336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.957424</td>\n",
       "      <td>0.962298</td>\n",
       "      <td>0.951607</td>\n",
       "      <td>-0.519916</td>\n",
       "      <td>...</td>\n",
       "      <td>0.967103</td>\n",
       "      <td>-0.913756</td>\n",
       "      <td>-0.943309</td>\n",
       "      <td>-0.752616</td>\n",
       "      <td>0.896904</td>\n",
       "      <td>-0.413157</td>\n",
       "      <td>0.846280</td>\n",
       "      <td>-0.036043</td>\n",
       "      <td>-0.960465</td>\n",
       "      <td>0.960465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGA</th>\n",
       "      <td>-0.906016</td>\n",
       "      <td>-0.649716</td>\n",
       "      <td>-0.441060</td>\n",
       "      <td>-0.433103</td>\n",
       "      <td>-0.629591</td>\n",
       "      <td>0.957424</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971851</td>\n",
       "      <td>0.975412</td>\n",
       "      <td>-0.658695</td>\n",
       "      <td>...</td>\n",
       "      <td>0.876471</td>\n",
       "      <td>-0.862268</td>\n",
       "      <td>-0.922384</td>\n",
       "      <td>-0.862926</td>\n",
       "      <td>0.755133</td>\n",
       "      <td>-0.541098</td>\n",
       "      <td>0.781163</td>\n",
       "      <td>-0.176454</td>\n",
       "      <td>-0.984748</td>\n",
       "      <td>0.984748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3P</th>\n",
       "      <td>-0.940914</td>\n",
       "      <td>-0.766785</td>\n",
       "      <td>-0.454942</td>\n",
       "      <td>-0.462273</td>\n",
       "      <td>-0.619287</td>\n",
       "      <td>0.962298</td>\n",
       "      <td>0.971851</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998012</td>\n",
       "      <td>-0.628982</td>\n",
       "      <td>...</td>\n",
       "      <td>0.938625</td>\n",
       "      <td>-0.849000</td>\n",
       "      <td>-0.957334</td>\n",
       "      <td>-0.828164</td>\n",
       "      <td>0.820704</td>\n",
       "      <td>-0.576849</td>\n",
       "      <td>0.754539</td>\n",
       "      <td>-0.219018</td>\n",
       "      <td>-0.986457</td>\n",
       "      <td>0.986457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3PA</th>\n",
       "      <td>-0.942686</td>\n",
       "      <td>-0.749120</td>\n",
       "      <td>-0.463012</td>\n",
       "      <td>-0.501800</td>\n",
       "      <td>-0.616316</td>\n",
       "      <td>0.951607</td>\n",
       "      <td>0.975412</td>\n",
       "      <td>0.998012</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.652039</td>\n",
       "      <td>...</td>\n",
       "      <td>0.920876</td>\n",
       "      <td>-0.835238</td>\n",
       "      <td>-0.953073</td>\n",
       "      <td>-0.844635</td>\n",
       "      <td>0.792061</td>\n",
       "      <td>-0.613400</td>\n",
       "      <td>0.729425</td>\n",
       "      <td>-0.261704</td>\n",
       "      <td>-0.987891</td>\n",
       "      <td>0.987891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT</th>\n",
       "      <td>0.696691</td>\n",
       "      <td>0.258078</td>\n",
       "      <td>0.164325</td>\n",
       "      <td>0.613528</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>-0.519916</td>\n",
       "      <td>-0.658695</td>\n",
       "      <td>-0.628982</td>\n",
       "      <td>-0.652039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.441667</td>\n",
       "      <td>0.396727</td>\n",
       "      <td>0.603139</td>\n",
       "      <td>0.947947</td>\n",
       "      <td>-0.182039</td>\n",
       "      <td>0.661517</td>\n",
       "      <td>-0.308092</td>\n",
       "      <td>0.471278</td>\n",
       "      <td>0.657317</td>\n",
       "      <td>-0.657317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTA</th>\n",
       "      <td>0.764273</td>\n",
       "      <td>0.341448</td>\n",
       "      <td>0.232174</td>\n",
       "      <td>0.627328</td>\n",
       "      <td>0.625972</td>\n",
       "      <td>-0.623812</td>\n",
       "      <td>-0.744614</td>\n",
       "      <td>-0.721252</td>\n",
       "      <td>-0.739768</td>\n",
       "      <td>0.984328</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.550232</td>\n",
       "      <td>0.508174</td>\n",
       "      <td>0.685568</td>\n",
       "      <td>0.973702</td>\n",
       "      <td>-0.315411</td>\n",
       "      <td>0.689182</td>\n",
       "      <td>-0.392020</td>\n",
       "      <td>0.462923</td>\n",
       "      <td>0.745435</td>\n",
       "      <td>-0.745435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORB</th>\n",
       "      <td>0.914163</td>\n",
       "      <td>0.845901</td>\n",
       "      <td>0.159513</td>\n",
       "      <td>0.215371</td>\n",
       "      <td>0.488695</td>\n",
       "      <td>-0.893248</td>\n",
       "      <td>-0.808372</td>\n",
       "      <td>-0.882326</td>\n",
       "      <td>-0.867888</td>\n",
       "      <td>0.470387</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.946920</td>\n",
       "      <td>0.804997</td>\n",
       "      <td>0.966721</td>\n",
       "      <td>0.655610</td>\n",
       "      <td>-0.839298</td>\n",
       "      <td>0.355268</td>\n",
       "      <td>-0.758797</td>\n",
       "      <td>0.018710</td>\n",
       "      <td>0.857875</td>\n",
       "      <td>-0.857875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB</th>\n",
       "      <td>-0.931577</td>\n",
       "      <td>-0.659428</td>\n",
       "      <td>-0.397758</td>\n",
       "      <td>-0.446325</td>\n",
       "      <td>-0.610753</td>\n",
       "      <td>0.934670</td>\n",
       "      <td>0.986333</td>\n",
       "      <td>0.971004</td>\n",
       "      <td>0.977585</td>\n",
       "      <td>-0.685290</td>\n",
       "      <td>...</td>\n",
       "      <td>0.867336</td>\n",
       "      <td>-0.834156</td>\n",
       "      <td>-0.953187</td>\n",
       "      <td>-0.874644</td>\n",
       "      <td>0.714076</td>\n",
       "      <td>-0.559262</td>\n",
       "      <td>0.760400</td>\n",
       "      <td>-0.200938</td>\n",
       "      <td>-0.981048</td>\n",
       "      <td>0.981048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRB</th>\n",
       "      <td>-0.814102</td>\n",
       "      <td>-0.463695</td>\n",
       "      <td>-0.483454</td>\n",
       "      <td>-0.511695</td>\n",
       "      <td>-0.593203</td>\n",
       "      <td>0.831388</td>\n",
       "      <td>0.953534</td>\n",
       "      <td>0.889723</td>\n",
       "      <td>0.907269</td>\n",
       "      <td>-0.702852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.704551</td>\n",
       "      <td>-0.739033</td>\n",
       "      <td>-0.818013</td>\n",
       "      <td>-0.872402</td>\n",
       "      <td>0.548072</td>\n",
       "      <td>-0.594094</td>\n",
       "      <td>0.660700</td>\n",
       "      <td>-0.271547</td>\n",
       "      <td>-0.916876</td>\n",
       "      <td>0.916876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AST</th>\n",
       "      <td>-0.800014</td>\n",
       "      <td>-0.604508</td>\n",
       "      <td>-0.515917</td>\n",
       "      <td>-0.301696</td>\n",
       "      <td>-0.590673</td>\n",
       "      <td>0.927256</td>\n",
       "      <td>0.952228</td>\n",
       "      <td>0.925863</td>\n",
       "      <td>0.918045</td>\n",
       "      <td>-0.557456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.843494</td>\n",
       "      <td>-0.814590</td>\n",
       "      <td>-0.836119</td>\n",
       "      <td>-0.775546</td>\n",
       "      <td>0.761602</td>\n",
       "      <td>-0.395540</td>\n",
       "      <td>0.823465</td>\n",
       "      <td>-0.029264</td>\n",
       "      <td>-0.932576</td>\n",
       "      <td>0.932576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STL</th>\n",
       "      <td>-0.249476</td>\n",
       "      <td>0.250323</td>\n",
       "      <td>-0.203345</td>\n",
       "      <td>-0.417719</td>\n",
       "      <td>-0.282112</td>\n",
       "      <td>0.117154</td>\n",
       "      <td>0.371785</td>\n",
       "      <td>0.267940</td>\n",
       "      <td>0.299745</td>\n",
       "      <td>-0.715083</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042235</td>\n",
       "      <td>-0.028734</td>\n",
       "      <td>-0.188733</td>\n",
       "      <td>-0.639857</td>\n",
       "      <td>-0.263992</td>\n",
       "      <td>-0.403675</td>\n",
       "      <td>0.086374</td>\n",
       "      <td>-0.327735</td>\n",
       "      <td>-0.318699</td>\n",
       "      <td>0.318699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLK</th>\n",
       "      <td>0.135180</td>\n",
       "      <td>0.395638</td>\n",
       "      <td>-0.034033</td>\n",
       "      <td>-0.127070</td>\n",
       "      <td>-0.137199</td>\n",
       "      <td>-0.118209</td>\n",
       "      <td>0.035901</td>\n",
       "      <td>-0.105510</td>\n",
       "      <td>-0.086226</td>\n",
       "      <td>-0.346433</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.290504</td>\n",
       "      <td>0.114598</td>\n",
       "      <td>0.221176</td>\n",
       "      <td>-0.258186</td>\n",
       "      <td>-0.342882</td>\n",
       "      <td>-0.101460</td>\n",
       "      <td>-0.018326</td>\n",
       "      <td>-0.098189</td>\n",
       "      <td>-0.010177</td>\n",
       "      <td>0.010177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOV</th>\n",
       "      <td>0.471893</td>\n",
       "      <td>0.449363</td>\n",
       "      <td>0.012710</td>\n",
       "      <td>-0.063446</td>\n",
       "      <td>0.579985</td>\n",
       "      <td>-0.584352</td>\n",
       "      <td>-0.455849</td>\n",
       "      <td>-0.473128</td>\n",
       "      <td>-0.445886</td>\n",
       "      <td>0.124274</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.611888</td>\n",
       "      <td>0.828274</td>\n",
       "      <td>0.516607</td>\n",
       "      <td>0.278021</td>\n",
       "      <td>-0.727552</td>\n",
       "      <td>0.037810</td>\n",
       "      <td>-0.527196</td>\n",
       "      <td>-0.174369</td>\n",
       "      <td>0.466018</td>\n",
       "      <td>-0.466018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PF</th>\n",
       "      <td>0.761543</td>\n",
       "      <td>0.382135</td>\n",
       "      <td>0.009984</td>\n",
       "      <td>0.495747</td>\n",
       "      <td>0.605374</td>\n",
       "      <td>-0.593413</td>\n",
       "      <td>-0.646231</td>\n",
       "      <td>-0.646528</td>\n",
       "      <td>-0.654095</td>\n",
       "      <td>0.899052</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.562476</td>\n",
       "      <td>0.528553</td>\n",
       "      <td>0.667100</td>\n",
       "      <td>0.879465</td>\n",
       "      <td>-0.364582</td>\n",
       "      <td>0.569841</td>\n",
       "      <td>-0.378724</td>\n",
       "      <td>0.361197</td>\n",
       "      <td>0.657808</td>\n",
       "      <td>-0.657808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTS</th>\n",
       "      <td>-0.885309</td>\n",
       "      <td>-0.807418</td>\n",
       "      <td>-0.409892</td>\n",
       "      <td>-0.275868</td>\n",
       "      <td>-0.562410</td>\n",
       "      <td>0.989015</td>\n",
       "      <td>0.937707</td>\n",
       "      <td>0.960154</td>\n",
       "      <td>0.947632</td>\n",
       "      <td>-0.426374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.973145</td>\n",
       "      <td>-0.906865</td>\n",
       "      <td>-0.934765</td>\n",
       "      <td>-0.680015</td>\n",
       "      <td>0.925308</td>\n",
       "      <td>-0.400520</td>\n",
       "      <td>0.836622</td>\n",
       "      <td>-0.028530</td>\n",
       "      <td>-0.947109</td>\n",
       "      <td>0.947109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FG%</th>\n",
       "      <td>-0.539625</td>\n",
       "      <td>-0.775638</td>\n",
       "      <td>-0.001825</td>\n",
       "      <td>0.181725</td>\n",
       "      <td>-0.256943</td>\n",
       "      <td>0.677942</td>\n",
       "      <td>0.438099</td>\n",
       "      <td>0.530410</td>\n",
       "      <td>0.489506</td>\n",
       "      <td>0.042902</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788307</td>\n",
       "      <td>-0.654456</td>\n",
       "      <td>-0.605719</td>\n",
       "      <td>-0.157189</td>\n",
       "      <td>0.871394</td>\n",
       "      <td>0.067754</td>\n",
       "      <td>0.634752</td>\n",
       "      <td>0.311486</td>\n",
       "      <td>-0.491723</td>\n",
       "      <td>0.491723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3P%</th>\n",
       "      <td>-0.126952</td>\n",
       "      <td>-0.415698</td>\n",
       "      <td>0.100456</td>\n",
       "      <td>0.477370</td>\n",
       "      <td>-0.108715</td>\n",
       "      <td>0.290425</td>\n",
       "      <td>0.053964</td>\n",
       "      <td>0.160534</td>\n",
       "      <td>0.102339</td>\n",
       "      <td>0.301205</td>\n",
       "      <td>...</td>\n",
       "      <td>0.426464</td>\n",
       "      <td>-0.351831</td>\n",
       "      <td>-0.209727</td>\n",
       "      <td>0.174804</td>\n",
       "      <td>0.593162</td>\n",
       "      <td>0.415537</td>\n",
       "      <td>0.399108</td>\n",
       "      <td>0.530151</td>\n",
       "      <td>-0.091664</td>\n",
       "      <td>0.091664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT%</th>\n",
       "      <td>-0.685196</td>\n",
       "      <td>-0.571278</td>\n",
       "      <td>-0.456979</td>\n",
       "      <td>-0.372297</td>\n",
       "      <td>-0.680471</td>\n",
       "      <td>0.790525</td>\n",
       "      <td>0.756943</td>\n",
       "      <td>0.787487</td>\n",
       "      <td>0.774089</td>\n",
       "      <td>-0.365288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.789608</td>\n",
       "      <td>-0.768070</td>\n",
       "      <td>-0.728564</td>\n",
       "      <td>-0.562170</td>\n",
       "      <td>0.797946</td>\n",
       "      <td>-0.465529</td>\n",
       "      <td>0.571556</td>\n",
       "      <td>-0.191516</td>\n",
       "      <td>-0.769653</td>\n",
       "      <td>0.769653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pace</th>\n",
       "      <td>-0.912632</td>\n",
       "      <td>-0.735478</td>\n",
       "      <td>-0.426296</td>\n",
       "      <td>-0.361774</td>\n",
       "      <td>-0.570483</td>\n",
       "      <td>0.976083</td>\n",
       "      <td>0.981886</td>\n",
       "      <td>0.978172</td>\n",
       "      <td>0.976500</td>\n",
       "      <td>-0.550942</td>\n",
       "      <td>...</td>\n",
       "      <td>0.925081</td>\n",
       "      <td>-0.863444</td>\n",
       "      <td>-0.953398</td>\n",
       "      <td>-0.782569</td>\n",
       "      <td>0.818118</td>\n",
       "      <td>-0.479740</td>\n",
       "      <td>0.816795</td>\n",
       "      <td>-0.107376</td>\n",
       "      <td>-0.979372</td>\n",
       "      <td>0.979372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eFG%</th>\n",
       "      <td>-0.904133</td>\n",
       "      <td>-0.873215</td>\n",
       "      <td>-0.324049</td>\n",
       "      <td>-0.268221</td>\n",
       "      <td>-0.549162</td>\n",
       "      <td>0.967103</td>\n",
       "      <td>0.876471</td>\n",
       "      <td>0.938625</td>\n",
       "      <td>0.920876</td>\n",
       "      <td>-0.441667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.874798</td>\n",
       "      <td>-0.942204</td>\n",
       "      <td>-0.663495</td>\n",
       "      <td>0.944830</td>\n",
       "      <td>-0.397754</td>\n",
       "      <td>0.792841</td>\n",
       "      <td>-0.043346</td>\n",
       "      <td>-0.910238</td>\n",
       "      <td>0.910238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOV%</th>\n",
       "      <td>0.792789</td>\n",
       "      <td>0.641425</td>\n",
       "      <td>0.292040</td>\n",
       "      <td>0.179953</td>\n",
       "      <td>0.676231</td>\n",
       "      <td>-0.913756</td>\n",
       "      <td>-0.862268</td>\n",
       "      <td>-0.849000</td>\n",
       "      <td>-0.835238</td>\n",
       "      <td>0.396727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.874798</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843812</td>\n",
       "      <td>0.630653</td>\n",
       "      <td>-0.882644</td>\n",
       "      <td>0.300832</td>\n",
       "      <td>-0.792985</td>\n",
       "      <td>-0.043592</td>\n",
       "      <td>0.851440</td>\n",
       "      <td>-0.851440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORB%</th>\n",
       "      <td>0.964123</td>\n",
       "      <td>0.791458</td>\n",
       "      <td>0.262314</td>\n",
       "      <td>0.336369</td>\n",
       "      <td>0.562981</td>\n",
       "      <td>-0.943309</td>\n",
       "      <td>-0.922384</td>\n",
       "      <td>-0.957334</td>\n",
       "      <td>-0.953073</td>\n",
       "      <td>0.603139</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.942204</td>\n",
       "      <td>0.843812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792811</td>\n",
       "      <td>-0.802304</td>\n",
       "      <td>0.469283</td>\n",
       "      <td>-0.785020</td>\n",
       "      <td>0.110555</td>\n",
       "      <td>0.948735</td>\n",
       "      <td>-0.948735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FT/FGA</th>\n",
       "      <td>0.849982</td>\n",
       "      <td>0.445725</td>\n",
       "      <td>0.276416</td>\n",
       "      <td>0.576608</td>\n",
       "      <td>0.625529</td>\n",
       "      <td>-0.752616</td>\n",
       "      <td>-0.862926</td>\n",
       "      <td>-0.828164</td>\n",
       "      <td>-0.844635</td>\n",
       "      <td>0.947947</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.663495</td>\n",
       "      <td>0.630653</td>\n",
       "      <td>0.792811</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.438142</td>\n",
       "      <td>0.655958</td>\n",
       "      <td>-0.547435</td>\n",
       "      <td>0.371746</td>\n",
       "      <td>0.854962</td>\n",
       "      <td>-0.854962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORtg</th>\n",
       "      <td>-0.743285</td>\n",
       "      <td>-0.826778</td>\n",
       "      <td>-0.336667</td>\n",
       "      <td>-0.121039</td>\n",
       "      <td>-0.517184</td>\n",
       "      <td>0.896904</td>\n",
       "      <td>0.755133</td>\n",
       "      <td>0.820704</td>\n",
       "      <td>0.792061</td>\n",
       "      <td>-0.182039</td>\n",
       "      <td>...</td>\n",
       "      <td>0.944830</td>\n",
       "      <td>-0.882644</td>\n",
       "      <td>-0.802304</td>\n",
       "      <td>-0.438142</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.241031</td>\n",
       "      <td>0.754686</td>\n",
       "      <td>0.082068</td>\n",
       "      <td>-0.783136</td>\n",
       "      <td>0.783136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REAL</th>\n",
       "      <td>0.550807</td>\n",
       "      <td>0.319203</td>\n",
       "      <td>0.555989</td>\n",
       "      <td>0.985686</td>\n",
       "      <td>0.497135</td>\n",
       "      <td>-0.413157</td>\n",
       "      <td>-0.541098</td>\n",
       "      <td>-0.576849</td>\n",
       "      <td>-0.613400</td>\n",
       "      <td>0.661517</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.397754</td>\n",
       "      <td>0.300832</td>\n",
       "      <td>0.469283</td>\n",
       "      <td>0.655958</td>\n",
       "      <td>-0.241031</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057053</td>\n",
       "      <td>0.918942</td>\n",
       "      <td>0.564762</td>\n",
       "      <td>-0.564762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAKE</th>\n",
       "      <td>-0.717070</td>\n",
       "      <td>-0.667855</td>\n",
       "      <td>-0.073758</td>\n",
       "      <td>0.196562</td>\n",
       "      <td>-0.361765</td>\n",
       "      <td>0.846280</td>\n",
       "      <td>0.781163</td>\n",
       "      <td>0.754539</td>\n",
       "      <td>0.729425</td>\n",
       "      <td>-0.308092</td>\n",
       "      <td>...</td>\n",
       "      <td>0.792841</td>\n",
       "      <td>-0.792985</td>\n",
       "      <td>-0.785020</td>\n",
       "      <td>-0.547435</td>\n",
       "      <td>0.754686</td>\n",
       "      <td>0.057053</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.446180</td>\n",
       "      <td>-0.785223</td>\n",
       "      <td>0.785223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>0.210476</td>\n",
       "      <td>0.022307</td>\n",
       "      <td>0.469253</td>\n",
       "      <td>0.961222</td>\n",
       "      <td>0.302723</td>\n",
       "      <td>-0.036043</td>\n",
       "      <td>-0.176454</td>\n",
       "      <td>-0.219018</td>\n",
       "      <td>-0.261704</td>\n",
       "      <td>0.471278</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043346</td>\n",
       "      <td>-0.043592</td>\n",
       "      <td>0.110555</td>\n",
       "      <td>0.371746</td>\n",
       "      <td>0.082068</td>\n",
       "      <td>0.918942</td>\n",
       "      <td>0.446180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.196063</td>\n",
       "      <td>-0.196063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REAL_PCT</th>\n",
       "      <td>0.935911</td>\n",
       "      <td>0.734939</td>\n",
       "      <td>0.417029</td>\n",
       "      <td>0.444323</td>\n",
       "      <td>0.618067</td>\n",
       "      <td>-0.960465</td>\n",
       "      <td>-0.984748</td>\n",
       "      <td>-0.986457</td>\n",
       "      <td>-0.987891</td>\n",
       "      <td>0.657317</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.910238</td>\n",
       "      <td>0.851440</td>\n",
       "      <td>0.948735</td>\n",
       "      <td>0.854962</td>\n",
       "      <td>-0.783136</td>\n",
       "      <td>0.564762</td>\n",
       "      <td>-0.785223</td>\n",
       "      <td>0.196063</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAKE_PCT</th>\n",
       "      <td>-0.935911</td>\n",
       "      <td>-0.734939</td>\n",
       "      <td>-0.417029</td>\n",
       "      <td>-0.444323</td>\n",
       "      <td>-0.618067</td>\n",
       "      <td>0.960465</td>\n",
       "      <td>0.984748</td>\n",
       "      <td>0.986457</td>\n",
       "      <td>0.987891</td>\n",
       "      <td>-0.657317</td>\n",
       "      <td>...</td>\n",
       "      <td>0.910238</td>\n",
       "      <td>-0.851440</td>\n",
       "      <td>-0.948735</td>\n",
       "      <td>-0.854962</td>\n",
       "      <td>0.783136</td>\n",
       "      <td>-0.564762</td>\n",
       "      <td>0.785223</td>\n",
       "      <td>-0.196063</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Rk       Age        Wt         G        MP        FG  \\\n",
       "Rk        1.000000  0.802695  0.163577  0.423625  0.507136 -0.907791   \n",
       "Age       0.802695  1.000000  0.099075  0.183097  0.243127 -0.777221   \n",
       "Wt        0.163577  0.099075  1.000000  0.578406  0.494945 -0.364562   \n",
       "G         0.423625  0.183097  0.578406  1.000000  0.448495 -0.288734   \n",
       "MP        0.507136  0.243127  0.494945  0.448495  1.000000 -0.598336   \n",
       "FG       -0.907791 -0.777221 -0.364562 -0.288734 -0.598336  1.000000   \n",
       "FGA      -0.906016 -0.649716 -0.441060 -0.433103 -0.629591  0.957424   \n",
       "3P       -0.940914 -0.766785 -0.454942 -0.462273 -0.619287  0.962298   \n",
       "3PA      -0.942686 -0.749120 -0.463012 -0.501800 -0.616316  0.951607   \n",
       "FT        0.696691  0.258078  0.164325  0.613528  0.545455 -0.519916   \n",
       "FTA       0.764273  0.341448  0.232174  0.627328  0.625972 -0.623812   \n",
       "ORB       0.914163  0.845901  0.159513  0.215371  0.488695 -0.893248   \n",
       "DRB      -0.931577 -0.659428 -0.397758 -0.446325 -0.610753  0.934670   \n",
       "TRB      -0.814102 -0.463695 -0.483454 -0.511695 -0.593203  0.831388   \n",
       "AST      -0.800014 -0.604508 -0.515917 -0.301696 -0.590673  0.927256   \n",
       "STL      -0.249476  0.250323 -0.203345 -0.417719 -0.282112  0.117154   \n",
       "BLK       0.135180  0.395638 -0.034033 -0.127070 -0.137199 -0.118209   \n",
       "TOV       0.471893  0.449363  0.012710 -0.063446  0.579985 -0.584352   \n",
       "PF        0.761543  0.382135  0.009984  0.495747  0.605374 -0.593413   \n",
       "PTS      -0.885309 -0.807418 -0.409892 -0.275868 -0.562410  0.989015   \n",
       "FG%      -0.539625 -0.775638 -0.001825  0.181725 -0.256943  0.677942   \n",
       "3P%      -0.126952 -0.415698  0.100456  0.477370 -0.108715  0.290425   \n",
       "FT%      -0.685196 -0.571278 -0.456979 -0.372297 -0.680471  0.790525   \n",
       "Pace     -0.912632 -0.735478 -0.426296 -0.361774 -0.570483  0.976083   \n",
       "eFG%     -0.904133 -0.873215 -0.324049 -0.268221 -0.549162  0.967103   \n",
       "TOV%      0.792789  0.641425  0.292040  0.179953  0.676231 -0.913756   \n",
       "ORB%      0.964123  0.791458  0.262314  0.336369  0.562981 -0.943309   \n",
       "FT/FGA    0.849982  0.445725  0.276416  0.576608  0.625529 -0.752616   \n",
       "ORtg     -0.743285 -0.826778 -0.336667 -0.121039 -0.517184  0.896904   \n",
       "REAL      0.550807  0.319203  0.555989  0.985686  0.497135 -0.413157   \n",
       "FAKE     -0.717070 -0.667855 -0.073758  0.196562 -0.361765  0.846280   \n",
       "TOTAL     0.210476  0.022307  0.469253  0.961222  0.302723 -0.036043   \n",
       "REAL_PCT  0.935911  0.734939  0.417029  0.444323  0.618067 -0.960465   \n",
       "FAKE_PCT -0.935911 -0.734939 -0.417029 -0.444323 -0.618067  0.960465   \n",
       "\n",
       "               FGA        3P       3PA        FT  ...      eFG%      TOV%  \\\n",
       "Rk       -0.906016 -0.940914 -0.942686  0.696691  ... -0.904133  0.792789   \n",
       "Age      -0.649716 -0.766785 -0.749120  0.258078  ... -0.873215  0.641425   \n",
       "Wt       -0.441060 -0.454942 -0.463012  0.164325  ... -0.324049  0.292040   \n",
       "G        -0.433103 -0.462273 -0.501800  0.613528  ... -0.268221  0.179953   \n",
       "MP       -0.629591 -0.619287 -0.616316  0.545455  ... -0.549162  0.676231   \n",
       "FG        0.957424  0.962298  0.951607 -0.519916  ...  0.967103 -0.913756   \n",
       "FGA       1.000000  0.971851  0.975412 -0.658695  ...  0.876471 -0.862268   \n",
       "3P        0.971851  1.000000  0.998012 -0.628982  ...  0.938625 -0.849000   \n",
       "3PA       0.975412  0.998012  1.000000 -0.652039  ...  0.920876 -0.835238   \n",
       "FT       -0.658695 -0.628982 -0.652039  1.000000  ... -0.441667  0.396727   \n",
       "FTA      -0.744614 -0.721252 -0.739768  0.984328  ... -0.550232  0.508174   \n",
       "ORB      -0.808372 -0.882326 -0.867888  0.470387  ... -0.946920  0.804997   \n",
       "DRB       0.986333  0.971004  0.977585 -0.685290  ...  0.867336 -0.834156   \n",
       "TRB       0.953534  0.889723  0.907269 -0.702852  ...  0.704551 -0.739033   \n",
       "AST       0.952228  0.925863  0.918045 -0.557456  ...  0.843494 -0.814590   \n",
       "STL       0.371785  0.267940  0.299745 -0.715083  ... -0.042235 -0.028734   \n",
       "BLK       0.035901 -0.105510 -0.086226 -0.346433  ... -0.290504  0.114598   \n",
       "TOV      -0.455849 -0.473128 -0.445886  0.124274  ... -0.611888  0.828274   \n",
       "PF       -0.646231 -0.646528 -0.654095  0.899052  ... -0.562476  0.528553   \n",
       "PTS       0.937707  0.960154  0.947632 -0.426374  ...  0.973145 -0.906865   \n",
       "FG%       0.438099  0.530410  0.489506  0.042902  ...  0.788307 -0.654456   \n",
       "3P%       0.053964  0.160534  0.102339  0.301205  ...  0.426464 -0.351831   \n",
       "FT%       0.756943  0.787487  0.774089 -0.365288  ...  0.789608 -0.768070   \n",
       "Pace      0.981886  0.978172  0.976500 -0.550942  ...  0.925081 -0.863444   \n",
       "eFG%      0.876471  0.938625  0.920876 -0.441667  ...  1.000000 -0.874798   \n",
       "TOV%     -0.862268 -0.849000 -0.835238  0.396727  ... -0.874798  1.000000   \n",
       "ORB%     -0.922384 -0.957334 -0.953073  0.603139  ... -0.942204  0.843812   \n",
       "FT/FGA   -0.862926 -0.828164 -0.844635  0.947947  ... -0.663495  0.630653   \n",
       "ORtg      0.755133  0.820704  0.792061 -0.182039  ...  0.944830 -0.882644   \n",
       "REAL     -0.541098 -0.576849 -0.613400  0.661517  ... -0.397754  0.300832   \n",
       "FAKE      0.781163  0.754539  0.729425 -0.308092  ...  0.792841 -0.792985   \n",
       "TOTAL    -0.176454 -0.219018 -0.261704  0.471278  ... -0.043346 -0.043592   \n",
       "REAL_PCT -0.984748 -0.986457 -0.987891  0.657317  ... -0.910238  0.851440   \n",
       "FAKE_PCT  0.984748  0.986457  0.987891 -0.657317  ...  0.910238 -0.851440   \n",
       "\n",
       "              ORB%    FT/FGA      ORtg      REAL      FAKE     TOTAL  \\\n",
       "Rk        0.964123  0.849982 -0.743285  0.550807 -0.717070  0.210476   \n",
       "Age       0.791458  0.445725 -0.826778  0.319203 -0.667855  0.022307   \n",
       "Wt        0.262314  0.276416 -0.336667  0.555989 -0.073758  0.469253   \n",
       "G         0.336369  0.576608 -0.121039  0.985686  0.196562  0.961222   \n",
       "MP        0.562981  0.625529 -0.517184  0.497135 -0.361765  0.302723   \n",
       "FG       -0.943309 -0.752616  0.896904 -0.413157  0.846280 -0.036043   \n",
       "FGA      -0.922384 -0.862926  0.755133 -0.541098  0.781163 -0.176454   \n",
       "3P       -0.957334 -0.828164  0.820704 -0.576849  0.754539 -0.219018   \n",
       "3PA      -0.953073 -0.844635  0.792061 -0.613400  0.729425 -0.261704   \n",
       "FT        0.603139  0.947947 -0.182039  0.661517 -0.308092  0.471278   \n",
       "FTA       0.685568  0.973702 -0.315411  0.689182 -0.392020  0.462923   \n",
       "ORB       0.966721  0.655610 -0.839298  0.355268 -0.758797  0.018710   \n",
       "DRB      -0.953187 -0.874644  0.714076 -0.559262  0.760400 -0.200938   \n",
       "TRB      -0.818013 -0.872402  0.548072 -0.594094  0.660700 -0.271547   \n",
       "AST      -0.836119 -0.775546  0.761602 -0.395540  0.823465 -0.029264   \n",
       "STL      -0.188733 -0.639857 -0.263992 -0.403675  0.086374 -0.327735   \n",
       "BLK       0.221176 -0.258186 -0.342882 -0.101460 -0.018326 -0.098189   \n",
       "TOV       0.516607  0.278021 -0.727552  0.037810 -0.527196 -0.174369   \n",
       "PF        0.667100  0.879465 -0.364582  0.569841 -0.378724  0.361197   \n",
       "PTS      -0.934765 -0.680015  0.925308 -0.400520  0.836622 -0.028530   \n",
       "FG%      -0.605719 -0.157189  0.871394  0.067754  0.634752  0.311486   \n",
       "3P%      -0.209727  0.174804  0.593162  0.415537  0.399108  0.530151   \n",
       "FT%      -0.728564 -0.562170  0.797946 -0.465529  0.571556 -0.191516   \n",
       "Pace     -0.953398 -0.782569  0.818118 -0.479740  0.816795 -0.107376   \n",
       "eFG%     -0.942204 -0.663495  0.944830 -0.397754  0.792841 -0.043346   \n",
       "TOV%      0.843812  0.630653 -0.882644  0.300832 -0.792985 -0.043592   \n",
       "ORB%      1.000000  0.792811 -0.802304  0.469283 -0.785020  0.110555   \n",
       "FT/FGA    0.792811  1.000000 -0.438142  0.655958 -0.547435  0.371746   \n",
       "ORtg     -0.802304 -0.438142  1.000000 -0.241031  0.754686  0.082068   \n",
       "REAL      0.469283  0.655958 -0.241031  1.000000  0.057053  0.918942   \n",
       "FAKE     -0.785020 -0.547435  0.754686  0.057053  1.000000  0.446180   \n",
       "TOTAL     0.110555  0.371746  0.082068  0.918942  0.446180  1.000000   \n",
       "REAL_PCT  0.948735  0.854962 -0.783136  0.564762 -0.785223  0.196063   \n",
       "FAKE_PCT -0.948735 -0.854962  0.783136 -0.564762  0.785223 -0.196063   \n",
       "\n",
       "          REAL_PCT  FAKE_PCT  \n",
       "Rk        0.935911 -0.935911  \n",
       "Age       0.734939 -0.734939  \n",
       "Wt        0.417029 -0.417029  \n",
       "G         0.444323 -0.444323  \n",
       "MP        0.618067 -0.618067  \n",
       "FG       -0.960465  0.960465  \n",
       "FGA      -0.984748  0.984748  \n",
       "3P       -0.986457  0.986457  \n",
       "3PA      -0.987891  0.987891  \n",
       "FT        0.657317 -0.657317  \n",
       "FTA       0.745435 -0.745435  \n",
       "ORB       0.857875 -0.857875  \n",
       "DRB      -0.981048  0.981048  \n",
       "TRB      -0.916876  0.916876  \n",
       "AST      -0.932576  0.932576  \n",
       "STL      -0.318699  0.318699  \n",
       "BLK      -0.010177  0.010177  \n",
       "TOV       0.466018 -0.466018  \n",
       "PF        0.657808 -0.657808  \n",
       "PTS      -0.947109  0.947109  \n",
       "FG%      -0.491723  0.491723  \n",
       "3P%      -0.091664  0.091664  \n",
       "FT%      -0.769653  0.769653  \n",
       "Pace     -0.979372  0.979372  \n",
       "eFG%     -0.910238  0.910238  \n",
       "TOV%      0.851440 -0.851440  \n",
       "ORB%      0.948735 -0.948735  \n",
       "FT/FGA    0.854962 -0.854962  \n",
       "ORtg     -0.783136  0.783136  \n",
       "REAL      0.564762 -0.564762  \n",
       "FAKE     -0.785223  0.785223  \n",
       "TOTAL     0.196063 -0.196063  \n",
       "REAL_PCT  1.000000 -1.000000  \n",
       "FAKE_PCT -1.000000  1.000000  \n",
       "\n",
       "[34 rows x 34 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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