FinalProject_group053_S.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we first compare the average win-rate of home teams in the 2019-2020 season before the NBA Bubble to that of home teams in the Bubble. After performing a t-test on these averages, we found the home team average win-rate outside of the Bubble was higher than the home teams’ average win-rate in the Bubble, however this difference was not statistically significant. To see what contributes to this difference, we compared the statistics highest correlated with winning. We found that there is a significant difference in the foul calls and rebounds of home teams in the NBA Bubble compared to before the NBA Bubble."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Dylan Lee\n",
    "- Ryan Shamoon\n",
    "- Alejandro Ramirez\n",
    "- Kamen Redfield\n",
    "- Justin Miller"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did the NBA bubble preserve the “home-court advantage” (a higher win percentage when playing at home) that teams regularly experienced during the 2019-2020 regular season? If the “home-court advantage” changed, what changes in game statistics reflect that change?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The National Basketball Association (NBA), is a professional men’s basketball league consisting of thirty different teams across the United States and Canada. Throughout the NBA regular season, teams play a total of eighty-two games, and the top sixteen teams with the best record from those regular season games advance to the playoffs. During those playoffs, those sixteen teams will compete against each other in an elimination bracket until one champion team remains (1). During the NBA regular season of 2020 however, unforeseen circumstances surrounding the COVID-19 pandemic put an abrupt halt towards the completion of the regular season. However, after a momentary pause towards the NBA regular season, current commissioner, Adam Silver, implemented a new plan to finish off the regular season and the playoffs in what is called the ‘NBA Bubble.’ This NBA Bubble consisted of having the top twenty-two teams from the regular season physically relocating to the Walt Disney World Resort in Orlando, Florida in order to adhere safe social distancing guidelines to finish the regular season and the playoffs there. Within the NBA Bubble, basketball players resided in various hotel rooms throughout the resort, practiced amongst their teams, and competed against each other all within the Walt Disney World Resort for an entire duration of four months (July - October), depending on how well the team performed in the regular season and playoffs (2). Given these unique and contrasting circumstances of the NBA Bubble compared to NBA seasons prior to COVID-19 and quarantine restrictions, our group was interested in using data science to determine whether or not the NBA Bubble affected player performance.\n",
    " \n",
    "Many people speculated that the NBA Bubble would be unable to fully replicate the environment of a live NBA game. Generally in sports, when two teams play, the team that is playing on their home field/court tends to perform better and win more often. The advantage of playing at home for a team often occurs from the psychological confidence from playing in front of supportive fans, the comfortability the home team has playing in familiar weather and a geographical location, and the lack of fatigue from not having to physically travel. Of course, these advantages serve to be disadvantages for the visiting team which has to perform in front of fans opposing them in a completely new location after days of traveling. As a result, it’s generally accepted by widespread sports analysts and fans that the team playing at home has the advantage (3). As stated by Bleacher Report sports writer Kevin Bellhumeur, a team’s home court advantage is typically measured by the percentage of the games a given team wins at home compared to the percentage of games that team wins away from home (4). This difference in percentages is how one can quantify home court advantage. \n",
    " \n",
    "A number of studies have been done to confirm this home team’s advantage and its effects. One study by Jon Bois indicates that teams in the NBA, NFL, MLB, and NHL would all experience a 5-10% increase in the number of games they win if they were able to play all of their games at home (7). This reflects the prominent home field/court advantage across a number of sports. In the NBA, this effect has been observed. One study found that the home team, that does not experience travel-related issues like jetlag that is experienced by the away team, had a higher shooting accuracy and got more rebounds than the away team (6). Additionally, another analysis found that the home teams generally receive more foul calls from the referees (8). They also tend to make 2-pointers and free throws more often (8). Home-court advantage is an idea that is clearly established and measured in the NBA.\n",
    " \n",
    "In regards to the advent of the NBA Bubble, the participating teams are forced to sequester themselves in the Walt Disney World Resort until they’re eliminated, which is a minimum of at least two months. Because of social distance precautions, teams won’t be playing in their home state/city with the absence of supporting fans. The absence of these supporters may take away the adrenaline rush players experience during a home game in front of cheering fans. Additionally, these players are physically isolated from home for a prolonged period of time, which could have adverse psychological effects as well, especially during games in a resort unfamiliar to them personally. Furthermore, players are being asked to play during turbulent circumstances, such as the ongoing fear of the COVID-19 pandemic, the social cause to promote the Black Lives Matter movement, and the upcoming presidential election, which could also detract away from basketball as a priority and hamper their performances as well. As a result, we could see how many of the advantages coming from playing at home aren’t relevant in the NBA Bubble. The NBA ultimately tried to replicate the feeling of having home court advantage through visual videos of fans, simulated audio of cheering fans, allowing home teams to warm up longer, and team decor throughout the ‘stadium’ in the resort (5). Given this, our group was curious to see how these new circumstances and attempts to replicate the feeling of playing at home in the NBA Bubble would affect overall performance across teams this season (4). \n",
    "The question was incredibly intriguing to our group because as sports fans, we are all familiar with the idea that the home team has an advantage during games they play in front of their supporters. However, it is extremely hard to quantify just how strong this “home-court advantage” is. The movement of the NBA playoffs to the bubble allows one to analyze the source of this “home-court advantage” given the lack of the significant presence that is live, in-person fans. Given the insane success of some sports teams at home, it's hard to believe this effect could ever be artificially replicated (4, 5). This is what led us to our hypothesis that simply the effect of the loud crowds that cause intense motivation and pressure in the NBA cannot be replicated by fake audio and online video streams in the crowd. If our hypothesis is proven wrong however, it could encourage more sports leagues to take a similar approach to the NBA with a bubble and artificial crowd interaction. If our hypothesis is correct, leagues will need to rethink their approach to replicating the feeling of playing at home for the players.\n",
    "References (include links):\n",
    "\n",
    "1. https://www.nba.com/news/faq\n",
    "2. https://www.espn.com/nba/story/_/id/30055011/everything-happened-nba-bubble\n",
    "3. http://psychology.iresearchnet.com/social-psychology/control/home-field-advantage/#:~:text=The%20home%2Dfield%20advantage%20refers,half%20of%20total%20games%20played.\n",
    "4. https://bleacherreport.com/articles/1520496-how-important-is-home-court-advantage-in-the-nba\n",
    "5. https://clutchpoints.com/differences-between-home-vs-away-teams-in-nba-bubble-trying-to-create-home-court-advantage/\n",
    "6. https://www.cnbc.com/2020/12/11/studyhome-court-advantage-is-real-heres-how-it-could-affect-your-focus.html\n",
    "7. https://www.sbnation.com/2011/1/19/1940438/home-field-advantage-sports-stats-data\n",
    "8. https://insidescience.org/news/subtle-biases-influence-home-court-advantage\n",
    " \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Bubble, the NBA was unable to replicate the home-court advantage that teams experienced during the 2019-2020 regular season pre-bubble because of the standard season’s live, in-person fans and the physical presence in one's home city/state. This led to the home teams in the Bubble generally performing worse than they did with live fans before the bubble. \n",
    "\n",
    "We predict this will be the case because we believe that the replication of a crowd by use of crowd noise, fan video feed, and varying warm up times in the Bubble are insufficient in simulating the sounds and faces of live, in-person fans. The environment and psychological effects of live home games are not replicated in the NBA Bubble. Thus, the home-court advantage of a live home game will not be replicated.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset: NBA Games (23,195 observations) <br>\n",
    "Link: https://www.kaggle.com/nathanlauga/nba-games?select=games.csv <br>\n",
    "Description: Dataset that contains basic game data for each NBA game since the 2003-2004 season.\n",
    "\n",
    "Dataset: Kaggle Team Data (30 observations) <br>\n",
    "Link: https://www.kaggle.com/nathanlauga/nba-games?select=teams.csv <br>\n",
    "Description: Dataset contains information on each team in the NBA, such as name, location, etc. Only purpose of this dataset is to match the Team ID's with that in the NBA Games Dataset.\n",
    "\n",
    "Dataset: Season 2019 Box Scores (1942 observations) <br>\n",
    "Link: https://www.nba.com/stats/teams/boxscores/?Season=2019-20&SeasonType=Regular%20Season&DateFrom=09%2F11%2F2019&DateTo=04%2F15%2F2020 <br>\n",
    "Description: Dataset with box scores from every game in the 2019 regular season not including any games played in the bubble. There are two observations for each game with each observation containing stats for one of the teams in the game.\n",
    "\n",
    "Dataset: Bubble Regular Season Box Scores (176 observations) <br>\n",
    "Link: https://www.nba.com/stats/teams/boxscores/?Season=2019-20&SeasonType=Regular%20Season&DateFrom=05%2F13%2F2020&DateTo=11%2F11%2F2020 <br>\n",
    "Description: Dataset with box scores from every regular season game in the bubble. There are two observations for each game with each observation containing stats for one of the teams in the game.\n",
    "\n",
    "Dataset: Bubble Season Playoff Box Scores (166 observations) <br>\n",
    "Link: https://www.nba.com/stats/teams/boxscores/?Season=2019-20&SeasonType=Playoffs <br>\n",
    "Description: Dataset with box scores from every playoff game in the bubble. There are two observations for each game with each observation containing stats for one of the teams in the game.\n",
    "\n",
    "Plan: <br>\n",
    "We plan to use the NBA Games dataset to first check whether home court advantage has existed historically in the NBA. then we will combine both of the Bubble Box Score datasets together and compare them with the Season 2019 Box Scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import stats\n",
    "from scipy.stats import gaussian_kde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Main CSVs\n",
    "nba_games = pd.read_csv('games.csv') #regular season games since 2014 with only cursory stats\n",
    "kaggle_team_data = pd.read_csv('teams.csv') #team data to be paired with regular_season \n",
    "\n",
    "# These next three csv's are sorted by date already.\n",
    "\n",
    "# team box scores of every 2019 season game. 2 box scores per game (one each team).\n",
    "season_2019_box = pd.read_csv('2019 Season Box Scores.csv') \n",
    "\n",
    "#team box scores of every bubble regular season game. 2 box scores per game (one each team)\n",
    "bubble_season_box = pd.read_csv('RS Bubble Box Scores.csv') \n",
    "\n",
    "#team box scores of every bubble playoff game. 2 box scores per game (one each team) \n",
    "bubble_playoff_box = pd.read_csv('Playoff Bubble Box Scores.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we clean our NBA historical DataFrame by dropping columns of data of stats that aren't too useful in determining home court advantage, and also unnecessary columns, such as game date and game status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unnecessary columns\n",
    "nba_games = nba_games.drop(labels=['GAME_DATE_EST', 'AST_home', 'REB_home', 'GAME_STATUS_TEXT', 'AST_away', 'REB_away',\n",
    "                                   'TEAM_ID_home', 'TEAM_ID_away'], axis = 1)\n",
    "\n",
    "\n",
    "# Replace home and away team IDs with the names of the teams using the kaggle_team_data DataFrame\n",
    "def change_id(int):\n",
    "    list_of_IDs = list(kaggle_team_data.get('TEAM_ID'))\n",
    "    for index, ID in enumerate(list_of_IDs):\n",
    "        if ID == int:\n",
    "            return kaggle_team_data.iloc[index]['CITY'] + ' ' + kaggle_team_data.iloc[index]['NICKNAME']\n",
    "\n",
    "# Apply change_id function to the Home_TEAM_ID and VISITOR_TEAM_ID columns\n",
    "nba_games['HOME_TEAM_ID'] = nba_games['HOME_TEAM_ID'].apply(change_id)\n",
    "nba_games['VISITOR_TEAM_ID'] = nba_games['VISITOR_TEAM_ID'].apply(change_id)\n",
    "\n",
    "#Rename columns to Home_Team and Away_Team\n",
    "nba_games = nba_games.rename(columns = {'HOME_TEAM_ID': 'Home_Team', 'VISITOR_TEAM_ID':'Away_Team'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DataFrame nba_games holds overview data for each game played since the 2003-2004 NBA season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>Home_Team</th>\n",
       "      <th>Away_Team</th>\n",
       "      <th>SEASON</th>\n",
       "      <th>PTS_home</th>\n",
       "      <th>FG_PCT_home</th>\n",
       "      <th>FT_PCT_home</th>\n",
       "      <th>FG3_PCT_home</th>\n",
       "      <th>PTS_away</th>\n",
       "      <th>FG_PCT_away</th>\n",
       "      <th>FT_PCT_away</th>\n",
       "      <th>FG3_PCT_away</th>\n",
       "      <th>HOME_TEAM_WINS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21900895</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>2019</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.229</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21900896</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>2019</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.310</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21900897</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>2019</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.542</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.488</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21900898</td>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>2019</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.500</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.263</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21900899</td>\n",
       "      <td>Sacramento Kings</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>2019</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.257</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.429</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    GAME_ID               Home_Team           Away_Team  SEASON  PTS_home  \\\n",
       "0  21900895       Charlotte Hornets     Milwaukee Bucks    2019      85.0   \n",
       "1  21900896  Minnesota Timberwolves    Dallas Mavericks    2019      91.0   \n",
       "2  21900897    Los Angeles Clippers  Philadelphia 76ers    2019     136.0   \n",
       "3  21900898          Denver Nuggets     Toronto Raptors    2019     133.0   \n",
       "4  21900899        Sacramento Kings     Detroit Pistons    2019     106.0   \n",
       "\n",
       "   FG_PCT_home  FT_PCT_home  FG3_PCT_home  PTS_away  FG_PCT_away  FT_PCT_away  \\\n",
       "0        0.354        0.900         0.229      93.0        0.402        0.762   \n",
       "1        0.364        0.400         0.310     111.0        0.468        0.632   \n",
       "2        0.592        0.805         0.542     130.0        0.505        0.650   \n",
       "3        0.566        0.700         0.500     118.0        0.461        0.897   \n",
       "4        0.407        0.885         0.257     100.0        0.413        0.667   \n",
       "\n",
       "   FG3_PCT_away  HOME_TEAM_WINS  \n",
       "0         0.226               0  \n",
       "1         0.275               0  \n",
       "2         0.488               1  \n",
       "3         0.263               1  \n",
       "4         0.429               1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_games.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By calculating the mean of the column HOME_TEAM_WINS, we get the win percentage of the entire NBA at home since the 2003 season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59.233 % of the time the Home team wins in the NBA since 2003.\n"
     ]
    }
   ],
   "source": [
    "print(round(nba_games['HOME_TEAM_WINS'].mean()*100, 3), '% of the time the Home team wins in the NBA since 2003.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data shows that for the last 16 years in the NBA, playing at home gives a team a noticeable advantage over the Away team."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bubble_season_box, the bubble_playoff_box, and season_2019_box have the exact same layout, so data cleaning is performed on all three datasets at the same time. The season_2019_box DataFrame is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEAM</th>\n",
       "      <th>MATCH UP</th>\n",
       "      <th>GAME DATE</th>\n",
       "      <th>W/L</th>\n",
       "      <th>MIN</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3:00 PM</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>OREB</th>\n",
       "      <th>DREB</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>#ERROR!</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NYK</td>\n",
       "      <td>NYK vs. POR</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>W</td>\n",
       "      <td>240</td>\n",
       "      <td>117</td>\n",
       "      <td>47</td>\n",
       "      <td>98</td>\n",
       "      <td>48.0</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>64.3</td>\n",
       "      <td>9</td>\n",
       "      <td>46</td>\n",
       "      <td>55</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ORL</td>\n",
       "      <td>ORL @ WAS</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>W</td>\n",
       "      <td>240</td>\n",
       "      <td>122</td>\n",
       "      <td>45</td>\n",
       "      <td>95</td>\n",
       "      <td>47.4</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>74.1</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>52</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MIL</td>\n",
       "      <td>MIL vs. MIN</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>W</td>\n",
       "      <td>240</td>\n",
       "      <td>106</td>\n",
       "      <td>40</td>\n",
       "      <td>94</td>\n",
       "      <td>42.6</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>65.4</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POR</td>\n",
       "      <td>POR @ NYK</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>L</td>\n",
       "      <td>240</td>\n",
       "      <td>93</td>\n",
       "      <td>37</td>\n",
       "      <td>94</td>\n",
       "      <td>39.4</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10</td>\n",
       "      <td>37</td>\n",
       "      <td>47</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MIN</td>\n",
       "      <td>MIN @ MIL</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>L</td>\n",
       "      <td>240</td>\n",
       "      <td>104</td>\n",
       "      <td>34</td>\n",
       "      <td>91</td>\n",
       "      <td>37.4</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>48</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  TEAM     MATCH UP   GAME DATE W/L  MIN  PTS  FGM  FGA   FG%  3:00 PM  ...  \\\n",
       "0  NYK  NYK vs. POR  01/01/2020   W  240  117   47   98  48.0       14  ...   \n",
       "1  ORL    ORL @ WAS  01/01/2020   W  240  122   45   95  47.4       12  ...   \n",
       "2  MIL  MIL vs. MIN  01/01/2020   W  240  106   40   94  42.6        9  ...   \n",
       "3  POR    POR @ NYK  01/01/2020   L  240   93   37   94  39.4       10  ...   \n",
       "4  MIN    MIN @ MIL  01/01/2020   L  240  104   34   91  37.4       15  ...   \n",
       "\n",
       "    FT%  OREB  DREB  REB  AST  STL  BLK  TOV  PF  #ERROR!  \n",
       "0  64.3     9    46   55   29    3    6    7  14       24  \n",
       "1  74.1    11    41   52   25   11    6    9  21       21  \n",
       "2  65.4    12    48   60   24    4   12   17  22        2  \n",
       "3  60.0    10    37   47   17    4    5    9  11      -24  \n",
       "4  75.0     6    42   48   18    7    8   12  25       -2  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season_2019_box.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next two cells, we drop unnecessary columns in all three datasets, rename the '3 pointers made' column, and create a new column displaying whether or not the team is playing at Home using the MATCH UP column information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Drops columns for minutes, all types of rebounds, assists, steals, blocks, turnovers, and fouls.\n",
    "bubble_season_box = bubble_season_box.drop(labels=['MIN', 'OREB', 'DREB', 'AST', 'STL', 'BLK'], axis=1)\n",
    "season_2019_box = season_2019_box.drop(labels=['MIN', 'OREB', 'DREB', 'AST', 'STL', 'BLK'], axis=1)\n",
    "bubble_playoff_box = bubble_playoff_box.drop(labels=['MIN', 'OREB', 'DREB', 'AST', 'STL', 'BLK'], axis=1)\n",
    "\n",
    "# One of the columns for 3 Pointers Made was accidently altered to 3:00 P in Excel, so we rename that for all the 3 dataframes.\n",
    "bubble_season_box = bubble_season_box.rename(columns={'3:00 PM': '3PM'})\n",
    "season_2019_box = season_2019_box.rename(columns={'3:00 PM': '3PM'})\n",
    "bubble_playoff_box = bubble_playoff_box.rename(columns={'3:00 PM': '3PM'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_home(match_up):\n",
    "    \"\"\"\n",
    "    Function to be used to add a column about whether a team is Home or Away to main 3 CSV's.\n",
    "    \"\"\"\n",
    "    if '@' in match_up:\n",
    "        return False\n",
    "    else:\n",
    "        return True\n",
    "    \n",
    "# Adds a column to each DF called Home where values are either True or False\n",
    "bubble_season_box = bubble_season_box.assign(Home = bubble_season_box['MATCH UP'].apply(set_home))\n",
    "season_2019_box = season_2019_box.assign(Home = season_2019_box['MATCH UP'].apply(set_home))\n",
    "bubble_playoff_box = bubble_playoff_box.assign(Home = bubble_playoff_box['MATCH UP'].apply(set_home))\n",
    "\n",
    "# Rename the plus minus columns to get parity across DataFrames.\n",
    "season_2019_box = season_2019_box.rename(columns={'#ERROR!': '+/-'})\n",
    "bubble_playoff_box = bubble_playoff_box.rename(columns={'+/-\\'': '+/-'})\n",
    "bubble_season_box = bubble_season_box.rename(columns={'+/-\\'': '+/-'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's important for us not to use any data from teams that didn't compete in the bubble. Therefore, we throw out any games from the earlier 2019 season that include a non-bubble team. We don't the keep the games where non-bubble teams played against teams that went to the bubble because it could skew our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of teams in bubble\n",
    "bubble_teams = list(bubble_season_box['TEAM'].unique())\n",
    "\n",
    "def only_bubble_teams(match_up):\n",
    "    \"\"\"\n",
    "    To be used on 2019 season to throw away any teams that didn't compete in the bubble.\n",
    "    \"\"\"\n",
    "    if match_up[:3] in bubble_teams and match_up[-3:] in bubble_teams:\n",
    "        return match_up\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "season_2019_box = season_2019_box.assign(MATCH_UP = season_2019_box['MATCH UP'].apply(only_bubble_teams))\n",
    "\n",
    "season_2019_box = season_2019_box[season_2019_box.get('MATCH_UP') != 0]\n",
    "\n",
    "season_2019_box = season_2019_box.drop(columns = ['MATCH_UP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .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>TEAM</th>\n",
       "      <th>MATCH UP</th>\n",
       "      <th>GAME DATE</th>\n",
       "      <th>W/L</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3PM</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FTM</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT%</th>\n",
       "      <th>REB</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>+/-</th>\n",
       "      <th>Home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ORL</td>\n",
       "      <td>ORL @ WAS</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>W</td>\n",
       "      <td>122</td>\n",
       "      <td>45</td>\n",
       "      <td>95</td>\n",
       "      <td>47.4</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>37.5</td>\n",
       "      <td>20</td>\n",
       "      <td>27</td>\n",
       "      <td>74.1</td>\n",
       "      <td>52</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LAL</td>\n",
       "      <td>LAL vs. PHX</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>W</td>\n",
       "      <td>117</td>\n",
       "      <td>43</td>\n",
       "      <td>90</td>\n",
       "      <td>47.8</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24</td>\n",
       "      <td>34</td>\n",
       "      <td>70.6</td>\n",
       "      <td>45</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WAS</td>\n",
       "      <td>WAS vs. ORL</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>L</td>\n",
       "      <td>101</td>\n",
       "      <td>33</td>\n",
       "      <td>87</td>\n",
       "      <td>37.9</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>29.0</td>\n",
       "      <td>26</td>\n",
       "      <td>31</td>\n",
       "      <td>83.9</td>\n",
       "      <td>46</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>-21</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PHX</td>\n",
       "      <td>PHX @ LAL</td>\n",
       "      <td>01/01/2020</td>\n",
       "      <td>L</td>\n",
       "      <td>107</td>\n",
       "      <td>42</td>\n",
       "      <td>86</td>\n",
       "      <td>48.8</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>26.1</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>73.9</td>\n",
       "      <td>47</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>-10</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>MEM</td>\n",
       "      <td>MEM @ SAC</td>\n",
       "      <td>01/02/2020</td>\n",
       "      <td>L</td>\n",
       "      <td>123</td>\n",
       "      <td>45</td>\n",
       "      <td>93</td>\n",
       "      <td>48.4</td>\n",
       "      <td>13</td>\n",
       "      <td>34</td>\n",
       "      <td>38.2</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>95.2</td>\n",
       "      <td>42</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>-5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TEAM     MATCH UP   GAME DATE W/L  PTS  FGM  FGA   FG%  3PM  3PA   3P%  \\\n",
       "1   ORL    ORL @ WAS  01/01/2020   W  122   45   95  47.4   12   32  37.5   \n",
       "5   LAL  LAL vs. PHX  01/01/2020   W  117   43   90  47.8    7   28  25.0   \n",
       "6   WAS  WAS vs. ORL  01/01/2020   L  101   33   87  37.9    9   31  29.0   \n",
       "7   PHX    PHX @ LAL  01/01/2020   L  107   42   86  48.8    6   23  26.1   \n",
       "11  MEM    MEM @ SAC  01/02/2020   L  123   45   93  48.4   13   34  38.2   \n",
       "\n",
       "    FTM  FTA   FT%  REB  TOV  PF  +/-   Home  \n",
       "1    20   27  74.1   52    9  21   21  False  \n",
       "5    24   34  70.6   45   13  17   10   True  \n",
       "6    26   31  83.9   46   13  23  -21   True  \n",
       "7    17   23  73.9   47   15  28  -10  False  \n",
       "11   20   21  95.2   42   17  25   -5  False  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View of the season_2019_box DataFrame after Data Cleaning.\n",
    "season_2019_box.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we merge the bubble_season_box dataset with the bubble_playoff_box dataset to get a combined DataFrame with the box scores of all the games that took place in the bubble. By default, this DataFrame is sorted by game date. This will be the DataFrame that we use the most along with the season_2019_box DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\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>TEAM</th>\n",
       "      <th>MATCH UP</th>\n",
       "      <th>GAME DATE</th>\n",
       "      <th>W/L</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3PM</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>FTM</th>\n",
       "      <th>FTA</th>\n",
       "      <th>FT%</th>\n",
       "      <th>REB</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>+/-</th>\n",
       "      <th>Home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UTA</td>\n",
       "      <td>UTA @ NOP</td>\n",
       "      <td>07/30/2020</td>\n",
       "      <td>W</td>\n",
       "      <td>106</td>\n",
       "      <td>37</td>\n",
       "      <td>84</td>\n",
       "      <td>44.0</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>23.5</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>85.7</td>\n",
       "      <td>43</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NOP</td>\n",
       "      <td>NOP vs. UTA</td>\n",
       "      <td>07/30/2020</td>\n",
       "      <td>L</td>\n",
       "      <td>104</td>\n",
       "      <td>40</td>\n",
       "      <td>94</td>\n",
       "      <td>42.6</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>35.5</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>72.2</td>\n",
       "      <td>45</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>-2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LAL</td>\n",
       "      <td>LAL vs. LAC</td>\n",
       "      <td>07/30/2020</td>\n",
       "      <td>W</td>\n",
       "      <td>103</td>\n",
       "      <td>32</td>\n",
       "      <td>82</td>\n",
       "      <td>39.0</td>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>30.6</td>\n",
       "      <td>28</td>\n",
       "      <td>37</td>\n",
       "      <td>75.7</td>\n",
       "      <td>45</td>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LAC</td>\n",
       "      <td>LAC @ LAL</td>\n",
       "      <td>07/30/2020</td>\n",
       "      <td>L</td>\n",
       "      <td>101</td>\n",
       "      <td>32</td>\n",
       "      <td>74</td>\n",
       "      <td>43.2</td>\n",
       "      <td>16</td>\n",
       "      <td>36</td>\n",
       "      <td>44.4</td>\n",
       "      <td>21</td>\n",
       "      <td>28</td>\n",
       "      <td>75.0</td>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>-2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WAS</td>\n",
       "      <td>WAS vs. PHX</td>\n",
       "      <td>07/31/2020</td>\n",
       "      <td>L</td>\n",
       "      <td>112</td>\n",
       "      <td>42</td>\n",
       "      <td>91</td>\n",
       "      <td>46.2</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>36.4</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>87.0</td>\n",
       "      <td>36</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>-13</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>LAL</td>\n",
       "      <td>LAL @ MIA</td>\n",
       "      <td>10/06/2020</td>\n",
       "      <td>W</td>\n",
       "      <td>102</td>\n",
       "      <td>35</td>\n",
       "      <td>79</td>\n",
       "      <td>44.3</td>\n",
       "      <td>14</td>\n",
       "      <td>39</td>\n",
       "      <td>35.9</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>85.7</td>\n",
       "      <td>42</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>MIA</td>\n",
       "      <td>MIA @ LAL</td>\n",
       "      <td>10/09/2020</td>\n",
       "      <td>W</td>\n",
       "      <td>111</td>\n",
       "      <td>38</td>\n",
       "      <td>83</td>\n",
       "      <td>45.8</td>\n",
       "      <td>14</td>\n",
       "      <td>33</td>\n",
       "      <td>42.4</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>95.5</td>\n",
       "      <td>35</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>LAL</td>\n",
       "      <td>LAL vs. MIA</td>\n",
       "      <td>10/09/2020</td>\n",
       "      <td>L</td>\n",
       "      <td>108</td>\n",
       "      <td>38</td>\n",
       "      <td>82</td>\n",
       "      <td>46.3</td>\n",
       "      <td>14</td>\n",
       "      <td>38</td>\n",
       "      <td>36.8</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>85.7</td>\n",
       "      <td>41</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>-3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>MIA</td>\n",
       "      <td>MIA vs. LAL</td>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>L</td>\n",
       "      <td>93</td>\n",
       "      <td>35</td>\n",
       "      <td>79</td>\n",
       "      <td>44.3</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>35.7</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>59.1</td>\n",
       "      <td>41</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>-13</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>LAL</td>\n",
       "      <td>LAL @ MIA</td>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>W</td>\n",
       "      <td>106</td>\n",
       "      <td>43</td>\n",
       "      <td>89</td>\n",
       "      <td>48.3</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "      <td>31.4</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>64.3</td>\n",
       "      <td>46</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    TEAM     MATCH UP   GAME DATE W/L  PTS  FGM  FGA   FG%  3PM  3PA   3P%  \\\n",
       "0    UTA    UTA @ NOP  07/30/2020   W  106   37   84  44.0    8   34  23.5   \n",
       "1    NOP  NOP vs. UTA  07/30/2020   L  104   40   94  42.6   11   31  35.5   \n",
       "2    LAL  LAL vs. LAC  07/30/2020   W  103   32   82  39.0   11   36  30.6   \n",
       "3    LAC    LAC @ LAL  07/30/2020   L  101   32   74  43.2   16   36  44.4   \n",
       "4    WAS  WAS vs. PHX  07/31/2020   L  112   42   91  46.2    8   22  36.4   \n",
       "..   ...          ...         ...  ..  ...  ...  ...   ...  ...  ...   ...   \n",
       "337  LAL    LAL @ MIA  10/06/2020   W  102   35   79  44.3   14   39  35.9   \n",
       "338  MIA    MIA @ LAL  10/09/2020   W  111   38   83  45.8   14   33  42.4   \n",
       "339  LAL  LAL vs. MIA  10/09/2020   L  108   38   82  46.3   14   38  36.8   \n",
       "340  MIA  MIA vs. LAL  10/11/2020   L   93   35   79  44.3   10   28  35.7   \n",
       "341  LAL    LAL @ MIA  10/11/2020   W  106   43   89  48.3   11   35  31.4   \n",
       "\n",
       "     FTM  FTA   FT%  REB  TOV  PF  +/-   Home  \n",
       "0     24   28  85.7   43   20  23    2  False  \n",
       "1     13   18  72.2   45   21  25   -2   True  \n",
       "2     28   37  75.7   45   16  27    2   True  \n",
       "3     21   28  75.0   36   22  30   -2  False  \n",
       "4     20   23  87.0   36   18  26  -13   True  \n",
       "..   ...  ...   ...  ...  ...  ..  ...    ...  \n",
       "337   18   21  85.7   42   15  14    6  False  \n",
       "338   21   22  95.5   35   13  19    3  False  \n",
       "339   18   21  85.7   41   15  21   -3   True  \n",
       "340   13   22  59.1   41   15  18  -13   True  \n",
       "341    9   14  64.3   46   14  22   13  False  \n",
       "\n",
       "[342 rows x 19 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_bubble_box = bubble_season_box.merge(bubble_playoff_box, how='outer')\n",
    "combined_bubble_box"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following cells, we will be determining and analyzing the presence of homecourt advantage, and also whether or not any effect of homecourt advantage differed between the NBA regular season prior to COVID-19 and the NBA bubble which was implemented shortly after to accomidate social distancing restrictions. To do so, we will first find the win percentages of each team, separated into two categories: the win percentage of each team in home games, and the win percentage of teams in away games. However, it is important to note that the only teams that are included in this data analysis are the ones who would later go on to participate in the NBA bubble season after the onset of COVID-19."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#For each team, find the number of wins and losses for both home and away games during the 2019-2020 regular season (PRE-Covid19)\n",
    "regular_season_record = season_2019_box.groupby(['TEAM', 'Home', 'W/L']).count()\n",
    "#rename the column to be more descriptive of the number of wins/losses per team\n",
    "regular_season_record = regular_season_record[['MATCH UP']].rename(columns={'MATCH UP': 'Count'})\n",
    "#reset the index of the dataframe for to aid in later analysis\n",
    "regular_season_record = regular_season_record.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell displays a dataframe, which shows the number of wins and losses of each team, separated by home and away games during the 2019 - 2020 NBA regular season, prior to restrictions implaced due to COVID-19."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>TEAM</th>\n",
       "      <th>Home</th>\n",
       "      <th>W/L</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BKN</td>\n",
       "      <td>False</td>\n",
       "      <td>L</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BKN</td>\n",
       "      <td>False</td>\n",
       "      <td>W</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BKN</td>\n",
       "      <td>True</td>\n",
       "      <td>L</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BKN</td>\n",
       "      <td>True</td>\n",
       "      <td>W</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BOS</td>\n",
       "      <td>False</td>\n",
       "      <td>L</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>UTA</td>\n",
       "      <td>True</td>\n",
       "      <td>W</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>WAS</td>\n",
       "      <td>False</td>\n",
       "      <td>L</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>WAS</td>\n",
       "      <td>False</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>WAS</td>\n",
       "      <td>True</td>\n",
       "      <td>L</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>WAS</td>\n",
       "      <td>True</td>\n",
       "      <td>W</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TEAM   Home W/L  Count\n",
       "0   BKN  False   L     16\n",
       "1   BKN  False   W      5\n",
       "2   BKN   True   L     11\n",
       "3   BKN   True   W     10\n",
       "4   BOS  False   L     12\n",
       "..  ...    ...  ..    ...\n",
       "83  UTA   True   W     16\n",
       "84  WAS  False   L     19\n",
       "85  WAS  False   W      2\n",
       "86  WAS   True   L     10\n",
       "87  WAS   True   W      8\n",
       "\n",
       "[88 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the number of wins and losses for each team, separated by home and away games\n",
    "regular_season_record"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the number of wins and losses that each team has during the regular season to determine the win percentage of each team from their regular season games. These win percentages will still be separated by number of home and away games for each team in order to see the prevalence of homecourt advantage. Win percentage will be calculated by dividing the number of wins each team has by the total number of games that team played."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get total number of games played for each bubble team during the regular season\n",
    "total_regular_games = regular_season_record.groupby(['TEAM', 'Home']).sum().rename(columns = {'Count': 'Total Games'})\n",
    "total_regular_games = total_regular_games.reset_index().set_index('TEAM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell will calculate the win percentages of each team in both their home and away games, and display that data in a dataframe. To interpret the data within this dataframe, each row represents the win percentage of a certain team in either their home or away games. As an example, the first row corresponds to the win percentage of the Brooklyn Nets during their away games, which is 37.5%. The following cell shows their win percentage of all their home games, which is 56.25%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Home</th>\n",
       "      <th>Games_Won</th>\n",
       "      <th>Total_Games</th>\n",
       "      <th>Win_Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>0.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>0.476190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>0.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>True</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>0.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAL</th>\n",
       "      <td>False</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAL</th>\n",
       "      <td>True</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEN</th>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEN</th>\n",
       "      <td>True</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>0.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IND</th>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>0.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IND</th>\n",
       "      <td>True</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0.590909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAC</th>\n",
       "      <td>False</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAC</th>\n",
       "      <td>True</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAL</th>\n",
       "      <td>False</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>0.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAL</th>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEM</th>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEM</th>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>0.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIA</th>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIA</th>\n",
       "      <td>True</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIL</th>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0.590909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIL</th>\n",
       "      <td>True</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOP</th>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>0.304348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOP</th>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>0.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OKC</th>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OKC</th>\n",
       "      <td>True</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORL</th>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORL</th>\n",
       "      <td>True</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>0.391304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHI</th>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>0.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHI</th>\n",
       "      <td>True</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHX</th>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>0.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHX</th>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POR</th>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "      <td>0.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POR</th>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>0.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAC</th>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAC</th>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>0.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAS</th>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAS</th>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>True</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>0.652174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTA</th>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTA</th>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>25</td>\n",
       "      <td>0.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>0.095238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Home  Games_Won  Total_Games  Win_Percentage\n",
       "TEAM                                               \n",
       "BKN   False          5           21        0.238095\n",
       "BKN    True         10           21        0.476190\n",
       "BOS   False         10           22        0.454545\n",
       "BOS    True         14           22        0.636364\n",
       "DAL   False         15           24        0.625000\n",
       "DAL    True         13           26        0.500000\n",
       "DEN   False         12           24        0.500000\n",
       "DEN    True         20           25        0.800000\n",
       "HOU   False         12           23        0.521739\n",
       "HOU    True         16           25        0.640000\n",
       "IND   False          9           19        0.473684\n",
       "IND    True         13           22        0.590909\n",
       "LAC   False         15           25        0.600000\n",
       "LAC    True         18           25        0.720000\n",
       "LAL   False         21           27        0.777778\n",
       "LAL    True         16           24        0.666667\n",
       "MEM   False          8           24        0.333333\n",
       "MEM    True         11           24        0.458333\n",
       "MIA   False         10           25        0.400000\n",
       "MIA    True         18           21        0.857143\n",
       "MIL   False         13           22        0.590909\n",
       "MIL    True         18           21        0.857143\n",
       "NOP   False          7           23        0.304348\n",
       "NOP    True         10           26        0.384615\n",
       "OKC   False         12           23        0.521739\n",
       "OKC    True         13           26        0.500000\n",
       "ORL   False          8           24        0.333333\n",
       "ORL    True          9           23        0.391304\n",
       "PHI   False          4           25        0.160000\n",
       "PHI    True         18           20        0.900000\n",
       "PHX   False          8           22        0.363636\n",
       "PHX    True          8           28        0.285714\n",
       "POR   False         10           28        0.357143\n",
       "POR    True         10           24        0.416667\n",
       "SAC   False          8           25        0.320000\n",
       "SAC    True         11           24        0.458333\n",
       "SAS   False          8           25        0.320000\n",
       "SAS    True         12           23        0.521739\n",
       "TOR   False         12           21        0.571429\n",
       "TOR    True         15           23        0.652174\n",
       "UTA   False         10           23        0.434783\n",
       "UTA    True         16           25        0.640000\n",
       "WAS   False          2           21        0.095238\n",
       "WAS    True          8           18        0.444444"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#keep only the number of wins\n",
    "regular_season_wins = regular_season_record[regular_season_record['W/L'] == 'W'].set_index('TEAM')\n",
    "#add the total number of games played\n",
    "regular_season_wins = regular_season_wins.assign(Total_Games = total_regular_games.get('Total Games'))\n",
    "#determine the percentage of games won\n",
    "regular_season_wins = regular_season_wins.assign(Win_Percentage = regular_season_wins.get('Count') / regular_season_wins.get('Total_Games'))\n",
    "#drop any irrelevant columns from the dataframe\n",
    "regular_season_wins = regular_season_wins.drop(columns = ['W/L'])\n",
    "#rename any columns for easier clarity\n",
    "regular_season_win_percentage = regular_season_wins.rename(columns = {'Count': 'Games_Won'})\n",
    "#display the dataframe\n",
    "regular_season_win_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will repeat this process for NBA games played in the bubble, determining the win percentages of the same teams seen in the above dataaframe in their home and away games during the NBA bubble season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For each team, find the number of wins and losses for both home and away games during the NBA bubble\n",
    "bubble_season = combined_bubble_box.groupby(['TEAM', 'Home', 'W/L']).count()\n",
    "#rename the columns and set the dataframe's index to the team name\n",
    "bubble_season = bubble_season[['MATCH UP']].rename(columns={'MATCH UP': 'Count'}).reset_index().set_index('TEAM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell prints a dataframe showing the number of wins and losses each team has during the NBA bubble, separated again by home and away games to determine examine any potential indicators of homecourt advantage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Home</th>\n",
       "      <th>W/L</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>False</td>\n",
       "      <td>L</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>False</td>\n",
       "      <td>W</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>True</td>\n",
       "      <td>L</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>True</td>\n",
       "      <td>W</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>False</td>\n",
       "      <td>L</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTA</th>\n",
       "      <td>True</td>\n",
       "      <td>L</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTA</th>\n",
       "      <td>True</td>\n",
       "      <td>W</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>False</td>\n",
       "      <td>L</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>False</td>\n",
       "      <td>W</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>True</td>\n",
       "      <td>L</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Home W/L  Count\n",
       "TEAM                  \n",
       "BKN   False   L      3\n",
       "BKN   False   W      3\n",
       "BKN    True   L      4\n",
       "BKN    True   W      2\n",
       "BOS   False   L      4\n",
       "...     ...  ..    ...\n",
       "UTA    True   L      3\n",
       "UTA    True   W      4\n",
       "WAS   False   L      3\n",
       "WAS   False   W      1\n",
       "WAS    True   L      4\n",
       "\n",
       "[82 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bubble_season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we did earlier for the regular season, we will calculate the win percentage of each NBA bubble team separated by home and away games. The total number of home and away games each team played in the bubble will be calculated which is needed to determine win percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the total number of games each team played in the NBA bubble\n",
    "total_bubble_games = bubble_season.groupby(['TEAM', 'Home']).sum().rename(columns = {'Count': 'Total Games'})\n",
    "#reset the index to be each team in the bubble\n",
    "total_bubble_games = total_bubble_games.reset_index().set_index('TEAM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following dataframe displayed will show the number of home and away wins of each team during the NBA bubble. It's important to note that there are missing rows, since some teams didn't experience any wins or losses for either all of their home or away games. This will be addressed in the following cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
       "        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>TEAM</th>\n",
       "      <th>Home</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BKN</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BKN</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BOS</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BOS</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DAL</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DAL</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DEN</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DEN</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>HOU</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HOU</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IND</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IND</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LAC</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LAC</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LAL</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LAL</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MEM</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MIA</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MIA</td>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MIL</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MIL</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NOP</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>OKC</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>OKC</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ORL</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ORL</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PHI</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PHI</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PHX</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PHX</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>POR</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>POR</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SAC</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>SAC</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>SAS</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>SAS</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>TOR</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>TOR</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>UTA</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>UTA</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>WAS</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TEAM   Home  Count\n",
       "0   BKN  False      3\n",
       "1   BKN   True      2\n",
       "2   BOS  False      9\n",
       "3   BOS   True      6\n",
       "4   DAL  False      3\n",
       "5   DAL   True      2\n",
       "6   DEN  False      6\n",
       "7   DEN   True      6\n",
       "8   HOU  False      3\n",
       "9   HOU   True      6\n",
       "10  IND  False      2\n",
       "11  IND   True      4\n",
       "12  LAC  False      7\n",
       "13  LAC   True      5\n",
       "14  LAL  False      8\n",
       "15  LAL   True     11\n",
       "16  MEM   True      2\n",
       "17  MIA  False      9\n",
       "18  MIA   True      8\n",
       "19  MIL  False      4\n",
       "20  MIL   True      4\n",
       "21  NOP   True      2\n",
       "22  OKC  False      1\n",
       "23  OKC   True      6\n",
       "24  ORL  False      2\n",
       "25  ORL   True      2\n",
       "26  PHI  False      2\n",
       "27  PHI   True      2\n",
       "28  PHX  False      4\n",
       "29  PHX   True      4\n",
       "30  POR  False      4\n",
       "31  POR   True      3\n",
       "32  SAC  False      1\n",
       "33  SAC   True      2\n",
       "34  SAS  False      2\n",
       "35  SAS   True      3\n",
       "36  TOR  False      9\n",
       "37  TOR   True      5\n",
       "38  UTA  False      2\n",
       "39  UTA   True      4\n",
       "40  WAS  False      1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#keep only the number of wins\n",
    "total_bubble_wins = bubble_season[bubble_season['W/L'] == 'W']\n",
    "#drop any irrelevant columns\n",
    "total_bubble_wins = total_bubble_wins.reset_index().drop(columns = ['W/L'])\n",
    "#display the total bubble wins\n",
    "total_bubble_wins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that during the NBA bubble, the Washington Wizards (at index 40) has 0 home wins, the New Orleans Pelicans (at index 21) has 0 away wins, and the Memphis Grizzlies (at index 16) has 0 away wins, so the rows for these teams are missing in the above dataframe. This can be determined since normally each team should be ideally represented twice (once for home and another for away), but these teams only appear once in the dataframe. Appropriate rows are manually inserted to avoid missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a row for the home games of the Washington Wizards\n",
    "WAS_home = pd.DataFrame({\"TEAM\": 'WAS', \"Home\": True, 'Count': 0}, index=[41])\n",
    "#insert that row into the dataframe\n",
    "total_bubble_wins = pd.concat([total_bubble_wins, WAS_home]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a row for the away games of the New Orleans Pelicans\n",
    "NOP_away = pd.DataFrame({\"TEAM\": 'NOP', \"Home\": False, 'Count': 0}, index=[21])\n",
    "#insert that row into the dataframe\n",
    "total_bubble_wins = pd.concat([total_bubble_wins.iloc[:21], NOP_away, total_bubble_wins.iloc[21:]]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a row for the away games of the Memphis Grizzlies\n",
    "MEM_away = pd.DataFrame({\"TEAM\": 'MEM', \"Home\": False, 'Count': 0}, index=[16])\n",
    "#insert that row into the dataframe\n",
    "total_bubble_wins = pd.concat([total_bubble_wins.iloc[:16], MEM_away, total_bubble_wins.iloc[16:]]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the appropriate rows now inserted, there is now the correct number of observations in the dataframe representing the NBA bubble season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Home</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAL</th>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAL</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEN</th>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEN</th>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IND</th>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IND</th>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAC</th>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAC</th>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAL</th>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAL</th>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEM</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEM</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIA</th>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIA</th>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIL</th>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIL</th>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOP</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOP</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OKC</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OKC</th>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORL</th>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORL</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHI</th>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHI</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHX</th>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHX</th>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POR</th>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POR</th>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAC</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAC</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAS</th>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAS</th>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTA</th>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTA</th>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Home  Count\n",
       "TEAM              \n",
       "BKN   False      3\n",
       "BKN    True      2\n",
       "BOS   False      9\n",
       "BOS    True      6\n",
       "DAL   False      3\n",
       "DAL    True      2\n",
       "DEN   False      6\n",
       "DEN    True      6\n",
       "HOU   False      3\n",
       "HOU    True      6\n",
       "IND   False      2\n",
       "IND    True      4\n",
       "LAC   False      7\n",
       "LAC    True      5\n",
       "LAL   False      8\n",
       "LAL    True     11\n",
       "MEM   False      0\n",
       "MEM    True      2\n",
       "MIA   False      9\n",
       "MIA    True      8\n",
       "MIL   False      4\n",
       "MIL    True      4\n",
       "NOP   False      0\n",
       "NOP    True      2\n",
       "OKC   False      1\n",
       "OKC    True      6\n",
       "ORL   False      2\n",
       "ORL    True      2\n",
       "PHI   False      2\n",
       "PHI    True      2\n",
       "PHX   False      4\n",
       "PHX    True      4\n",
       "POR   False      4\n",
       "POR    True      3\n",
       "SAC   False      1\n",
       "SAC    True      2\n",
       "SAS   False      2\n",
       "SAS    True      3\n",
       "TOR   False      9\n",
       "TOR    True      5\n",
       "UTA   False      2\n",
       "UTA    True      4\n",
       "WAS   False      1\n",
       "WAS    True      0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reset the index back to team\n",
    "total_bubble_wins = total_bubble_wins.set_index('TEAM')\n",
    "#display the corrected dataframe\n",
    "total_bubble_wins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell will diplay a dataframe containing the win percentages of each team's home and away games during the NBA bubble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Home</th>\n",
       "      <th>Games_Won</th>\n",
       "      <th>Total_Games</th>\n",
       "      <th>Win_Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAL</th>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAL</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEN</th>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEN</th>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>0.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IND</th>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IND</th>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAC</th>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAC</th>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAL</th>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAL</th>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>0.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEM</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEM</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIA</th>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIA</th>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIL</th>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIL</th>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOP</th>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOP</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OKC</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OKC</th>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORL</th>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORL</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHI</th>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHI</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHX</th>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHX</th>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POR</th>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POR</th>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAC</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAC</th>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAS</th>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAS</th>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTA</th>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTA</th>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Home  Games_Won  Total_Games  Win_Percentage\n",
       "TEAM                                               \n",
       "BKN   False          3            6        0.500000\n",
       "BKN    True          2            6        0.333333\n",
       "BOS   False          9           13        0.692308\n",
       "BOS    True          6           12        0.500000\n",
       "DAL   False          3            7        0.428571\n",
       "DAL    True          2            7        0.285714\n",
       "DEN   False          6           14        0.428571\n",
       "DEN    True          6           13        0.461538\n",
       "HOU   False          3           10        0.300000\n",
       "HOU    True          6           10        0.600000\n",
       "IND   False          2            6        0.333333\n",
       "IND    True          4            6        0.666667\n",
       "LAC   False          7           10        0.700000\n",
       "LAC    True          5           11        0.454545\n",
       "LAL   False          8           13        0.615385\n",
       "LAL    True         11           16        0.687500\n",
       "MEM   False          0            4        0.000000\n",
       "MEM    True          2            4        0.500000\n",
       "MIA   False          9           15        0.600000\n",
       "MIA    True          8           14        0.571429\n",
       "MIL   False          4            8        0.500000\n",
       "MIL    True          4           10        0.400000\n",
       "NOP   False          0            4        0.000000\n",
       "NOP    True          2            4        0.500000\n",
       "OKC   False          1            8        0.125000\n",
       "OKC    True          6            7        0.857143\n",
       "ORL   False          2            7        0.285714\n",
       "ORL    True          2            6        0.333333\n",
       "PHI   False          2            6        0.333333\n",
       "PHI    True          2            6        0.333333\n",
       "PHX   False          4            4        1.000000\n",
       "PHX    True          4            4        1.000000\n",
       "POR   False          4            7        0.571429\n",
       "POR    True          3            6        0.500000\n",
       "SAC   False          1            4        0.250000\n",
       "SAC    True          2            4        0.500000\n",
       "SAS   False          2            4        0.500000\n",
       "SAS    True          3            4        0.750000\n",
       "TOR   False          9            9        1.000000\n",
       "TOR    True          5           10        0.500000\n",
       "UTA   False          2            8        0.250000\n",
       "UTA    True          4            7        0.571429\n",
       "WAS   False          1            4        0.250000\n",
       "WAS    True          0            4        0.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the win percentage of home and away games for each team in the NBA bubble\n",
    "total_bubble_wins = total_bubble_wins.assign(Total_Games = total_bubble_games.get('Total Games'))\n",
    "total_bubble_wins = total_bubble_wins.assign(Win_Percentage = total_bubble_wins.get('Count') / total_bubble_wins.get('Total_Games'))\n",
    "#rename the columns for easier interpretation\n",
    "bubble_win_percentage = total_bubble_wins.rename(columns = {'Count': 'Games_Won'})\n",
    "#display the dataframe\n",
    "bubble_win_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Paired T Test Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to determine whether or not NBA teams feel the same effect of home court advantage in the bubble as opposed to during the 2019 season before the bubble, we will compare the average win percentage of home games played before the bubble to the average win percentage of home games played during the bubble using a Paired T Test. This test is used to compare dependent samples, and since we will be testing the win percentages of the same teams before and during the bubble, this test is the most appropriate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Null Hypothesis:</b> ($\\mu_1 = \\mu_2$) The average home game win percentage before the bubble is <b>equal to</b> the average home game win percentage during the bubble.<br>\n",
    "<b>Alternative Hypothesis:</b> ($\\mu_1 \\neq \\mu_2$) The average home game win percentage before the bubble is <b>not equal to</b> the average home game win percentage during the bubble.<br>\n",
    "<b>Confidence Level:</b> $\\alpha = 0.05$. This means that there is 95% confidence that the conclusion of this test will be valid.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.10789933075676 0.28043386344916393\n"
     ]
    }
   ],
   "source": [
    "# extract two arrays containing each team's home game win percentage\n",
    "# one array is for before the bubble and the other is for during the bubble\n",
    "reg_pct_byTeam = regular_season_win_percentage[regular_season_win_percentage['Home'] == True]['Win_Percentage'].values\n",
    "bubble_pct_byTeam = bubble_win_percentage[bubble_win_percentage['Home'] == True]['Win_Percentage'].values\n",
    "\n",
    "# two-sided t-test for the null hypothesis that 2 related samples have equal average (expected) values\n",
    "tStat, pVal = stats.ttest_rel(reg_pct_byTeam, bubble_pct_byTeam)\n",
    "print(tStat, pVal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>T-statistic:</b> $t = 1.10789933075676$.<br>\n",
    "<b>P-value:</b> $p = 0.28043386344916393$.<br>\n",
    "<b>Decision:</b> Since our p-value (0.2804) is <b>greater than</b> our alpha value (0.05), we fail to reject the null hypothesis.<br>\n",
    "<b>Conclusion:</b> Our Paired T Test demonstrates that there is insufficient evidence to support the claim that the average win percentages differ between home games played in the bubble versus outside the bubble.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set plotting size and font\n",
    "plt.rcParams['figure.figsize'] = (17, 7)\n",
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['font.sans-serif'] = ['Tahoma']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure and axes\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# set the title\n",
    "plt.suptitle('Distribution of Home Game Win Percentages', fontsize=36, fontweight='bold', x=0.37, y=1.025)\n",
    "\n",
    "# remove top and right spines and add tick marks to left and bottom spines\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.get_xaxis().tick_bottom()\n",
    "ax.get_yaxis().tick_left()\n",
    "\n",
    "# set the axes' limits, tick mark values, and labels where needed\n",
    "plt.xlim(0, 1) \n",
    "plt.ylim(0, 2.5) \n",
    "plt.xticks([0, 0.2, 0.4, 0.6, 0.8, 1], [str(x) + \"%\" for x in range(0, 101, 20)], fontsize=20)\n",
    "plt.yticks([0.5, 1, 1.5, 2, 2.5], fontsize=20)\n",
    "plt.ylabel(\"Density\", fontsize=26, labelpad=20)\n",
    "\n",
    "# initialize the two distributions using gaussian kernel density estimation as well as the line space\n",
    "density1 = gaussian_kde(regular_season_win_percentage[regular_season_win_percentage['Home'] == True]['Win_Percentage'])\n",
    "density2 = gaussian_kde(bubble_win_percentage[bubble_win_percentage['Home'] == True]['Win_Percentage'])\n",
    "xs = np.linspace(0,1,200)\n",
    "\n",
    "# plot the two distributions along with their means\n",
    "plt.plot(xs, density1(xs))\n",
    "plt.plot(xs, density2(xs))\n",
    "\n",
    "# calculate and plot direct averages of the regular season home game win percentages and bubble win percentages\n",
    "# (all teams equally weighted)\n",
    "avg_reg_pct = regular_season_wins[regular_season_wins['Home'] == True]['Win_Percentage'].values.mean()\n",
    "avg_bub_pct = total_bubble_wins[total_bubble_wins['Home'] == True]['Win_Percentage'].values.mean()\n",
    "plt.axvline(avg_reg_pct, color='C0', linestyle='dashed', ymax=0.75)\n",
    "plt.axvline(avg_bub_pct, color='#C38821', linestyle='dashed', ymax=0.81)\n",
    "\n",
    "# calculate and plot adjusted averages of the regular season home game win percentages and bubble win percentages\n",
    "# (proportional to number of games played by each team)\n",
    "adjusted_reg_pct = regular_season_wins[regular_season_wins['Home'] == True]['Count'].values.sum() / regular_season_wins[regular_season_wins['Home'] == True]['Total_Games'].values.sum()\n",
    "adjusted_bub_pct = total_bubble_wins[total_bubble_wins['Home'] == True]['Count'].values.sum() /total_bubble_wins[total_bubble_wins['Home'] == True]['Total_Games'].values.sum()\n",
    "plt.axvline(adjusted_reg_pct, color='black', linestyle='dashed', ymax=0.76)\n",
    "plt.axvline(adjusted_bub_pct, color='black', linestyle='dashed', ymax=0.81)\n",
    "\n",
    "# add labels to each distribution directly on the graph\n",
    "plt.text(0.74, 1.3, 'Pre-Bubble', color='C0', fontsize=24)\n",
    "plt.text(0.74, 0.85, 'Bubble', color='#C38821', fontsize=24)\n",
    "\n",
    "# add labels to each average percentage directly on the graph\n",
    "plt.text(0.59, 0.25, str(round(avg_reg_pct*100,2)) + '%', color='C0', fontsize=20)\n",
    "plt.text(0.435, 0.25, str(round(avg_bub_pct*100,2)) + '%', color='#C38821', fontsize=20)\n",
    "plt.text(0.59, 0.45, '(' + str(round(adjusted_reg_pct*100,2)) + '%)', color='black', fontsize=16)\n",
    "plt.text(0.435, 0.45, '(' + str(round(adjusted_bub_pct*100,2)) + '%)', color='black', fontsize=16)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from the graph above, the distribution of home game win percentages <b>before</b> the bubble (blue line) is somewhat normal with a clear right skew. Alternatively, the distribution of home game win percentages <b>during</b> the bubble (orange line) appears to be more normal with much less of a skew. The dashed lines represent the averages for each distribution in its corresponding color. These averages are calculated by taking all the team's individual win percentages and computing the mean. The black lines show the <i>adjusted</i> averages of home game win percentages. These averages are calculated by totaling up the number of home game wins among all teams, and dividing by the total number of home games played before the bubble and during the bubble respectively. Although the Paired T Test performed previously did not conclude that there was a statistically significant difference between the home game win percentages, by observing the distribution above, we can still note that the average win percentage of home games was, in fact, greater before the bubble than it was during the bubble. It's also worth noting here that the bubble home game win percentage is slightly inflated because of the NBA Playoff format (this is elaborated on in the Ethics and Privacy section)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Home Game Win Percentage in the Bubble vs. the 2019 Season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extract only home games for analysis\n",
    "bubble_win_percent = bubble_win_percentage[bubble_win_percentage['Home'] == True]\n",
    "szn_2019_win_percent = regular_season_win_percentage[regular_season_win_percentage['Home'] == True]\n",
    "\n",
    "#Concatenate data from both seasons, for use in box plot\n",
    "box_plot_DF = pd.concat([szn_2019_win_percent['Win_Percentage']*100,bubble_win_percent['Win_Percentage']*100], axis=1, keys=['Pre-Bubble','Bubble'])\n",
    "\n",
    "# create a figure and axes\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# set the title\n",
    "plt.suptitle('Home Court Advantage By Team', fontsize=36, fontweight='bold', horizontalalignment='right')\n",
    "\n",
    "# remove top and right spines\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "sns.set(font_scale=1.4, style = \"ticks\", palette = \"colorblind\") \n",
    "\n",
    "#plot the box plot and set font sizes\n",
    "sns.boxplot(data = box_plot_DF, fliersize = 12) \n",
    "plt.ylabel('Home Game Win %', fontsize=26)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The box plot above is graphed using data from home games of the NBA 2019 season and part of the NBA 2020 season that was played in the Bubble. The box plot compiles the average win percentage per team and compares it between the home games played in the Bubble and in the 2019 season before the bubble. This visualization allows us to clearly see the decrease of a home advantage in the games played in the Bubble. Furthermore, we are able to see the outliers in the bubble season with this visualizaiton, which might be attributed to the limited number of games that some teams played in the bubble."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to find which team statistics are most heavily correlated to a team winning. After we find these statistics, we will look to see if any of those stats changed significantly in the bubble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get DataFrame that just includes raw stats without any Team information to be used to run correlation.\n",
    "correlation_stats_2019 = season_2019_box.drop(columns=['TEAM', 'MATCH UP', 'GAME DATE'])\n",
    "correlation_stats_bubble = combined_bubble_box.drop(columns=['TEAM', 'MATCH UP', 'GAME DATE'])\n",
    "\n",
    "# Here we change the values in the Win/Loss column of both correlation Dataframes to True or False.\n",
    "temp = []\n",
    "for i in correlation_stats_2019['W/L']:\n",
    "    if i == 'W':\n",
    "        temp.append(True)\n",
    "    else:\n",
    "        temp.append(False)\n",
    "        \n",
    "correlation_stats_2019['W/L'] = temp\n",
    "\n",
    "temp = []\n",
    "for i in correlation_stats_bubble['W/L']:\n",
    "    if i == 'W':\n",
    "        temp.append(True)\n",
    "    else:\n",
    "        temp.append(False)\n",
    "        \n",
    "correlation_stats_bubble['W/L'] = temp\n",
    "\n",
    "\n",
    "# Here we change the values in the Home column of both correlation DataFrames to 1's and 0's.\n",
    "temp = []\n",
    "\n",
    "for i in correlation_stats_2019[\"Home\"]:\n",
    "    if i == True:\n",
    "        temp.append(1)\n",
    "    else:\n",
    "        temp.append(0)\n",
    "\n",
    "correlation_stats_2019[\"Home\"] = temp\n",
    "\n",
    "temp = []\n",
    "\n",
    "for i in correlation_stats_bubble[\"Home\"]:\n",
    "    if i == True:\n",
    "        temp.append(1)\n",
    "    else:\n",
    "        temp.append(0)\n",
    "\n",
    "correlation_stats_bubble[\"Home\"] = temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can the correlation coefficients of each stat with the W/L column in the correlation_stats_bubble DataFrame and the correlation_stats_2019 DataFrame. Both are stored in respective dictionaries and are then plotted using a bar graph below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PTS': 0.4505507584482207,\n",
       " 'FGM': 0.3803388958336306,\n",
       " 'FGA': -0.035675825810750836,\n",
       " 'FG%': 0.4198769094643511,\n",
       " '3PM': 0.23397087841992448,\n",
       " '3PA': -0.04277271036986875,\n",
       " '3P%': 0.3467993718715599,\n",
       " 'FTM': 0.1333445750803225,\n",
       " 'FTA': 0.12074572217095704,\n",
       " 'FT%': 0.0744331501349495,\n",
       " 'REB': 0.30910079561404663,\n",
       " 'TOV': -0.06585039322785168,\n",
       " 'PF': -0.08824572645317386,\n",
       " '+/-': 0.8035574443475214,\n",
       " 'Home': 0.15116279069767438}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coefficients stored in two dictionaries with the stat as the key and coefficient as the value.\n",
    "\n",
    "correlation_coeff = {}\n",
    "for i in correlation_stats_2019.columns:\n",
    "    if i == 'W/L':\n",
    "        continue\n",
    "    correlation_coeff[i] = correlation_stats_2019[i].corr(correlation_stats_2019['W/L'])\n",
    "\n",
    "    \n",
    "bubble_correlation_coeff = {}\n",
    "for i in correlation_stats_bubble.columns:\n",
    "    if i == 'W/L':\n",
    "        continue\n",
    "    bubble_correlation_coeff[i] = correlation_stats_bubble['W/L'].corr(correlation_stats_bubble[i])\n",
    "\n",
    "# Dictioanry of 2019 coefficients.\n",
    "correlation_coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qqtI//vGPBj8D7tLc34Odjf86d/x+Z7W33npLDz30kFasWKHx48e75WfU/e/6ySefOB0zZswYnTp1yu2/i+ALlEoAXJacnCxJqqmp0bFjx5SVlaW8vDx99NFHWrRokeP8V6Wlpam8vFwzZ85UcHBwvXOxsbGSpNdee00LFy6UaZoaMWKExo4dK29vbxUVFenAgQPatWuXxo8fr969e7t1XV9Xl69OQUGBfvazn+n/t3ffYVFce9zAvxQJUlRsqyCKYhakWLAgIHrhCkiuoCtNJVbwxqAEY8xFREURghqNChowEjSWoLv0qwJKuQoiRRCM7bGCWAARERClyLx/+O68LLuLlJ0F857P8+R54szs7PkxZc+cOed3Hjx4gH79+mH69OkYMWIE5OTkUF5ejmvXriElJQV79+5Fdna20P7k5eXR3NyMqKgorF+/Xmh9cXEx8vLy6O2YJK2YAYCiKERFRUFGRgYURYHH4zHSwMLHdGxpaWn48ccfMWHCBCxcuBCXL1/G+vXroaysLPQA7u/vjwEDBsDLy0vicQIfry89PT2Ymppi0KBBePfuHQoLCxESEoIzZ86Ay+Vi+PDhAp/R0NAAh8MBANTX16OoqAjJycm4ePEigoODYWVlJbC9vLw8nj17hqysLMyYMUOoDOfPn8fbt2+lct4C0on5/PnzqK2tpRuBX79+LdCg8znGtnfvXhw7dgw2NjZgsVhISEjA0qVLkZiYiKFDh9L7e/v2LbZs2QJ7e3vGGpRaa+96Xbp0KWprawWWx8bG4tmzZ+BwONDQ0BBYN23aNIF/y8vLIykpCb6+vkK/PwDA5XKldt62xlTM0r7XtiXpuFpaWrB69Wo8ePAAHA4H79+/R0JCAp48eYLTp08LNHLfvn0b4eHh2LZtG6MNSq21rhOVlJQgJSUFubm5uHnzJrZu3SryM6qqqli2bJnYfYo6T6dNm0Yf5+bmZpSVlSEtLQ3+/v4oKCiQeKN3V+tyAJCXlwdPT0+8fv0a2trasLOzg7KyMkpKSnDp0iUkJSXB3t4egYGBUFBQgKysLBwcHHDw4EHweDz4+PiILVdiYiJqa2thY2Mj1Qal1j5VDxa3fVtt6yN/BykpKVBUVBRZTyD+xiiCIIgOYrPZFJvNFlqelZVF6ejoUDo6OlRpaanIz1pYWFBsNlvs+vr6esrIyIgaN24clZWVJXKb/Px86s2bN10PQAxxcYlSXl5OzZgxg2Kz2dSOHTuouro6kdtlZmZS9vb2Ir/L3NycWrBgAWVmZkY1NTUJbbN7926KzWZTHh4eFJvNpqKjozsXUAdIM2a+y5cvU2w2m9q4cSNlampKGRsbUw0NDV0qf3ukFZubmxtlbW1NH8OamhpqypQplLu7u8B2iYmJlI6ODpWXl9eFaDrm/fv3Ipf/8ssvFJvNpvz8/Ohl2dnZFJvNpr7++muh7Q8cOECx2WzK0tJSaHs3NzfKwMCA8vT0FPldjo6OlJmZGeXk5NTutS4pTMbM5+zsTOnq6lK7du2i2Gw2FRERIbHyt4ep2FpaWqgJEyZQGzdupLfJycmh2Gw29dtvvwl8dtu2bZSpqSn1+vVrCUQkXmeu19a+/vpris1mU9nZ2WK3CQ4OFriXnjx5Umib6upqytDQkFqzZg3FZrMpCwuLTpels5iMmaKkd69ti6m4CgsLKTabTcXGxtLLQkJCKDabTRUVFdHLmpqaqHnz5lErVqzofOG7QFy8165do3R1dSkdHR3qyZMnAutKS0s7fZ7xz+Pg4GChdU+fPqUmTpwolXsuRX26LkdRFHX//n1q4sSJlK6uLnX8+HGqpaVFYP3z588pDodDsdlsysfHR2D5uHHjPnm+Llq0iGKz2VRmZmb3A+qkztaDu3pN9BbR0dEduufwNTQ0UEZGRtS3337LaLk68nftyLlKSA7JqUQQRLeZmJhgzJgxoCgKf/31V5f2cf/+fdTV1eHLL7+EiYmJyG2MjIxEvr2Tpn379qGiogJz587F5s2boaysLHI7MzMzREdHi92Ps7MzXr58if/9738Cy5uamhAXF4dJkya12/1bmiQVM4/HAwA4OTnBzs4Or1+/RkpKCiNl7qjuxPb8+XPo6enRQw5UVVWhpaWF58+f09u8fv0a/v7+cHV1xZQpUxiL44svvhC53NbWFgBQUlLSof24uroC+DhEo6qqSmDdgAEDYG1tjbS0NKF1d+/exY0bN+Dg4AA5ObnOFr9LmI753r17KCwshImJCVatWoU+ffqAy+V2s9Qdw1RsVVVVePfuHcaPH09vw///1udtXl4eIiMjsXXrVgwYMKBLMfQm5ubmGDZsmMjjFx8fj4aGBjg5OfVAyZjRG++13cEfLmdoaEgv4/9/66F0hw8fRklJCXbs2CHdArYxefJkuk5069YtRr9LQ0MDo0ePBgCh+3JPCQgIQH19Pdzd3bFkyRKhIYDDhw9HWFgY+vfvj+joaOTn59PLzc3N2z1fHz58iPz8fIwYMQKmpqaMx9JRkqgH/x1cvXoVdXV1Qr1+e5urV6/Czc0N06ZNg6GhIWxsbLBnzx6hXpTAx3QUOjo6aGpqwsGDBzF79mwYGhpizpw5Ar8pkZGRsLOzw/jx4zFz5kwEBwejpaVF5PcXFRXhu+++g5mZGQwMDDBr1ixs3boV5eXljMXMNNKoRBCERFAUBQBClYeO4j+4VFRUoL6+XmLlkqR3797h7NmzAMR3ZW6tvfwG/Pw8/Mo/X1paGiorK+Hs7Ny9wkqIpGKurKxEWloatLS0YGRkROcQOHPmjOQK20ndjW348OG4c+cOXWmoq6tDcXEx1NXV6W0CAgKgqKgocpijNPBzw3Q0Nwb/OhbHyckJTU1NiI2NFVjO4/EgIyMDR0fHrhVUgiQVM7+yyOFwoKamBgsLCzx69AjXrl2TTEG7oLuxDRw4EIqKigIPujdv3gQA+rx9//49Nm/eDGtra9jY2Eii2D1OTk4ODg4OuHv3rtADH4/Hg4aGRq96QO2O3niv7S7+uSnqvOUPl7t//z5CQ0OxYcMGoSF0PYF/7Uk6z1FbL168wOPHj6GsrCyVvFmfUlpaiqtXr0JBQQHu7u5itxs6dCj9e9H6wZzfuNu2bsQXFRVFb9fV+iZTulsP/jtISUmBvLw8LCwserooYp0+fRorVqxAQUEB/vnPf2LZsmXo378/jhw5goULF6Kmpkbk59avXw8ulwsTExM4OjqipqYGW7ZsQUxMDIKCgrB//37o6enBxcUFffr0waFDhxAeHi60n+joaCxatAiXL1+GsbExli5dCgMDA/B4PDg4OAi84PmckJxKBEF0W1ZWFh4/fgwZGRmBN4mdoampCUNDQ/z1119YtGgRnJ2d6d46CgoKEi6xaCEhIULLNDQ06Er5zZs30djYiGHDhtFvBrtKRUUFX331FWJjY1FWVoZhw4YB+Fi5UlFRga2tLcLCwrr1HR0hrZhjYmLQ1NRE75fNZkNfXx85OTkoKSlhJE8W07EtWrQIa9asweLFizFp0iRkZGSgpqYGixYtAgCkp6fj7NmzOHr0qNgeUJL2+++/o76+HrW1tbh58yby8/Oho6ODf//73x36/KlTpwB8vB4HDhwotN7Y2BijRo0Cj8eDm5sbAND5TUxMTKCpqSm5YDqIiZgbGhqQkJAAVVVV+o0rh8PBhQsXcObMGUZ7nbXGRGzOzs44ceIEamtrwWKxEB8fD2VlZTrB7P79+1FdXQ0/Pz9mghLjU9drdzk6OiI0NBQ8Ho/+nSosLMS9e/ewbt26HnkQZCLmnrjXtiXpuAwNDaGvrw8/Pz9cv36dvucYGhrCwMAAHz58wKZNmzBx4kQsXry4u8Xvtry8PDx+/Bh9+vQR6BXYWk1Njci/EwAMHjyY/h1pLTc3l/5Mc3MzKioqkJaWBgUFBezYsQMqKiqSC6KL+L2O9PX10b9//3a3NTMzw++//46CggJ6mYWFBYYOHYqrV6+itLRU4DelsbERcXFxkJeXh4ODAzMBdNGn6sFM3996g5aWFqSlpWHq1KlS6+Eq7hoCILJx6NmzZwgICKBf6mpra9Prtm3bhsjISPz8888iezs+f/4cZ8+epUdMrFy5Era2tggKCoKqqioSEhLoPG6enp6wsrJCREQEVq5cSTcuP378GH5+ftDQ0MDJkycF8r5dvXoVK1euRGBgIA4dOtS1P0gPIo1KBEF0WutKDT9BIUVRWL58eZffEMrIyCA4OBje3t7Izc2Fv78/gI+zGI0bNw5WVlZYvHgxo5WmtrOrAB8TY/J/9F++fAkAYpN/xsTECM1qM3v2bLGJGJ2dnREVFYWoqCisXbuWToLs4uKCvn37dieUDpNGzNT/TRQrKyuL+fPn08s5HA5u3boFHo+HDRs2dDuWtpiObfbs2di5cyfCw8MRGRkJTU1N7N69G5aWlqitrYWfnx+cnJxgamqK5ORk7Nu3DyUlJRg+fDi++eYbuLi4SDJcAEBERAQqKyvpf5ubm2Pnzp0iG4iePXtGX8v19fW4ceMGrl27BllZWbFJffm9kfbu3Yu8vDxMnToVSUlJqKmp6bHhQ0zEnJiYiDdv3sDFxQWKiooAgJkzZ2Lw4MFITk7G5s2bP/nAJAlMxPbjjz9CUVER586dQ0ZGBvT09PCf//wHLBYLRUVF+OOPP7Br1y6oqanh4MGDOH36NKqqqqCrqwtfX19MnjyZkVg/db12l7q6OszMzHD27Fls3LgRSkpK4HK5kJOT67EHO0nH3FP32rYkHZecnBxCQ0MRFBSExMREyMjIwMbGBj4+PpCVlUV4eDju3buH+Ph41NTUYMeOHUhNTUVzczPMzMywfft2RpN2t64T8RN1UxQFb29vgeT3rdXW1or8OwGArq6u2EYl/qywfPLy8nB0dBTbeCVt/N/VthMJiMJ/mVZRUUEv4/cqDA0NRVRUFL7//nt6XWpqKqqqqmBlZYUhQ4ZIuOSd09l6MNP3t96goKAAlZWV8PDwkNp3iruGxElISEBTUxNWrlwp0KAEAN9//z0SEhIQHx+PLVu2CL3Q3rBhg0AKDk1NTRgZGSEnJwfe3t4C95h+/frB0tISMTExKC8vp8+JyMhINDU1wdfXV+ieZGJiAktLS6Snp6Ourq5XNBJ3BmlUIgii0/g3cRkZGfTr1w+TJ0+Go6Mj5s2b1639qqur48SJE3j48CGuXLmCmzdv4saNG/R/kZGROH78OGO9IdqbmrQ1cW+0Y2NjhSp8GhoaYhuVJkyYADabjZiYGHh4eIDH46GlpUWqQ9+kEXN2djaePHmCGTNmCPyIzp07F7t27UJsbCy8vLzQp0+fLkQgnjRi408b3VZQUBAAwNvbG7du3YKXlxesra3h5+eHCxcuYOvWrWCxWBKfVevKlSsAPg6BuX79Ovbs2YP58+fj8OHDQlPGP3v2jL6W5eXloaamBmtra6xYsQJGRkZiv2PBggUIDg4Gl8vF1KlTweVyoaamhtmzZ0s0lo5iImb+cIzWFX55eXnY2dnh6NGjiI+Px9KlS5kOjZHYFBQU8MMPP+CHH34Q+HxjYyM2bdqEWbNmwd7eHseOHUNISAjWrl0LIyMjhIWFwd3dHRcvXsTgwYMlHmtHr9fucHZ2RkZGBs6dOwdbW1skJiZi1qxZYLFYUp/5DZB8zD11r22LiWPJYrGwf/9+oeXFxcUICQmBl5cXtLS04OHhgdzcXPj5+UFFRQX+/v5Yu3YtuFwuY73R2j7YysjIIDAwsN3eNBoaGvRw1o5au3YtPD09AXzsFfLy5UukpKRg586dSE1NBY/H61BjDpO6MgSs7bZOTk44fPgwYmJi8N1339F5+vhD4npDeoDO1oOlcX/rLktLS6EXaXyifu84HA527txJ//vixYuQkZERqAvk5OSIrEdJqjGtvb+rqHhu374NAJg+fbrQ9v3794eenh7y8vLw6NEj6OrqCqw3MDAQ+gy/0bi9dWVlZXSjUmFhIYCPDcSicm+9evUKHz58QHFxsch99makUYkgiE5j+sdRW1tb4A3Cw4cP4evri+vXryMoKAi//voro98vDv/NmLhEeidOnKD/f9++fR0avubs7IyAgABkZGQgJiYG+vr60NPTk0yBJUASMfNzebStRKipqcHS0hLJyclITU3FnDlzJFXsDmHieAIfGwKio6Nx+PBhqKqqIiIiAsrKyti5cyeUlJQwffp0XLlyBUeOHGFsqvbBgwfDysoKenp6sLGxgbe3N50/im/atGkCMXZm3xYWFrhw4QIWL16M/Px8rFy5UmrDVNsrlyRi5ieCHTNmDCZOnCiwbsGCBTh69Ch4PJ5UGpX4mDyefIcOHUJ5eTkiIiIAfBx6Z2JiQj/Ejh49GpaWljh16hS8vLy6HkwPsrS0xJAhQxAVFYXm5mbU19f3igdUSemt91qmUBQFX19f6OjoYPny5SguLkZqaiq8vLzonlp1dXXw9vZGdna22ElAuotfJ6qvr0dhYSF8fX3h5+cHdXV1xr5TVlYWLBYLrq6uqKioQFhYGEJDQ+le3j2F/yDdkbwwZWVlACDU64if4ywzMxOXLl2CpaUlnj59iqysLGhoaPSKqeo/h0aizlq6dKlQouo7d+4gNTUVHA5HqAdW2xemKSkpGD9+vECDdm5urlCja0/20OLHJ66nG3+5qKFzqqqqQsv4w9pE9Srir2v9wqK6uhrAx9/X9vTW3LLtIY1KBEH0etra2ti9ezesrKyQnZ3dY+UwMDCAgoICXrx4geLiYmhpaXV7n/PmzcOePXvoWR/WrFnT/YJKUHdjrqqqomdxWb9+vdiE1VwuV+oPOkwcz7dv32LLli2wt7enG4wePXqE0aNHQ0lJCcDHN5vjxo2TyrmsoaGBsWPH4s6dO6iqqhI5bKornJ2dceHCBaxbtw4AetXMWd2Nmf9g/ujRI7EJse/du4eCgoJ2e3Qxganjefv2bYSHh2Pbtm1gsVioq6tDRUUF7Ozs6G3U1dWhpqaGBw8eSOQ7e4K8vDw4HA5+++03OpfdzJkze7pYEtGb77VMOXnyJIqKihAXFwdZWVk8fPgQAAR68fHf9j948ICxBh4+JSUlmJqaIjQ0FAsWLMDGjRuRlJTE+HD2CRMmAABu3LjB6Pd0BH947K1bt1BTU9PujL1ZWVkAIPI+6uLigszMTHC5XFhaWiIqKgoURcHBwQGysmSeKSYsX75caFlMTAzdqGRsbCz2s3fu3MHTp0+FhvV7enrSLyZ6A37DUGVlJb788kuh9fzhm6IakCSB3/iUn5//2Q1v+xRyVRIE8VngJzr+1OxUTOrbty/mzp0LABJLotevXz/Y2NigrKwMSkpK+Ne//iWR/UpKd2OOjY1FU1MT9PX14ejoKPK/gQMHIisrC6WlpZIufruYOJ579uxBQ0MDfH196WUURaGxsVFgu4aGBol8X0fw81XwhxBIgpmZGTQ0NFBWVoapU6f2ilmHWutqzI2NjYiPj4esrCwcHBxEnq/8t+TiZidimqSPZ3NzMzZt2gRjY2OhxsGePG+Zwp81qqysDA4ODhK9LnpSb77XMuHp06f45ZdfsGbNGowdOxbA/6sftD5ve+Kc1dXVhZOTE8rKynDs2DHGv4/fq0Lc9OXSpKmpCWNjYzQ2NrbbG6OyspKeyU1Ub0F+r8LLly/jxYsXiI2NhZycXK+YYZQQdvHiRQCgJ7borfi9q3JycoTW1dTU4M6dO/jiiy+E8i1JCr/3c0/OIssU0lOJIIheobS0FOnp6eBwOEJvCCiKQmhoKABg6tSpPVE82rp165CZmYmEhASoqalh3bp1dA+U1urq6jq1T2tra6ipqfXKNxfdiZn/4L1t2zaxiUT3798vMimnNEjyeObl5SEyMhIHDhwQmPlk7NixOH/+PD2TTW1tLfLz88FmsyUSw8OHD9GvXz+h7twtLS04cOAAXr16hUmTJkk0sbSsrCxCQkLw4sWLHmlQYirm5ORkVFdXY+bMmfjpp59EblNXVwdzc3MkJiZi06ZNEn+jKe3jefjwYZSUlAg0rKqoqIDFYiEjIwPNzc2Ql5dHbm4u3r59Sz/Af65GjhyJ8PBwvH//nrGk4z2ht99rJW3Lli3Q0tLCqlWr6GX8ngdpaWn0w216ejoASP289fDwQGxsLCIiIrB48WLGEvs3Njbizz//BPBxWFFv4OvrCxcXFxw5cgQsFktoRr7y8nJ4eHiguroaCxYsEHkdtu5VuGHDBpSVlcHCwoLRhOtE1128eBFjx47t9szITLO3t8evv/6KkydPgsPhCMyGeeDAAdTV1cHJyYmx4fyurq7gcrkICgrCqFGjhP5ejY2NuHHjhtRmmJUk0qhEEESvUFdXh8DAQOzevRtGRkZgs9lQVlbGq1evkJ2djdLSUgwaNEjsrFTSwmKxcOzYMXh6euKPP/5AXFwcpk+fjhEjRkBWVhavXr1CQUEBiouLMWjQoA49cKurq0NdXV0Kpe+arsack5ODx48fg81mtzszjaOjI8LCwhAdHQ1PT096HLo0SOp4vn//Hps3b4a1tTVsbGwE1rm5ueHcuXNYtmwZrKyskJWVhZqamg5PC/8pGRkZ+PnnnzFlyhSMHDkSAwYMQGVlJfLy8lBaWoohQ4YgICBAIt/Vmr6+vlCyaGlhKmZ+gu72hvOpqKhgzpw5iImJQUJCAlxdXbschyjSPJ73799HaGgofHx8hPJluLu7IzAwEK6urhg/fjz++9//QklJSeLx9oTekJNFkj6He60kcblc5ObmIioqSiCGUaNGwcrKCjExMaivr4eKigpiY2Mxfvx4kYl5mcRiseDi4oLjx48jPDxcKDF+TU1Nu9OhczgcjBgxQmBZbm4u/RmKovDy5UtcvnwZZWVl0NTUxLfffiv5QLpAR0cHYWFh8PLywvbt2/Hnn3/C2NgYysrKKCkpwaVLl/Du3TvY2dlh+/btYvfj7OyMI0eO0L06mJgxlei+kpIS3Lt3r9ecf+0ZMWIEfHx84O/vDw6HA1tbWwwcOBB5eXm4fv06xowZw+gMmdra2ggMDISvry/mzp0Lc3NzaGlpobm5Gc+fP0d+fj7U1NSQlJTEWBmY8nn+mhAE8bejra2NQ4cOITMzE0VFRTh//jzevHkDRUVFjBo1CqtXr8ayZcsklkOku2WNi4vDuXPnkJycjOvXryM9PR0yMjIYNGgQdHV14e7ujq+++ooetve560rMHXlABz7+yJuamuLKlStIT0+XevdpSRzPAwcOoLq6Gn5+fkLrxo0bh5CQEOzfvx+nTp3CsGHDEBAQgFmzZkmk/KampnB2dkZBQQHu3r2L2tpa9O3bF1paWpg3bx6WLFki0HPq74CJmIuLi5Gbm4tBgwbBwsKi3W2dnJwQExMDLpcr8UYWaR3PDx8+YNOmTZgwYYJQTwIAWLJkCerq6nD69GncunULurq68PHxYWTmN6J7Ppd7rSSUl5dj9+7dWLVqlciZVX/66ScoKysjNTUVzc3N+Mc//gE/Pz/GZn5rzzfffAMej4cTJ05g2bJlAtdObW1tu9OhT5s2TWSjUuuZtPr27YuRI0di/vz5cHNzazd/kbRNnz4dSUlJOHnyJNLT0xEXF4eGhgYMHDgQM2fOhLOz8ycbdzU1NWFiYoKsrKy/Vf6zv5vPZegbn6urK0aNGoWIiAhcuHAB7969w/Dhw+Hm5obVq1czfh3NmzcPurq6OHr0KHJycpCZmQklJSUMHToUNjY2sLW1ZfT7mSJD9WSCEoIgCIIgCIIgCIIgPjsLFy5EeXk5PdSU+P8TSdRNEARBEARBEARBEESHVVRUoLCwELNnz+7pohA9jPRUIgiCIAiCIAiCIAiCIDqN9FQiCIIgCIIgCIIgCIIgOo00KhEEQRAEQRAEQRAE40CpawAAAF5JREFUQRCdRhqVCIIgCIIgCIIgCIIgiE4jjUoEQRAEQRAEQRAEQRBEp5FGJYIgCIIgCIIgCIIgCKLTSKMSQRAEQRAEQRAEQRAE0WmkUYkgCIIgCIIgCIIgCILotP8DZCh58LDhfNQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure and axes\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# set the title\n",
    "plt.suptitle('Correlation of Pre-Bubble Game Statistics With Win %', fontsize=36, fontweight='bold', x=0.4)\n",
    "\n",
    "# remove top and right spines and add tick marks to left and bottom spines\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.get_xaxis().tick_bottom()\n",
    "ax.get_yaxis().tick_left()\n",
    "\n",
    "# set the axes' ticks and labels where needed\n",
    "plt.ylabel(\"Correlation Coefficient\", fontsize=26, labelpad=20)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "\n",
    "plt.bar(*zip(*correlation_coeff.items()))\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure and axes\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# set the title\n",
    "plt.suptitle('Correlation of Bubble Game Statistics With Win %', fontsize=36, fontweight='bold', x=0.4)\n",
    "\n",
    "# remove top and right spines and add tick marks to left and bottom spines\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.get_xaxis().tick_bottom()\n",
    "ax.get_yaxis().tick_left()\n",
    "\n",
    "# set the axes' ticks and labels where needed\n",
    "plt.ylabel(\"Correlation Coefficient\", fontsize=26, labelpad=20)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "\n",
    "plt.bar(*zip(*bubble_correlation_coeff.items()), color = \"#C38821\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see not too many of the statistics have very strong correlations besides the +/- category. The +/- category is just the difference in points a team wins or loses by. For example, if the Nuggets beat the Clippers by 15 points in Game 7, the Nuggets' +/- is 15, and the Clippers is -15. Because of how +/- is calculated, we are going to throw out that statistic, because it doesn't really carry any significance.\n",
    "\n",
    "We will keep any statistic that has a correlation value above 0.3 or below -0.3. While a correlation of 0.3 is somewhat weak, we did not expect any of the statistics to have a very large correlation coefficient, because basketball games are determined by many factors besides just one stat.\n",
    "\n",
    "We will also keep the two statistics TOV and PF, which are Turnovers and Player Fouls, because our source states that those are stats that are often very different when teams play at Home versus when they are visiting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we get a list of stat names that had a correlation coeff above abs(0.3) in the either correlation dictionary.\n",
    "stat_names = []\n",
    "\n",
    "for key, val in correlation_coeff.items():\n",
    "    if val >= abs(0.3):\n",
    "        stat_names.append(key)\n",
    "\n",
    "for key, val in bubble_correlation_coeff.items():\n",
    "    if val >= abs(0.3):\n",
    "        if key in stat_names:\n",
    "            continue\n",
    "        stat_names.append(key)\n",
    "\n",
    "# Next, we create a list bubble_indicator, which will classify a value as being \"Pre-Bubble\" or \"Bubble\".\n",
    "# The list corr_values is also created to hold the correlation values of each key seen in stat_names.\n",
    "bubble_indicator = []\n",
    "corr_values = []\n",
    "for key in stat_names:\n",
    "    if key == '+/-':\n",
    "        continue\n",
    "    corr_values.append(correlation_coeff[key])\n",
    "    corr_values.append(bubble_correlation_coeff[key])\n",
    "    bubble_indicator.append('Pre_Bubble')\n",
    "    bubble_indicator.append('Bubble')\n",
    "\n",
    "# The stat column is made by taking the stat_names and repeating the stats twice to accomodate for the bubble and\n",
    "# non-bubble stats.\n",
    "stat_col = []\n",
    "for i in stat_names:\n",
    "    if i == '+/-':\n",
    "        continue\n",
    "    stat_col.append(i)\n",
    "    stat_col.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>stat</th>\n",
       "      <th>bubble_indicator</th>\n",
       "      <th>corr_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PTS</td>\n",
       "      <td>Pre_Bubble</td>\n",
       "      <td>0.450551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PTS</td>\n",
       "      <td>Bubble</td>\n",
       "      <td>0.433674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FGM</td>\n",
       "      <td>Pre_Bubble</td>\n",
       "      <td>0.380339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FGM</td>\n",
       "      <td>Bubble</td>\n",
       "      <td>0.340281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FG%</td>\n",
       "      <td>Pre_Bubble</td>\n",
       "      <td>0.419877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FG%</td>\n",
       "      <td>Bubble</td>\n",
       "      <td>0.404737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3P%</td>\n",
       "      <td>Pre_Bubble</td>\n",
       "      <td>0.346799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3P%</td>\n",
       "      <td>Bubble</td>\n",
       "      <td>0.331192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>REB</td>\n",
       "      <td>Pre_Bubble</td>\n",
       "      <td>0.309101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>REB</td>\n",
       "      <td>Bubble</td>\n",
       "      <td>0.263922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stat bubble_indicator  corr_values\n",
       "0  PTS       Pre_Bubble     0.450551\n",
       "1  PTS           Bubble     0.433674\n",
       "2  FGM       Pre_Bubble     0.380339\n",
       "3  FGM           Bubble     0.340281\n",
       "4  FG%       Pre_Bubble     0.419877\n",
       "5  FG%           Bubble     0.404737\n",
       "6  3P%       Pre_Bubble     0.346799\n",
       "7  3P%           Bubble     0.331192\n",
       "8  REB       Pre_Bubble     0.309101\n",
       "9  REB           Bubble     0.263922"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A DataFrame is created with the corr_values and then the bubble_indicator and stat_col lists are also added.\n",
    "corr_df = pd.DataFrame(stat_col, columns = ['stat'])\n",
    "corr_df = corr_df.assign(bubble_indicator = bubble_indicator)\n",
    "corr_df = corr_df.assign(corr_values = corr_values)\n",
    "\n",
    "corr_df\n",
    "# As shown below, the corr_df DataFrame has the corr_values for Pre_Bubble and Bubble and the Stat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure and axes\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# set the title\n",
    "plt.suptitle('Correlation of Selected Statistics with Win %', fontsize=36, fontweight='bold', x=0.37)\n",
    "\n",
    "# remove top and right spines and add tick marks to left and bottom spines\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.get_xaxis().tick_bottom()\n",
    "ax.get_yaxis().tick_left()\n",
    "\n",
    "# set the axes' ticks and labels where needed\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.ylabel('Correlation Coefficient', fontsize=26)\n",
    "\n",
    "# Generate grouped barplot with bubble_indicator as the hue.\n",
    "corr_graph = sns.barplot(data=corr_df, y = 'corr_values', x = 'stat', hue = 'bubble_indicator')\n",
    "\n",
    "corr_label = ['PTS', 'FGM', 'FG%', '3P%', 'REB']\n",
    "# Add title and axis labels to graph for clarity\n",
    "corr_graph.set_ylabel('Correlation Coefficient')\n",
    "corr_graph.set_xlabel(None)\n",
    "corr_graph.set_xticklabels(corr_label)\n",
    "\n",
    "leg = corr_graph.axes.get_legend()\n",
    "leg.set_title('Sample Indicator')\n",
    "new_labels = ['Pre-Bubble', 'Bubble']\n",
    "for t, l in zip(leg.texts, new_labels): t.set_text(l)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above shows the correlation coefficients of the Pre-Bubble and Bubble stats with their respective win data. All of the Bubble correlation coefficients are slightly smaller than the Pre-Bubble, and this could be due to the small amount of games played in the bubble. That being said all of the correlations of the stats we will be exploring, between the Pre-Bubble and Bubble are fairly similar with the biggest difference between Rebounds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that will be used later to grab stats out of our datasets\n",
    "def get_stat(made, attempted = []):\n",
    "    \"\"\"\n",
    "    To be used on stats that have a Made and Attempted category or singular stats. Collects the totals of these statistics \n",
    "    over all games. Takes in two arrays, lists, or series of numbers for made and attempted. \n",
    "    Returns a list of [total, average made per game, total attempted, percentage] or just [total, average] for singular stats.\n",
    "    Rounds all numbers to the 3 decimals places.\n",
    "    \"\"\"\n",
    "    if len(attempted) == 0:\n",
    "        totals = [sum(made), round(made.mean(), 3)]\n",
    "    else:\n",
    "        totals = [sum(made), round(made.mean(), 3), sum(attempted), round(sum(made/sum(attempted)), 3)]\n",
    "    return totals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next few cells, we create new DataFrames that contain the significant stats that were determined from the correlation analysis. The Dataframes created are as follows:\n",
    "\n",
    "bub_stats_home - average stat grouped per team at home during the bubble <br>\n",
    "bub_stats_away - average stat grouped per team when away during the bubble <br>\n",
    "stats_home_2019 - average stat grouped per team at home outside of the bubble <br>\n",
    "stats_awat_2019 - average stat grouped per team when away outside of the bubble <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates new DataFrame with only Home games from bubble with stat averages grouped by team.\n",
    "bubble_only_home = combined_bubble_box[combined_bubble_box['Home'] == True]\n",
    "bub_stats_home = bubble_only_home.groupby('TEAM').mean().round(3)\n",
    "\n",
    "# Drops columns for stats, we will no longer be looking at.\n",
    "bub_stats_home = bub_stats_home.drop(columns = ['FGA', 'FG%', '3PA', '3P%', 'FTM', 'FTA', 'FT%', '+/-'])\n",
    "\n",
    "# Uses get_stat fucntion to get the FG_PCT for each team and adds to DF.\n",
    "temp_lst = []\n",
    "for team in bub_stats_home.index:\n",
    "    current_team = bubble_only_home[bubble_only_home['TEAM'] == team]\n",
    "    totals = get_stat(current_team['FGM'], current_team['FGA'])\n",
    "    temp_lst.append(totals[3])\n",
    "\n",
    "bub_stats_home = bub_stats_home.assign(FG_PCT = temp_lst)\n",
    "\n",
    "# Uses get_stat fucntion to get the 3P_PCT for each team and adds to DF.\n",
    "temp_lst = []\n",
    "for team in bub_stats_home.index:\n",
    "    current_team = bubble_only_home[bubble_only_home['TEAM'] == team]\n",
    "    totals = get_stat(current_team['3PM'], current_team['3PA'])\n",
    "    temp_lst.append(totals[3])\n",
    "\n",
    "# Changes name of column to 3PM.\n",
    "bub_stats_home = bub_stats_home.assign(Three_PCT = temp_lst)\n",
    "bub_stats_home = bub_stats_home.rename(columns = {'ThreePM_per_game': '3PM_per_game', 'Three_PCT':'3P_PCT'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This block of code is the exact same as in the cell above, but for away games.\n",
    "\n",
    "# Creates new DataFrame with only Away games from bubble with stat averages grouped by team.\n",
    "bubble_only_away = combined_bubble_box[combined_bubble_box['Home'] == False]\n",
    "bub_stats_away = bubble_only_away.groupby('TEAM').mean().round(3)\n",
    "\n",
    "# Drops columns for stats, we will no longer be looking at.\n",
    "bub_stats_away = bub_stats_away.drop(columns = ['FGA', 'FG%', '3PA', '3P%', 'FTM', 'FTA', 'FT%', '+/-'])\n",
    "\n",
    "# Uses get_stat fucntion to get the FG_PCT for each team and adds to DF.\n",
    "temp_lst = []\n",
    "for team in bub_stats_away.index:\n",
    "    current_team = bubble_only_away[bubble_only_away['TEAM'] == team]\n",
    "    totals = get_stat(current_team['FGM'], current_team['FGA'])\n",
    "    temp_lst.append(totals[3])\n",
    "\n",
    "bub_stats_away = bub_stats_away.assign(FG_PCT = temp_lst)\n",
    "\n",
    "# Uses get_stat fucntion to get the 3P_PCT for each team and adds to DF.\n",
    "temp_lst = []\n",
    "for team in bub_stats_away.index:\n",
    "    current_team = bubble_only_away[bubble_only_away['TEAM'] == team]\n",
    "    totals = get_stat(current_team['3PM'], current_team['3PA'])\n",
    "    temp_lst.append(totals[3])\n",
    "\n",
    "# Changes name of column to 3PM.\n",
    "bub_stats_away = bub_stats_away.assign(Three_PCT = temp_lst)\n",
    "bub_stats_away = bub_stats_away.rename(columns = {'ThreePM_per_game': '3PM_per_game', 'Three_PCT':'3P_PCT'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>3PM</th>\n",
       "      <th>REB</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>FG_PCT</th>\n",
       "      <th>3P_PCT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>117.000</td>\n",
       "      <td>41.000</td>\n",
       "      <td>15.000</td>\n",
       "      <td>43.833</td>\n",
       "      <td>12.833</td>\n",
       "      <td>21.000</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>115.000</td>\n",
       "      <td>40.583</td>\n",
       "      <td>13.250</td>\n",
       "      <td>45.417</td>\n",
       "      <td>13.417</td>\n",
       "      <td>21.833</td>\n",
       "      <td>0.467</td>\n",
       "      <td>0.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAL</th>\n",
       "      <td>125.857</td>\n",
       "      <td>44.714</td>\n",
       "      <td>16.571</td>\n",
       "      <td>45.857</td>\n",
       "      <td>12.143</td>\n",
       "      <td>23.571</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEN</th>\n",
       "      <td>109.769</td>\n",
       "      <td>40.538</td>\n",
       "      <td>12.000</td>\n",
       "      <td>41.077</td>\n",
       "      <td>13.692</td>\n",
       "      <td>22.462</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>108.700</td>\n",
       "      <td>36.400</td>\n",
       "      <td>17.100</td>\n",
       "      <td>37.800</td>\n",
       "      <td>13.000</td>\n",
       "      <td>22.100</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PTS     FGM     3PM     REB     TOV      PF  FG_PCT  3P_PCT\n",
       "TEAM                                                                 \n",
       "BKN   117.000  41.000  15.000  43.833  12.833  21.000   0.446   0.361\n",
       "BOS   115.000  40.583  13.250  45.417  13.417  21.833   0.467   0.365\n",
       "DAL   125.857  44.714  16.571  45.857  12.143  23.571   0.468   0.374\n",
       "DEN   109.769  40.538  12.000  41.077  13.692  22.462   0.480   0.379\n",
       "HOU   108.700  36.400  17.100  37.800  13.000  22.100   0.426   0.348"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A peek at what the bub_stats_home df looks like.\n",
    "bub_stats_home.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The same code done up above is now done for the regular season splitting between Home and Away.\n",
    "\n",
    "# Creates new DataFrame with only Home games from regular season with stat averages grouped by team.\n",
    "only_home_2019 = season_2019_box[season_2019_box['Home'] == True]\n",
    "stats_home_2019 = only_home_2019.groupby('TEAM').mean().round(3)\n",
    "\n",
    "# Drops columns for stats, we will no longer be looking at.\n",
    "stats_home_2019 = stats_home_2019.drop(columns = ['FGA', 'FG%', '3PA', '3P%', 'FTM', 'FTA', 'FT%', '+/-'])\n",
    "\n",
    "# Uses get_stat fucntion to get the FG_PCT for each team and adds to DF.\n",
    "temp_lst = []\n",
    "for team in season_2019_box['TEAM'].unique():\n",
    "    current_team = only_home_2019[only_home_2019['TEAM'] == team]\n",
    "    totals = get_stat(current_team['FGM'], current_team['FGA'])\n",
    "    temp_lst.append(totals[3])\n",
    "\n",
    "stats_home_2019 = stats_home_2019.assign(FG_PCT = temp_lst)\n",
    "\n",
    "# Uses get_stat fucntion to get the 3P_PCT for each team and adds to DF.\n",
    "temp_lst = []\n",
    "for team in season_2019_box['TEAM'].unique():\n",
    "    current_team = only_home_2019[only_home_2019['TEAM'] == team]\n",
    "    totals = get_stat(current_team['3PM'], current_team['3PA'])\n",
    "    temp_lst.append(totals[3])\n",
    "\n",
    "# Changes name of column to 3PM.\n",
    "stats_home_2019 = stats_home_2019.assign(Three_PCT = temp_lst)\n",
    "stats_home_2019 = stats_home_2019.rename(columns = {'ThreePM_per_game': '3PM_per_game', 'Three_PCT':'3P_PCT'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same as above, but for Away.\n",
    "\n",
    "# Creates new DataFrame with only Away games from regular season with stat averages grouped by team.\n",
    "only_away_2019 = season_2019_box[season_2019_box['Home'] == False]\n",
    "stats_away_2019 = only_away_2019.groupby('TEAM').mean().round(3)\n",
    "\n",
    "# Drops columns for stats, we will no longer be looking at.\n",
    "stats_away_2019 = stats_away_2019.drop(columns = ['FGA', 'FG%', '3PA', '3P%', 'FTM', 'FTA', 'FT%', '+/-'])\n",
    "\n",
    "# Uses get_stat fucntion to get the FG_PCT for each team and adds to DF.\n",
    "temp_lst = []\n",
    "for team in season_2019_box['TEAM'].unique():\n",
    "    current_team = only_away_2019[only_away_2019['TEAM'] == team]\n",
    "    totals = get_stat(current_team['FGM'], current_team['FGA'])\n",
    "    temp_lst.append(totals[3])\n",
    "\n",
    "stats_away_2019 = stats_away_2019.assign(FG_PCT = temp_lst)\n",
    "\n",
    "# Uses get_stat fucntion to get the 3P_PCT for each team and adds to DF.\n",
    "temp_lst = []\n",
    "for team in season_2019_box['TEAM'].unique():\n",
    "    current_team = only_away_2019[only_away_2019['TEAM'] == team]\n",
    "    totals = get_stat(current_team['3PM'], current_team['3PA'])\n",
    "    temp_lst.append(totals[3])\n",
    "\n",
    "# Changes name of column to 3PM.\n",
    "stats_away_2019 = stats_away_2019.assign(Three_PCT = temp_lst)\n",
    "stats_away_2019 = stats_away_2019.rename(columns = {'ThreePM_per_game': '3PM_per_game', 'Three_PCT':'3P_PCT'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>3PM</th>\n",
       "      <th>REB</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>Home</th>\n",
       "      <th>FG_PCT</th>\n",
       "      <th>3P_PCT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>108.905</td>\n",
       "      <td>38.857</td>\n",
       "      <td>12.429</td>\n",
       "      <td>49.095</td>\n",
       "      <td>14.333</td>\n",
       "      <td>21.286</td>\n",
       "      <td>False</td>\n",
       "      <td>0.439</td>\n",
       "      <td>0.353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>108.591</td>\n",
       "      <td>39.409</td>\n",
       "      <td>12.091</td>\n",
       "      <td>45.955</td>\n",
       "      <td>14.273</td>\n",
       "      <td>21.636</td>\n",
       "      <td>False</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAL</th>\n",
       "      <td>116.167</td>\n",
       "      <td>40.958</td>\n",
       "      <td>16.125</td>\n",
       "      <td>47.375</td>\n",
       "      <td>12.625</td>\n",
       "      <td>19.542</td>\n",
       "      <td>False</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEN</th>\n",
       "      <td>107.125</td>\n",
       "      <td>40.000</td>\n",
       "      <td>11.208</td>\n",
       "      <td>42.417</td>\n",
       "      <td>14.583</td>\n",
       "      <td>19.958</td>\n",
       "      <td>False</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>116.478</td>\n",
       "      <td>40.391</td>\n",
       "      <td>15.652</td>\n",
       "      <td>42.870</td>\n",
       "      <td>15.783</td>\n",
       "      <td>22.478</td>\n",
       "      <td>False</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PTS     FGM     3PM     REB     TOV      PF   Home  FG_PCT  3P_PCT\n",
       "TEAM                                                                        \n",
       "BKN   108.905  38.857  12.429  49.095  14.333  21.286  False   0.439   0.353\n",
       "BOS   108.591  39.409  12.091  45.955  14.273  21.636  False   0.487   0.365\n",
       "DAL   116.167  40.958  16.125  47.375  12.625  19.542  False   0.450   0.353\n",
       "DEN   107.125  40.000  11.208  42.417  14.583  19.958  False   0.456   0.355\n",
       "HOU   116.478  40.391  15.652  42.870  15.783  22.478  False   0.462   0.341"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A peek at what the stats_away_2019 df looks like.\n",
    "stats_away_2019.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have split up the data for bubble and non-bubble games and also home and away games. We subtract the averages of the home stats in the bubble from the average away stats in the bubble. This gives us a value that shows how differently teams performed when at Home and when Away to determine if there were any benefits to playing at Home in the bubble. We do the same with the non-bubble games and store them in both in new Dataframes called, bub_stat_diff and stat_diff_2019 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates dataframe with Teams as index the same as in the previously created df's.\n",
    "bub_stat_diff = pd.DataFrame()\n",
    "bub_stat_diff = bub_stat_diff.assign(TEAM = bub_stats_home.index)\n",
    "bub_stat_diff = bub_stat_diff.set_index('TEAM')\n",
    "\n",
    "# In each stat column, the home average is subtracted from the away average.\n",
    "for i in bub_stats_home.columns:\n",
    "    bub_stat_diff[i] = bub_stats_home[i].subtract(bub_stats_away[i])\n",
    "\n",
    "\n",
    "# Creates dataframe with Teams as index the same as in the previously created df's.\n",
    "stat_diff_2019 = pd.DataFrame()\n",
    "stat_diff_2019 = stat_diff_2019.assign(TEAM = stats_home_2019.index)\n",
    "stat_diff_2019 = stat_diff_2019.set_index('TEAM')\n",
    "\n",
    "# In each stat column, the home average is subtracted from the away average.\n",
    "for i in bub_stats_home.columns:\n",
    "    stat_diff_2019[i] = stats_home_2019[i].subtract(stats_away_2019[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below both of the stat difference, DataFrames are shown. Reading these dataframes can be tricky, so here is an example.\n",
    "\n",
    "The team BOS, which is the Boston Celtics, have a 5.000 in the PTS column. This means that when Boston played at Home in the bubble, on average they scored about 5 more points than they scored on average when they were away in the bubble. A negative number such as the TOV column for BOS, means they turned the ball over on average 1.814 more times when they were Away in the bubble. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>3PM</th>\n",
       "      <th>REB</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>FG_PCT</th>\n",
       "      <th>3P_PCT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>3.667</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.500</td>\n",
       "      <td>-1.667</td>\n",
       "      <td>-3.500</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>5.000</td>\n",
       "      <td>1.660</td>\n",
       "      <td>0.712</td>\n",
       "      <td>-0.660</td>\n",
       "      <td>-1.814</td>\n",
       "      <td>-1.398</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAL</th>\n",
       "      <td>11.428</td>\n",
       "      <td>5.143</td>\n",
       "      <td>5.857</td>\n",
       "      <td>1.857</td>\n",
       "      <td>-0.286</td>\n",
       "      <td>1.285</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEN</th>\n",
       "      <td>-1.802</td>\n",
       "      <td>0.467</td>\n",
       "      <td>-0.214</td>\n",
       "      <td>-0.494</td>\n",
       "      <td>-0.737</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>-4.000</td>\n",
       "      <td>-2.500</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-2.300</td>\n",
       "      <td>-1.900</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PTS    FGM    3PM    REB    TOV     PF  FG_PCT  3P_PCT\n",
       "TEAM                                                           \n",
       "BKN    3.667 -0.500  0.167  0.500 -1.667 -3.500  -0.008   0.016\n",
       "BOS    5.000  1.660  0.712 -0.660 -1.814 -1.398   0.013   0.025\n",
       "DAL   11.428  5.143  5.857  1.857 -0.286  1.285   0.015   0.049\n",
       "DEN   -1.802  0.467 -0.214 -0.494 -0.737 -0.109   0.005   0.007\n",
       "HOU   -4.000 -2.500 -0.100 -2.300 -1.900 -0.800  -0.015   0.003"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bub_stat_diff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>3PM</th>\n",
       "      <th>REB</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>FG_PCT</th>\n",
       "      <th>3P_PCT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>0.666</td>\n",
       "      <td>1.572</td>\n",
       "      <td>0.476</td>\n",
       "      <td>-1.524</td>\n",
       "      <td>1.857</td>\n",
       "      <td>-1.572</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>-0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>8.045</td>\n",
       "      <td>2.500</td>\n",
       "      <td>1.091</td>\n",
       "      <td>-1.637</td>\n",
       "      <td>-2.046</td>\n",
       "      <td>-0.227</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>-0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAL</th>\n",
       "      <td>-3.167</td>\n",
       "      <td>-1.343</td>\n",
       "      <td>-1.894</td>\n",
       "      <td>-1.567</td>\n",
       "      <td>0.837</td>\n",
       "      <td>-0.927</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEN</th>\n",
       "      <td>4.315</td>\n",
       "      <td>2.000</td>\n",
       "      <td>-0.528</td>\n",
       "      <td>3.223</td>\n",
       "      <td>-1.263</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.369</td>\n",
       "      <td>-1.932</td>\n",
       "      <td>4.450</td>\n",
       "      <td>-1.983</td>\n",
       "      <td>-0.838</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PTS    FGM    3PM    REB    TOV     PF  FG_PCT  3P_PCT\n",
       "TEAM                                                          \n",
       "BKN   0.666  1.572  0.476 -1.524  1.857 -1.572  -0.011  -0.028\n",
       "BOS   8.045  2.500  1.091 -1.637 -2.046 -0.227  -0.016  -0.030\n",
       "DAL  -3.167 -1.343 -1.894 -1.567  0.837 -0.927   0.020   0.043\n",
       "DEN   4.315  2.000 -0.528  3.223 -1.263 -0.118   0.012   0.000\n",
       "HOU  -0.038  0.369 -1.932  4.450 -1.983 -0.838   0.012   0.012"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_diff_2019.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>3PM</th>\n",
       "      <th>REB</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>FG_PCT</th>\n",
       "      <th>3P_PCT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BKN</th>\n",
       "      <td>3.001</td>\n",
       "      <td>-2.072</td>\n",
       "      <td>-0.309</td>\n",
       "      <td>2.024</td>\n",
       "      <td>-3.524</td>\n",
       "      <td>-1.928</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOS</th>\n",
       "      <td>-3.045</td>\n",
       "      <td>-0.840</td>\n",
       "      <td>-0.379</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.232</td>\n",
       "      <td>-1.171</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAL</th>\n",
       "      <td>14.595</td>\n",
       "      <td>6.486</td>\n",
       "      <td>7.751</td>\n",
       "      <td>3.424</td>\n",
       "      <td>-1.123</td>\n",
       "      <td>2.212</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEN</th>\n",
       "      <td>-6.117</td>\n",
       "      <td>-1.533</td>\n",
       "      <td>0.314</td>\n",
       "      <td>-3.717</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.009</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOU</th>\n",
       "      <td>-3.962</td>\n",
       "      <td>-2.869</td>\n",
       "      <td>1.832</td>\n",
       "      <td>-6.750</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>-0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IND</th>\n",
       "      <td>7.181</td>\n",
       "      <td>3.442</td>\n",
       "      <td>3.045</td>\n",
       "      <td>-5.803</td>\n",
       "      <td>1.265</td>\n",
       "      <td>2.889</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAC</th>\n",
       "      <td>-2.356</td>\n",
       "      <td>-1.233</td>\n",
       "      <td>-0.216</td>\n",
       "      <td>1.985</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.415</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>-0.063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAL</th>\n",
       "      <td>5.473</td>\n",
       "      <td>2.480</td>\n",
       "      <td>2.828</td>\n",
       "      <td>-1.896</td>\n",
       "      <td>-2.825</td>\n",
       "      <td>1.695</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEM</th>\n",
       "      <td>-1.709</td>\n",
       "      <td>1.042</td>\n",
       "      <td>-3.833</td>\n",
       "      <td>7.500</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-7.500</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIA</th>\n",
       "      <td>-9.399</td>\n",
       "      <td>-3.588</td>\n",
       "      <td>-2.409</td>\n",
       "      <td>3.430</td>\n",
       "      <td>1.351</td>\n",
       "      <td>0.669</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>-0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIL</th>\n",
       "      <td>-13.538</td>\n",
       "      <td>-7.494</td>\n",
       "      <td>-2.149</td>\n",
       "      <td>-4.687</td>\n",
       "      <td>2.190</td>\n",
       "      <td>0.522</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>-0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOP</th>\n",
       "      <td>-9.831</td>\n",
       "      <td>-4.332</td>\n",
       "      <td>1.361</td>\n",
       "      <td>6.705</td>\n",
       "      <td>3.068</td>\n",
       "      <td>1.588</td>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OKC</th>\n",
       "      <td>10.556</td>\n",
       "      <td>2.523</td>\n",
       "      <td>1.786</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.854</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORL</th>\n",
       "      <td>5.236</td>\n",
       "      <td>0.719</td>\n",
       "      <td>3.134</td>\n",
       "      <td>-7.141</td>\n",
       "      <td>1.807</td>\n",
       "      <td>-0.196</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHI</th>\n",
       "      <td>-6.327</td>\n",
       "      <td>-5.180</td>\n",
       "      <td>1.720</td>\n",
       "      <td>-5.554</td>\n",
       "      <td>-4.810</td>\n",
       "      <td>2.220</td>\n",
       "      <td>-0.064</td>\n",
       "      <td>-0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHX</th>\n",
       "      <td>-2.815</td>\n",
       "      <td>-0.724</td>\n",
       "      <td>-1.429</td>\n",
       "      <td>4.150</td>\n",
       "      <td>2.221</td>\n",
       "      <td>2.150</td>\n",
       "      <td>-0.055</td>\n",
       "      <td>-0.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POR</th>\n",
       "      <td>-4.565</td>\n",
       "      <td>-0.477</td>\n",
       "      <td>-4.107</td>\n",
       "      <td>0.757</td>\n",
       "      <td>-1.125</td>\n",
       "      <td>2.381</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>-0.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAC</th>\n",
       "      <td>-2.835</td>\n",
       "      <td>-2.463</td>\n",
       "      <td>0.837</td>\n",
       "      <td>-0.765</td>\n",
       "      <td>-2.113</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAS</th>\n",
       "      <td>7.092</td>\n",
       "      <td>6.711</td>\n",
       "      <td>-1.005</td>\n",
       "      <td>-3.328</td>\n",
       "      <td>-4.233</td>\n",
       "      <td>-4.145</td>\n",
       "      <td>0.034</td>\n",
       "      <td>-0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>-16.893</td>\n",
       "      <td>-5.612</td>\n",
       "      <td>-3.106</td>\n",
       "      <td>-4.691</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.664</td>\n",
       "      <td>-0.048</td>\n",
       "      <td>-0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UTA</th>\n",
       "      <td>6.543</td>\n",
       "      <td>-0.893</td>\n",
       "      <td>2.898</td>\n",
       "      <td>-5.791</td>\n",
       "      <td>-3.581</td>\n",
       "      <td>1.547</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>-0.297</td>\n",
       "      <td>1.246</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>-4.671</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.401</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PTS    FGM    3PM    REB    TOV     PF  FG_PCT  3P_PCT\n",
       "TEAM                                                           \n",
       "BKN    3.001 -2.072 -0.309  2.024 -3.524 -1.928   0.003   0.044\n",
       "BOS   -3.045 -0.840 -0.379  0.977  0.232 -1.171   0.029   0.055\n",
       "DAL   14.595  6.486  7.751  3.424 -1.123  2.212  -0.005   0.006\n",
       "DEN   -6.117 -1.533  0.314 -3.717  0.526  0.009  -0.007   0.007\n",
       "HOU   -3.962 -2.869  1.832 -6.750  0.083  0.038  -0.027  -0.009\n",
       "IND    7.181  3.442  3.045 -5.803  1.265  2.889   0.069   0.048\n",
       "LAC   -2.356 -1.233 -0.216  1.985  0.725  0.415  -0.056  -0.063\n",
       "LAL    5.473  2.480  2.828 -1.896 -2.825  1.695  -0.026   0.033\n",
       "MEM   -1.709  1.042 -3.833  7.500 -1.000 -7.500   0.048   0.004\n",
       "MIA   -9.399 -3.588 -2.409  3.430  1.351  0.669  -0.012  -0.029\n",
       "MIL  -13.538 -7.494 -2.149 -4.687  2.190  0.522  -0.053  -0.021\n",
       "NOP   -9.831 -4.332  1.361  6.705  3.068  1.588  -0.096   0.031\n",
       "OKC   10.556  2.523  1.786  1.624  1.854  0.098   0.065   0.084\n",
       "ORL    5.236  0.719  3.134 -7.141  1.807 -0.196  -0.012   0.039\n",
       "PHI   -6.327 -5.180  1.720 -5.554 -4.810  2.220  -0.064  -0.048\n",
       "PHX   -2.815 -0.724 -1.429  4.150  2.221  2.150  -0.055  -0.059\n",
       "POR   -4.565 -0.477 -4.107  0.757 -1.125  2.381  -0.013  -0.085\n",
       "SAC   -2.835 -2.463  0.837 -0.765 -2.113  0.088  -0.009   0.066\n",
       "SAS    7.092  6.711 -1.005 -3.328 -4.233 -4.145   0.034  -0.054\n",
       "TOR  -16.893 -5.612 -3.106 -4.691  2.150  0.664  -0.048  -0.052\n",
       "UTA    6.543 -0.893  2.898 -5.791 -3.581  1.547   0.018   0.017\n",
       "WAS   -0.297  1.246 -0.008 -4.671  0.087 -0.401   0.001   0.012"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we create a new df called season_bub_comparison.\n",
    "season_bub_comparison = pd.DataFrame()\n",
    "season_bub_comparison = season_bub_comparison.assign(TEAM = bub_stats_home.index)\n",
    "season_bub_comparison = season_bub_comparison.set_index('TEAM')\n",
    "\n",
    "# Subtracts the bub_stat_diff from the stat_diff_2019\n",
    "for i in bub_stat_diff.columns:\n",
    "    season_bub_comparison[i] = bub_stat_diff[i].subtract(stat_diff_2019[i])\n",
    "\n",
    "season_bub_comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe above is also a bit tricky to understand. This season_bub_comparison dataframe contains the home/away differentials of the bubble season minus the home/away differentials of the regular season.\n",
    "\n",
    "To give an example, UTA the Utah Jazz, have a value of 1.547 in the PF column. That means the number that represents the difference between number of home player fouls and away player fouls in the bubble was 1.547 more than the difference of home and away player fouls in the regular season. What this means, is that there was a <b> larger </b> difference between the bubble home and away games than the non-bubble home and away games."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we perform a T-test on the stats at home in the bubble and the stats at home when out of the Bubble. This will tell us if there were any significant changes in any of the stats when teams played at home in the bubble versus when they played at home in the normal season. The same is done for the away stats and the results are stored in the dataframe, p_value_df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Home_Pvalues</th>\n",
       "      <th>Away_Pvalues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PF</th>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REB</th>\n",
       "      <td>0.074051</td>\n",
       "      <td>0.536184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3PM</th>\n",
       "      <td>0.120670</td>\n",
       "      <td>0.473772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGM</th>\n",
       "      <td>0.261443</td>\n",
       "      <td>0.952529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3P_PCT</th>\n",
       "      <td>0.466088</td>\n",
       "      <td>0.739639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTS</th>\n",
       "      <td>0.545670</td>\n",
       "      <td>0.119185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOV</th>\n",
       "      <td>0.601110</td>\n",
       "      <td>0.157180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FG_PCT</th>\n",
       "      <td>0.798291</td>\n",
       "      <td>0.288644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Home_Pvalues  Away_Pvalues\n",
       "PF          0.000037      0.000354\n",
       "REB         0.074051      0.536184\n",
       "3PM         0.120670      0.473772\n",
       "FGM         0.261443      0.952529\n",
       "3P_PCT      0.466088      0.739639\n",
       "PTS         0.545670      0.119185\n",
       "TOV         0.601110      0.157180\n",
       "FG_PCT      0.798291      0.288644"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_values_home = {}\n",
    "p_values_away = {}\n",
    "\n",
    "# Adds the t-test done on each stat to the corresponding dictionary with the stat as the key.\n",
    "for col in stat_diff_2019.columns:\n",
    "    p_values_home[col] = stats.ttest_rel(stats_home_2019[col], bub_stats_home[col])[1]\n",
    "    p_values_away[col] = stats.ttest_rel(stats_away_2019[col], bub_stats_away[col])[1]\n",
    "    \n",
    "\n",
    "# Creates the df using the dictionaries of the p_values.\n",
    "p_value_df = pd.DataFrame.from_dict(p_values_home, orient = 'index')\n",
    "p_value_df = p_value_df.rename(columns = {0: 'Home_Pvalues'})\n",
    "p_value_df = p_value_df.assign(Away_Pvalues = p_values_away.values())\n",
    "\n",
    "# Sorts the df in increasng order using column 'Home_Pvalues'.\n",
    "p_value_df = p_value_df.sort_values('Home_Pvalues')\n",
    "p_value_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using an alpha 0.05, p_values dataframe shows that Player Fouls and REB, for home, are the only stats that were significant, therefore unlikely to be just a coincidence or happen by chance. Below the average player fouls in the bubble and out of the bubble are shown. The home player fouls in went up almost 2 whole fouls a game, which is a very large amount. This is really interesting, because our source also mentioned that generally referees call more fouls on the away team, but in the bubble this did not stay consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-Bubble\n",
      "Average Player Fouls at home 20.267\n",
      "Average Player Fouls when away 21.069\n"
     ]
    }
   ],
   "source": [
    "print('Pre-Bubble')\n",
    "print('Average Player Fouls at home {}\\nAverage Player Fouls when away {}'.format(round(stats_home_2019['PF'].mean(), 3), \\\n",
    "                                                                                  round(stats_away_2019['PF'].mean(), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bubble\n",
      "Average Player Fouls at home 22.147\n",
      "Average Player Fouls when away 22.774\n"
     ]
    }
   ],
   "source": [
    "print('Bubble')\n",
    "print('Average Player Fouls at home {}\\nAverage Player Fouls when away {}'.format(round(bub_stats_home['PF'].mean(), 3), \\\n",
    "                                                                                  round(bub_stats_away['PF'].mean(), 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below the average rebounds per game in the bubble and out of the bubble are shown. If you remember the away rebounds were not significant, but they are printed here to display the difference between the home and away differentials. The rebounds in the bubble decreased by about 1.5 for Home teams and about .4 for Away teams. \n",
    "\n",
    "One reason for this could have been because the amount of fouls increased in the bubble. Free throws are easier to make, and made shots lead to no rebound and that could be a reason for the lower amount of rebounds. Another cause could have been because there were less shots taken in the bubble. The average shots per game in the bubble was about 2 less.\n",
    "\n",
    "That being said there is not much that can be concluded from the rebounds of only home teams in the bubble being significantly different, which could have been caused by many factors, such as which teams played more games in the bubble or just because there weren't enough games played in the bubble for the value to average out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-Bubble\n",
      "Average Rebounds at home 45.853\n",
      "Average Rebounds when away 44.396\n"
     ]
    }
   ],
   "source": [
    "print('Pre-Bubble')\n",
    "print('Average Rebounds at home {}\\nAverage Rebounds when away {}'.format(round(stats_home_2019['REB'].mean(), 3), \\\n",
    "                                                                          round(stats_away_2019['REB'].mean(), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-Bubble\n",
      "Average Rebounds at home 44.36\n",
      "Average Rebounds when away 43.912\n"
     ]
    }
   ],
   "source": [
    "print('Pre-Bubble')\n",
    "print('Average Rebounds at home {}\\nAverage Rebounds when away {}'.format(round(bub_stats_home['REB'].mean(), 3), \\\n",
    "                                                                          round(bub_stats_away['REB'].mean(), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Shots Taken Per Game Regular Season: 89.313\n",
      "Number of Shots Taken Per Game Bubble: 87.018\n"
     ]
    }
   ],
   "source": [
    "print('Number of Shots Taken Per Game Regular Season: {}\\nNumber of Shots Taken Per Game Bubble: {}'.format(\n",
    "       get_stat(season_2019_box['FGA'])[1], get_stat(combined_bubble_box['FGA'])[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In regards to the datasets utilized in our analysis, all data was obtained from the website of the National Basketball Association (NBA) and Kaggle. Both of these sources offer their datasets to be publically available for free open-use in our analysis, meaning we have permission to access and use the datasets. Furthermore, because our data pertains to the statistics of team performance, there is no personally identifying information of any one player to preserve privacy. The National Basketball Association is a considerably reputable source to obtain relevant datasets, so we can be confident our data itself is not misrepresentative, especially because our data mainly focuses on objective statistics in regards to basketball performance, such as the average points scored, three point shot accuracy, and points allowed per team. The Kaggle dataset we used was created using data from the NBA stat website as well, so it is also reputable. Therefore, our data is collected and used in a way that is ethical and protects players’ privacy. Our question is also ethical in that it is not asked with malicious intent and the answer does not require the use of private information.\n",
    "\n",
    "While all data is objective, we do recognize the data is in some ways biased. Particularly, in the amount of home and away teams played in the bubble. In the bubble playoff games, teams that were seeded higher, and therefore stronger, we’re given more home games by nature of the playoff format. Therefore, because stronger teams play at home slightly more often than the weaker teams, the home win percentage in the bubble is slightly higher than it would be if each team got an equal number of home and away games. Because this is natural to the NBA playoff format, we did not adjust for this in our analysis, and instead, note it here.\n",
    "\n",
    "To ensure the data is not skewed, we limited the scope of our data to include only the teams that participated in both the 2019-2020 regular season and the NBA bubble so that our analysis isn’t skewed by including outside teams which lie beyond our intended population (teams that are playing in the NBA Bubble). Without this correction of the dataset, the statistics for the 2019-2020 season before the Bubble would be inaccurate as they would be privy to influence from the weaker teams that did not end up being invited to the NBA Bubble, which would have possibly inaccurately increased the home win-rate.\n",
    "\n",
    "Our analysis doesn’t biasedly discriminate against any one player or team or intrude upon their personally private information. Beyond our analysis into our visualizations, we’ve thoroughly ensured that our visualizations are not misleading or biased, but also presented in a clear and easily understandable manner to our audience. To ensure the accuracy of our data, we printed out a number of dataframes during the analysis to ensure there is no surprising missingness or NaN values, along with no extreme outlier values that need to be explained.\n",
    "\n",
    "Fortunately, our research question and analysis doesn’t carry any heavy ethical burdens or repercussions. Nevertheless, it’s still necessary to prevent any morally harmful consequences as a result of our findings. One likely rare but possible reason for ethical concern is if, in a scenario where there is an NBA Bubble, a team-owner or one player were to read our project and abuse our findings for personal monetary gain. Particularly, in cases where this owner or financial investor in the NBA sees that some performance statistics decreased in the NBA Bubble, which they could use to argue that the NBA Bubble is significantly impacting player performance and game quality, and thus the league needs to be reopened. This demand to reopen the league could be solely with the motive that reopening the league will result in the owners and investors making more money. We condemn this use of the results and we emphasize that the NBA’s #1 concern should always be the safety of the players and the fans.\n",
    "\n",
    "We would also want to protect the safety of individual players given our results, such as if head coaches and management begin to handle players in an unethical manner because our results show a change in overall performance. In order to avert such outcomes, we would place a noticeable disclaimer to dissuade any unethical applications of our project in its deployment. \n",
    "\n",
    "Note, all values to determine statistical significance throughout the analysis (for example, the threshold to determine a significant p value or the value the marks a significant correlation value) are standard and in-line with standard practice in analysis in the field of Data Science. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our analysis, we were unable to prove our hypothesis that NBA Bubble was unsuccessful in replicating the home-court advantage in the 2019-2020 regular season. By comparing the winning percentage of home teams before and during the NBA Bubble with a t-test, we found that the winning percentage did change, but not with a strong enough p value to confidently say the results are conclusive. After finding this difference, we were able to conclude in our analysis that there were only a few statistics that contribute to winning that were significantly different when comparing home games in and out of the Bubble. Namely, in the Bubble, home teams comitted more fouls and they got less rebounds. Our source, BleacherReport, mentioned how teams at Home often get less fouls called on them than the Away team, and we found that fouls at home and away in the bubble were much closer than outside of the bubble. While we cannot definitively know the cause for this, it is possible that the absence of the fans caused the referees to be less biased.\n",
    "\n",
    "There are various limitations in regards to the scope of analysis for our project. For one, the number of basketball games played in the NBA bubble is relatively small compared to a typical season, leaving only a small sample size to conduct our analysis as a result for bubble games. Furthermore, to limit any confounding variables in regards to the difference in skill and quality of teams contributing to the win percentage when comparing the regular season to the NBA bubble, we’ve restricted our analysis to only include teams who’ve participated in both the 2019-2020 regular season and bubble. While this limits any fluctuations between players and teams between the regular season and the bubble, this further limits the size of the dataset we can use to conduct our analysis. Because the size of our dataset is not very large, it naturally lowers the confidence level in our results. Note, the dataset that we are comparing with the Bubble is limited to the 2019-2020 season because we are accounting for roster changes between seasons. Because the vastly different rosters in previous seasons (before the 2019-2020 season), we cannot compare game statistics or home win-rate in those seasons to that of the most recent season. The teams have simply changed too much. Throughout even the 2019-2020 season there were a number of injuries and roster changes that changed the ability of teams to win at certain points of the season. As a result, this wins and losses of a team over the course of a season may not be consistent. If these injuries or roster changes occurred more or less often during home games, it could influence home-court win-rate. Aside from the bubble itself, many external factors were present as well which may have had an impact on the performance of players. Aspects such as the ongoing fight for social justice, the complications of COVID-19, and the psychological toll of leaving one's family to stay at the Walt Disney Resort for an extended period of time would definitely have a psychological impact on the players playing in the bubble. While we took these factors into consideration while doing our analysis, it was difficult to incorporate them into our analysis because these factors cannot be easily measured.\n",
    "\n",
    "Our intentions in completing this project were to analyze the impact the NBA Bubble had on player performance, especially in regards to the notion of homecourt advantage. Given the unpredictable status of long-term effects of COVID-19, the implementation of a quarantine bubble could be implemented again in the future, although we currently believe and hope the Bubble was a one-time event. Potentially, NBA teams and owners can see which aspects of standard NBA games they could implement in future seasons in COVID-modified environments similar to the NBA bubble. Our hope is that our research will provide some kind idea of how much home-court advantage and associated statistics must change to replicate live games. We hope that our analysis can aid in sports leagues, not just the NBA, across the globe in creating an environment which maintains the competitiveness and integrity of pre-COVID performance, while still protecting the physical safety of the players. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overview, Research Question, Background, Hypothesis: Ryan, Dylan <br>\n",
    "Datasets: Kamen <br>\n",
    "Data Cleaning: Kamen, Dylan <br>\n",
    "Data Analysis: Ryan, Kamen, Justin, Dylan, Alejandro <br>\n",
    "T-test : Justin <br>\n",
    "Box-Plot: Alejandro <br>\n",
    "Correlation: Kamen, Ryan, Dylan, Justin <br>\n",
    "Ethics and Privacy: Ryan, Dylan <br>\n",
    "Conclusion and Limitations: Ryan, Dylan <br>\n",
    "Extra Credit Video: Alejandro, Ryan, Kamen, Justin, Dylan <br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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