FinalProject_group133.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# COGS 108 - Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "In this project, we investigated the correlation between having more free throws attempted and winning more games by the use of bar charts, tables and pie charts to  visualize our data and T-test in order to analyze our data and check for correlations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "Zeshu Zhu <br>\n",
    "David Zhao <br>\n",
    "Kousha Changizi <br>\n",
    "Brandon Chau <br>\n",
    "Brian Wang <br>\n",
    "Vala Masjedizadeh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question\n",
    "The question that we tackled and analyzed in this project is how much of an effect having more attempted free throws will have on winning games in the regular season.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background and Prior Work\n",
    "In today’s modern NBA, James Harden is prolific for being a player that draws in more fouls than any other player in NBA history. Although James Harden is a super star athlete in his own right, many fans argue if his team wins only because of the amount of free throws he gets to shoot. Our question looks to dive into this by going into recent NBA seasons and analyzing each game though looking at the team that attempted more free throws and seeing if that team won or loss that game. Perhaps many people think that the more free throws mean the higher percentage the team winning rate, but the data we collect is not. \n",
    "\n",
    "From our research, we found previous work that showed free throws are important and crucial to team success in volume rather than accuracy (attempted over made).\n",
    "\n",
    "References <br>\n",
    " 1) http://theseason.gc.com/basketball-free-throw-rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n",
    "Our hypothesis for this question leads us to believe that more free throws attempted does lead to a higher % of won games because more free throw shots lead to more uncontested attempts which could naturally result in more points. Therefore we would have to expect the teams that make it to playoffs in Eastern and Western conference based on their regular season results and their seedings would have a correlation with their attempted free throws. In other words, since these teams had more wins, we would expect that they would have more attempted free throws than the league average and after finding meaningful values based on our analysis, we would look to see how much having more free throws impacted their winning rates.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import needed libraries and modules\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import os\n",
    "import scipy\n",
    "from IPython.display import display_html\n",
    "def display_side_by_side(*args):\n",
    "   html_str=''\n",
    "   for df in args:\n",
    "       html_str+=df.to_html()\n",
    "   display_html(html_str.replace('table','table style=\"display:inline\"'),raw=True)\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "def multi_table(table_list):\n",
    "    ''' Acceps a list of IpyTable objects and returns a table which contains each IpyTable in a cell\n",
    "    '''\n",
    "    return HTML(\n",
    "        '<table><tr style=\"background-color:white;\">' + \n",
    "        ''.join(['<td>' + table._repr_html_() + '</td>' for table in table_list]) +\n",
    "        '</tr></table>'\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets\n",
    "Our dataset was obtained from https://www.kaggle.com/pablote/nba-enhanced-stats. This dataset is based on box score and standings statistics of the NBA teams from 2012-2018. The data that we chose is official box scores which contains every statistic in an NBA game. The size of the whole dataset is 44.3k rows x 119 column.These data points that we used are the number of games that were played each regular season game results and each team’s rankings and free throws attempted in those games.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in our file of data\n",
    "df = pd.read_csv(\"2012-18_officialBoxScore.csv\")\n",
    "df.drop_duplicates( keep='first',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning and Pre-Processing\n",
    "The first step taken was extracting the data necessary from this dataset since it was a huge and detailed dataset and not all the data that was presented in this dataset interested us. We broke up the data just to have the games played in every regular season, their dates, game results and each team’s ranking and free throws attempted. \n",
    " \t\n",
    "   Afterwards, we broke the data frame to look at the teams that had more free throws attempted in their games and observed the outcome of the game in which the respective team had more attempted free throws.\n",
    "    \n",
    "   Then, to go into more depth, we then extracted just the top 8 teams from each conference in the first and last season and compared their free throw attempts to the league average for that particular season. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Break up data frame to just the dates, team result, team free throws\n",
    "DateRsltFta = df\n",
    "\n",
    "wins = DateRsltFta[DateRsltFta['teamRslt'] == 'Win']\n",
    "loss = DateRsltFta[DateRsltFta['teamRslt'] == 'Loss']\n",
    "\n",
    "# Break up dataframes for each season\n",
    "mask1213 = (DateRsltFta['gmDate'] >= '2012-10-30') & (DateRsltFta['gmDate'] <= '2013-04-17')\n",
    "season1213 = DateRsltFta.loc[mask1213]\n",
    "\n",
    "mask1314 = (DateRsltFta['gmDate'] >= '2013-10-29') & (DateRsltFta['gmDate'] <= '2014-04-16')\n",
    "season1314 = DateRsltFta.loc[mask1314]\n",
    "\n",
    "mask1415 = (DateRsltFta['gmDate'] >= '2014-10-28') & (DateRsltFta['gmDate'] <= '2015-04-15')\n",
    "season1415 = DateRsltFta.loc[mask1415]\n",
    "\n",
    "mask1516 = (DateRsltFta['gmDate'] >= '2015-10-27') & (DateRsltFta['gmDate'] <= '2016-04-13')\n",
    "season1516 = DateRsltFta.loc[mask1516]\n",
    "\n",
    "mask1617 = (DateRsltFta['gmDate'] >= '2016-10-25') & (DateRsltFta['gmDate'] <= '2017-04-12')\n",
    "season1617 = DateRsltFta.loc[mask1617]\n",
    "\n",
    "mask1718 = (DateRsltFta['gmDate'] >= '2017-10-17') & (DateRsltFta['gmDate'] <= '2018-04-11')\n",
    "season1718 = DateRsltFta.loc[mask1718]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summing total wins and losses per season:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhaoy\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\zhaoy\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\zhaoy\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:8682: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n",
      "C:\\Users\\zhaoy\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3296: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "C:\\Users\\zhaoy\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\zhaoy\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\zhaoy\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\zhaoy\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\zhaoy\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:35: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\zhaoy\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:41: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
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   ],
   "source": [
    "# Break up data frame to see which team shot more free throws\n",
    "\n",
    "# Season 2012-2013\n",
    "moreFTA1213 = season1213[season1213['teamFTA'] > season1213['opptFTA']]\n",
    "moreFTA1213.drop_duplicates( keep='first',inplace=True) \n",
    "sumMoreFTA1213Win = sum(moreFTA1213['teamRslt'] == 'Win')\n",
    "sumMoreFTA1213Loss = sum(moreFTA1213['teamRslt'] == 'Loss')\n",
    "\n",
    "\n",
    "moreFTA1213copy = moreFTA1213 \n",
    "moreFTA1213copy.teamRslt[moreFTA1213copy.teamRslt == 'Win'] = 1\n",
    "moreFTA1213copy.teamRslt[moreFTA1213copy.teamRslt == 'Loss'] = 0\n",
    "\n",
    "\n",
    "# Season 2013-2014\n",
    "moreFTA1314 = season1314[season1314['teamFTA'] > season1314['opptFTA']]\n",
    "moreFTA1314.drop_duplicates( keep='first',inplace=True) \n",
    "sumMoreFTA1314Win = sum(moreFTA1314['teamRslt'] == 'Win')\n",
    "sumMoreFTA1314Loss = sum(moreFTA1314['teamRslt'] == 'Loss')\n",
    "\n",
    "# Season 2014-2015\n",
    "moreFTA1415 = season1415[season1415['teamFTA'] > season1415['opptFTA']]\n",
    "moreFTA1415.drop_duplicates( keep='first',inplace=True) \n",
    "sumMoreFTA1415Win = sum(moreFTA1415['teamRslt'] == 'Win')\n",
    "sumMoreFTA1415Loss = sum(moreFTA1415['teamRslt'] == 'Loss')\n",
    "\n",
    "# Season 2015-2016\n",
    "moreFTA1516 = season1516[season1516['teamFTA'] > season1516['opptFTA']]\n",
    "moreFTA1516.drop_duplicates( keep='first',inplace=True) \n",
    "sumMoreFTA1516Win = sum(moreFTA1516['teamRslt'] == 'Win')\n",
    "sumMoreFTA1516Loss = sum(moreFTA1516['teamRslt'] == 'Loss')\n",
    "\n",
    "# Season 2016-2017\n",
    "moreFTA1617 = season1617[season1617['teamFTA'] > season1617['opptFTA']]\n",
    "moreFTA1617.drop_duplicates( keep='first',inplace=True)\n",
    "sumMoreFTA1617Win = sum(moreFTA1617['teamRslt'] == 'Win')\n",
    "sumMoreFTA1617Loss = sum(moreFTA1617['teamRslt'] == 'Loss')\n",
    "\n",
    "# Season 2017-2018\n",
    "moreFTA1718 = season1718[season1718['teamFTA'] > season1718['opptFTA']]\n",
    "moreFTA1718.drop_duplicates( keep='first',inplace=True) \n",
    "sumMoreFTA1718Win = sum(moreFTA1718['teamRslt'] == 'Win')\n",
    "sumMoreFTA1718Loss = sum(moreFTA1718['teamRslt'] == 'Loss')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis and Results\n",
    "Graphing Wins vs. Losses when shooting more free throws than the opposing team (All seasons combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seasons = ['2012-2013', '2013-2014', '2014-2015', '2015-2016', '2016-2017', '2017-2018']\n",
    "wins = [sumMoreFTA1213Win, sumMoreFTA1314Win, sumMoreFTA1415Win, sumMoreFTA1516Win, sumMoreFTA1617Win, sumMoreFTA1718Win]\n",
    "losses = [sumMoreFTA1213Loss, sumMoreFTA1314Loss, sumMoreFTA1415Loss, sumMoreFTA1516Loss, sumMoreFTA1617Loss, sumMoreFTA1718Loss]\n",
    "# WinAndLossDf = pd.DataFrame(seasons,columns=['Seasons'])\n",
    "# WinAndLossDf['Games Won'] = wins\n",
    "# WinAndLossDf['Games Lost'] = losses\n",
    "# WinAndLossDf.plot(x=\"Seasons\", y=[\"Games Won\", \"Games Lost\"],kind=\"bar\", width=.4).legend(bbox_to_anchor=(1, 1))\n",
    "\n",
    "# create plt\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(6)\n",
    "bar_width = 0.3\n",
    "opacity = 0.8\n",
    "\n",
    "rects1 = plt.bar(index, wins, bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Wins')\n",
    "\n",
    "rects2 = plt.bar(index + bar_width, losses, bar_width,\n",
    "alpha=opacity,\n",
    "color='r',\n",
    "label='Losses')\n",
    "\n",
    "plt.xlabel('Seasons')\n",
    "plt.ylabel('Number of Games')\n",
    "plt.title('Win or Loss Result of More Free Throw Attempts')\n",
    "plt.xticks(index + bar_width-.17, seasons)\n",
    "plt.legend()\n",
    "plt.figure(figsize=(15,15))\n",
    "# # plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "# plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
    "#            ncol=2, mode=\"expand\", borderaxespad=0.)\n",
    "ax.legend(loc='best', bbox_to_anchor=(1,1))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 1: This is a bar plot indicating the outcome of the games for the team with more free throws attempted in each season from 2012-2018. For instance in the 2012-2013 season, approximately 2025 games were won where the winning team had more free throw attempts. From the plot we can tell that more games were won each season when more free throws were attempted and therefore there is a higher chance of winning a single game when more free throws are made.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For better visual, with a pie chart\n",
    "\n",
    "labels = 'Win and more Free Throws Attempts', 'Loss and more Free Throw Attempts'\n",
    "colors = ['gold', 'lightskyblue']\n",
    "sizes = [sumMoreFTA1213Win, sumMoreFTA1213Loss]\n",
    "explode = (0.1, 0)\n",
    "\n",
    "patches,texts = plt.pie(sizes, colors=colors, shadow=True, startangle=90, explode=explode)\n",
    "plt.legend(patches, labels, loc=\"best\")\n",
    "plt.pie(sizes, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 2: In order to be able to better visualize the results, a pie chart is plotted in which the percentage of the games that were won and lost with more free throws attempted are shown. From the plot we can interpret that more free throws leads to a higher chance of winning a game since 58.6% of the games were won when more free shots were made.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NBA Team Rankings 2012 - 2018\n",
    "\n",
    "rank1 = pd.read_csv('12-13_Standing.csv')\n",
    "rank1 = rank1.sort_values(by=['RANK'], ascending=True)\n",
    "\n",
    "rank2 = pd.read_csv('13-14_Standing.csv')\n",
    "rank2 = rank2.sort_values(by=['RANK'], ascending=True)\n",
    "\n",
    "rank3 = pd.read_csv('14-15_Standing.csv')\n",
    "rank3 = rank3.sort_values(by=['RANK'], ascending=True)\n",
    "\n",
    "rank4 = pd.read_csv('15-16_Standing.csv')\n",
    "rank4 = rank4.sort_values(by=['RANK'], ascending=True)\n",
    "\n",
    "rank5 = pd.read_csv('16-17_Standing.csv')\n",
    "rank5 = rank5.sort_values(by=['RANK'], ascending=True)\n",
    "\n",
    "rank6 = pd.read_csv('17-18_Standing.csv')\n",
    "rank6 = rank6.sort_values(by=['RANK'], ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop one of the duplicate column for each season\n",
    "\n",
    "rank11 = rank1.drop(['RANKORD'], axis = 1)\n",
    "rank22 = rank2.drop(['RANKORD'], axis = 1)\n",
    "rank33 = rank3.drop(['RANKORD'], axis = 1)\n",
    "rank44 = rank4.drop(['RANKORD'], axis = 1)\n",
    "rank55 = rank5.drop(['RANKORD'], axis = 1)\n",
    "rank66 = rank6.drop(['RANKORD'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playoff Team ranking for reference (Each season)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>RANK</th>\n",
       "      <th>WIN</th>\n",
       "      <th>LOST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>OKC</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>MIA</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>SA</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>NY</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>DEN</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>IND</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>MEM</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>BKN</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>LAC</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>CHI</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>ATL</td>\n",
       "      <td>6</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>GS</td>\n",
       "      <td>6</td>\n",
       "      <td>47</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>LAL</td>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>BOS</td>\n",
       "      <td>7</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>HOU</td>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>MIL</td>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>TOR</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>UTA</td>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>PHI</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>DAL</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>POR</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>WAS</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>DET</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>MIN</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>CLE</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>SAC</td>\n",
       "      <td>13</td>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>NO</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>CHA</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>PHO</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2012-13</td>\n",
       "      <td>ORL</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>RANK</th>\n",
       "      <th>WIN</th>\n",
       "      <th>LOST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>SA</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>IND</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>MIA</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>OKC</td>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>TOR</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>CHI</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>LAC</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>POR</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>HOU</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>BKN</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>WAS</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>GS</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>CHA</td>\n",
       "      <td>7</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>MEM</td>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>DAL</td>\n",
       "      <td>8</td>\n",
       "      <td>49</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>NY</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>PHO</td>\n",
       "      <td>9</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>MIN</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>CLE</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>DEN</td>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>DET</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>NO</td>\n",
       "      <td>12</td>\n",
       "      <td>34</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>BOS</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>ORL</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>SAC</td>\n",
       "      <td>13</td>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>LAL</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>PHI</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>MIL</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013-14</td>\n",
       "      <td>UTA</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\">"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display data table 2012 - 13, 2013 - 14\n",
    "display_side_by_side(rank11, rank22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>RANK</th>\n",
       "      <th>WIN</th>\n",
       "      <th>LOST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>ATL</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>GS</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>LAC</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>CLE</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>HOU</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>CHI</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>TOR</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>SA</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>MEM</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>WAS</td>\n",
       "      <td>5</td>\n",
       "      <td>46</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>MIL</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>POR</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>DAL</td>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>BOS</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>IND</td>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>BKN</td>\n",
       "      <td>8</td>\n",
       "      <td>38</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>NO</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>OKC</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>MIA</td>\n",
       "      <td>10</td>\n",
       "      <td>37</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>PHO</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>CHA</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>UTA</td>\n",
       "      <td>11</td>\n",
       "      <td>38</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>DEN</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>DET</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>ORL</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>SAC</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>LAL</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>PHI</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>MIN</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-15</td>\n",
       "      <td>NY</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>RANK</th>\n",
       "      <th>WIN</th>\n",
       "      <th>LOST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>CLE</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>GS</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>TOR</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>SA</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>ATL</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>BOS</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>CHA</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>OKC</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>MIA</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>LAC</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>POR</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>MEM</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>DAL</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>IND</td>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>HOU</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>DET</td>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>UTA</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>CHI</td>\n",
       "      <td>9</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>SAC</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>DEN</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>WAS</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>ORL</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>NO</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>MIL</td>\n",
       "      <td>12</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>MIN</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>NY</td>\n",
       "      <td>13</td>\n",
       "      <td>32</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>PHO</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>BKN</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>PHI</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015-16</td>\n",
       "      <td>LAL</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\">"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display data table 2014 - 15, 2015 - 16\n",
    "display_side_by_side(rank33, rank44)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>RANK</th>\n",
       "      <th>WIN</th>\n",
       "      <th>LOST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>BOS</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>GS</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>TOR</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>SA</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>CLE</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>HOU</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>WAS</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>UTA</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>LAC</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>ATL</td>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>IND</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>OKC</td>\n",
       "      <td>6</td>\n",
       "      <td>47</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>MIL</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>MEM</td>\n",
       "      <td>7</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>POR</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>CHI</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>MIA</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>DEN</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>NO</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>DET</td>\n",
       "      <td>10</td>\n",
       "      <td>37</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>DAL</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>CHA</td>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>SAC</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>NY</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>ORL</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>MIN</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>LAL</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>PHI</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>PHO</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-2017</td>\n",
       "      <td>BKN</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEASON</th>\n",
       "      <th>TEAM</th>\n",
       "      <th>RANK</th>\n",
       "      <th>WIN</th>\n",
       "      <th>LOST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>TOR</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>HOU</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>GS</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>PHI</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>POR</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>OKC</td>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>CLE</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>NO</td>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>UTA</td>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>IND</td>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>MIL</td>\n",
       "      <td>6</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>MIA</td>\n",
       "      <td>6</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>SA</td>\n",
       "      <td>7</td>\n",
       "      <td>47</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>MIN</td>\n",
       "      <td>7</td>\n",
       "      <td>47</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>WAS</td>\n",
       "      <td>8</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>DET</td>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>DEN</td>\n",
       "      <td>9</td>\n",
       "      <td>46</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>CHA</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>LAC</td>\n",
       "      <td>10</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>NY</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>LAL</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>BKN</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>SAC</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>DAL</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>CHI</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>MEM</td>\n",
       "      <td>14</td>\n",
       "      <td>22</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>ORL</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>PHO</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-18</td>\n",
       "      <td>ATL</td>\n",
       "      <td>15</td>\n",
       "      <td>24</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\">"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display data table 2016 - 17, 2017 - 18\n",
    "display_side_by_side(rank55, rank66)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating each season's league average free throws per game:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#League average free throw attempts\n",
    "\n",
    "leagueAVG1213 = season1213['teamFTA'].sum() / len(season1213)\n",
    "\n",
    "leagueAVG1314 = season1314['teamFTA'].sum() / len(season1314)\n",
    "\n",
    "leagueAVG1415 = season1415['teamFTA'].sum() / len(season1415)\n",
    "\n",
    "leagueAVG1516 = season1516['teamFTA'].sum() / len(season1516)\n",
    "\n",
    "leagueAVG1617 = season1617['teamFTA'].sum() / len(season1617)\n",
    "\n",
    "leagueAVG1718 = season1718['teamFTA'].sum() / len(season1718)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating Average Free Throw Attempts for teams that made the playoffs (For simplication, we will only look at the Season 2012-2013 and 2017-2018):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Season 12-13 teams making playoffs\n",
    "\n",
    "# Eastern Conference\n",
    "\n",
    "# Seed 1\n",
    "MIA1213 = season1213[season1213['teamAbbr'] == 'MIA']\n",
    "MIAFTA1213 = MIA1213['teamFTA'].sum() / len(MIA1213)\n",
    "MIA1213DIFF = MIAFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 2\n",
    "NY1213 = season1213[season1213['teamAbbr'] == 'NY']\n",
    "NYFTA1213 = NY1213['teamFTA'].sum() / len(NY1213)\n",
    "NY1213DIFF = NYFTA1213 - leagueAVG1213;\n",
    "\n",
    "\n",
    "\n",
    "# Seed 3\n",
    "IND1213 = season1213[season1213['teamAbbr'] == 'IND']\n",
    "INDFTA1213 = IND1213['teamFTA'].sum() / len(IND1213)\n",
    "IND1213DIFF = INDFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 4\n",
    "BKN1213 = season1213[season1213['teamAbbr'] == 'BKN']\n",
    "BKNFTA1213 = BKN1213['teamFTA'].sum() / len(BKN1213)\n",
    "BKN1213DIFF = BKNFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 5\n",
    "CHI1213 = season1213[season1213['teamAbbr'] == 'CHI']\n",
    "CHIFTA1213 = CHI1213['teamFTA'].sum() / len(CHI1213)\n",
    "CHI1213DIFF = CHIFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 6\n",
    "ATL1213 = season1213[season1213['teamAbbr'] == 'ATL']\n",
    "ATLFTA1213 = ATL1213['teamFTA'].sum() / len(ATL1213)\n",
    "ATL1213DIFF = ATLFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 7\n",
    "BOS1213 = season1213[season1213['teamAbbr'] == 'BOS']\n",
    "BOSFTA1213 = BOS1213['teamFTA'].sum() / len(BOS1213)\n",
    "BOS1213DIFF = BOSFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 8\n",
    "MIL1213 = season1213[season1213['teamAbbr'] == 'MIL']\n",
    "MILFTA1213 = MIL1213['teamFTA'].sum() / len(MIL1213)\n",
    "MIL1213DIFF = MILFTA1213 - leagueAVG1213;\n",
    "\n",
    "East1213Top8 = [MIAFTA1213, NYFTA1213, INDFTA1213, BKNFTA1213, CHIFTA1213, ATLFTA1213, BOSFTA1213, MILFTA1213]\n",
    "East1213Top8DIFF = [MIA1213DIFF, NY1213DIFF, IND1213DIFF, BKN1213DIFF, CHI1213DIFF, ATL1213DIFF, BOS1213DIFF, MIL1213DIFF]\n",
    "\n",
    "\n",
    "# Western Conference\n",
    "\n",
    "# Seed 1\n",
    "OKC1213 = season1213[season1213['teamAbbr'] == 'OKC']\n",
    "OKCFTA1213 = OKC1213['teamFTA'].sum() / len(OKC1213)\n",
    "OKC1213DIFF = OKCFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 2\n",
    "SA1213 = season1213[season1213['teamAbbr'] == 'SA']\n",
    "SAFTA1213 = SA1213['teamFTA'].sum() / len(SA1213)\n",
    "SA1213DIFF = SAFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 3\n",
    "DEN1213 = season1213[season1213['teamAbbr'] == 'DEN']\n",
    "DENFTA1213 = DEN1213['teamFTA'].sum() / len(DEN1213)\n",
    "DEN1213DIFF = DENFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 4\n",
    "LAC1213 = season1213[season1213['teamAbbr'] == 'LAC']\n",
    "LACFTA1213 = LAC1213['teamFTA'].sum() / len(LAC1213)\n",
    "LAC1213DIFF = LACFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 5\n",
    "MEM1213 = season1213[season1213['teamAbbr'] == 'MEM']\n",
    "MEMFTA1213 = MEM1213['teamFTA'].sum() / len(MEM1213)\n",
    "MEM1213DIFF = MEMFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 6\n",
    "GS1213 = season1213[season1213['teamAbbr'] == 'GS']\n",
    "GSFTA1213 = GS1213['teamFTA'].sum() / len(GS1213)\n",
    "GS1213DIFF = GSFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 7\n",
    "LAL1213 = season1213[season1213['teamAbbr'] == 'LAL']\n",
    "LALFTA1213 = LAL1213['teamFTA'].sum() / len(LAL1213)\n",
    "LAL1213DIFF = LALFTA1213 - leagueAVG1213;\n",
    "\n",
    "# Seed 8\n",
    "HOU1213 = season1213[season1213['teamAbbr'] == 'HOU']\n",
    "HOUFTA1213 = HOU1213['teamFTA'].sum() / len(HOU1213)\n",
    "HOU1213DIFF = HOUFTA1213 - leagueAVG1213;\n",
    "\n",
    "West1213Top8 = [OKCFTA1213, SAFTA1213, DENFTA1213, LACFTA1213, MEMFTA1213, GSFTA1213, LALFTA1213, HOUFTA1213]\n",
    "West1213Top8DIFF = [OKC1213DIFF, SA1213DIFF, DEN1213DIFF, LAC1213DIFF, MEM1213DIFF, GS1213DIFF, LAL1213DIFF, HOU1213DIFF]\n",
    "\n",
    "leagueAVG1213Index = [leagueAVG1213, leagueAVG1213, leagueAVG1213, leagueAVG1213, leagueAVG1213, leagueAVG1213, leagueAVG1213, leagueAVG1213]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Season 17-18 teams making playoffs\n",
    "\n",
    "# Eastern Conference\n",
    "\n",
    "# Seed 1\n",
    "TOR1718 = season1718[season1718['teamAbbr'] == 'TOR']\n",
    "TORFTA1718 = TOR1718['teamFTA'].sum() / len(TOR1718)\n",
    "TOR1718DIFF = TORFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 2\n",
    "BOS1718 = season1718[season1718['teamAbbr'] == 'BOS']\n",
    "BOSFTA1718 = BOS1718['teamFTA'].sum() / len(BOS1718)\n",
    "BOS1718DIFF = BOSFTA1718 - leagueAVG1718;\n",
    "\n",
    "\n",
    "# Seed 3\n",
    "PHI1718 = season1718[season1718['teamAbbr'] == 'PHI']\n",
    "PHIFTA1718 = PHI1718['teamFTA'].sum() / len(PHI1718)\n",
    "PHI1718DIFF = PHIFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 4\n",
    "CLE1718 = season1718[season1718['teamAbbr'] == 'CLE']\n",
    "CLEFTA1718 = CLE1718['teamFTA'].sum() / len(CLE1718)\n",
    "CLE1718DIFF = CLEFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 5\n",
    "IND1718 = season1718[season1718['teamAbbr'] == 'IND']\n",
    "INDFTA1718 = IND1213['teamFTA'].sum() / len(IND1718)\n",
    "IND1718DIFF = INDFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 6\n",
    "MIA1718 = season1718[season1718['teamAbbr'] == 'MIA']\n",
    "MIAFTA1718 = MIA1213['teamFTA'].sum() / len(MIA1718)\n",
    "MIA1718DIFF = MIAFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 7\n",
    "MIL1718 = season1718[season1718['teamAbbr'] == 'MIL']\n",
    "MILFTA1718 = MIL1213['teamFTA'].sum() / len(MIL1718)\n",
    "MIL1718DIFF = MILFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 8\n",
    "WAS1718 = season1718[season1718['teamAbbr'] == 'WAS']\n",
    "WASFTA1718 = WAS1718['teamFTA'].sum() / len(WAS1718)\n",
    "WAS1718DIFF = WASFTA1718 - leagueAVG1718;\n",
    "\n",
    "East1718Top8 = [TORFTA1718, BOSFTA1718, PHIFTA1718, CLEFTA1718, INDFTA1718, MIAFTA1718, MILFTA1718, WASFTA1718]\n",
    "East1718Top8DIFF = [TOR1718DIFF, BOS1718DIFF, PHI1718DIFF, CLE1718DIFF, IND1718DIFF, MIA1718DIFF, MIL1718DIFF, WAS1718DIFF]\n",
    "\n",
    "# Western Conference\n",
    "\n",
    "# Seed 1\n",
    "HOU1718 = season1718[season1718['teamAbbr'] == 'HOU']\n",
    "HOUFTA1718 = HOU1718['teamFTA'].sum() / len(HOU1718)\n",
    "HOU1718DIFF = HOUFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 2\n",
    "GS1718 = season1718[season1718['teamAbbr'] == 'GS']\n",
    "GSFTA1718 = GS1718['teamFTA'].sum() / len(GS1718)\n",
    "GS1718DIFF = GSFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 3\n",
    "POR1718 = season1718[season1718['teamAbbr'] == 'POR']\n",
    "PORFTA1718 = POR1718['teamFTA'].sum() / len(POR1718)\n",
    "POR1718DIFF = PORFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 4\n",
    "OKC1718 = season1718[season1718['teamAbbr'] == 'OKC']\n",
    "OKCFTA1718 = OKC1718['teamFTA'].sum() / len(OKC1718)\n",
    "OKC1718DIFF = OKCFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 5\n",
    "UTA1718 = season1718[season1718['teamAbbr'] == 'UTA']\n",
    "UTAFTA1718 = UTA1718['teamFTA'].sum() / len(UTA1718)\n",
    "UTA1718DIFF = UTAFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 6\n",
    "NO1718 = season1718[season1718['teamAbbr'] == 'NO']\n",
    "NOFTA1718 = NO1718['teamFTA'].sum() / len(NO1718)\n",
    "NO1718DIFF = NOFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 7\n",
    "SA1718 = season1718[season1718['teamAbbr'] == 'SA']\n",
    "SAFTA1718 = SA1718['teamFTA'].sum() / len(SA1718)\n",
    "SA1718DIFF = SAFTA1718 - leagueAVG1718;\n",
    "\n",
    "# Seed 8\n",
    "MIN1718 = season1718[season1718['teamAbbr'] == 'MIN']\n",
    "MINFTA1718 = MIN1718['teamFTA'].sum() / len(MIN1718)\n",
    "MIN1718DIFF = MINFTA1718 - leagueAVG1718;\n",
    "\n",
    "West1718Top8 = [HOUFTA1718, GSFTA1718, PORFTA1718, OKCFTA1718, UTAFTA1718, NOFTA1718, SAFTA1718, MINFTA1718]\n",
    "West1718Top8DIFF = [HOU1718DIFF, GS1718DIFF, POR1718DIFF, OKC1718DIFF, UTA1718DIFF, NO1718DIFF, SA1718DIFF, MIN1718DIFF]\n",
    "\n",
    "leagueAVG1718Index = [leagueAVG1718, leagueAVG1718, leagueAVG1718, leagueAVG1718, leagueAVG1718, leagueAVG1718, leagueAVG1718, leagueAVG1718]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for correlation in free throws per game between teams making the playoffs and the league average of 2012-2013 and 2017-2018 season (Comparing averages and the differences):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "playOffSeed = ['Seed 1', 'Seed 2', 'Seed 3', 'Seed 4', 'Seed 5', 'Seed 6', 'Seed 7', 'Seed 8']\n",
    "\n",
    "# create plt\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(8)\n",
    "bar_width = 0.3\n",
    "opacity = 0.8\n",
    "\n",
    "rects1 = plt.bar(index, East1213Top8 , bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Playoff Teams')\n",
    "\n",
    "rects2 = plt.bar(index + bar_width, leagueAVG1213Index, bar_width,\n",
    "alpha=opacity,\n",
    "color='r',\n",
    "label='League Average')\n",
    "\n",
    "plt.xlabel('Playoff Seeds')\n",
    "plt.ylabel('Free Throws Per Game')\n",
    "plt.title('Eastern Conference Playoff Teams 12-13')\n",
    "plt.xticks(index + bar_width-.17, playOffSeed)\n",
    "plt.legend()\n",
    "plt.figure(figsize=(15,15))\n",
    "ax.legend(loc='best', bbox_to_anchor=(1,1))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 3 above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "playOffSeed = ['Seed 1', 'Seed 2', 'Seed 3', 'Seed 4', 'Seed 5', 'Seed 6', 'Seed 7', 'Seed 8']\n",
    "\n",
    "# create plt\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(8)\n",
    "bar_width = 0.3\n",
    "opacity = 0.8\n",
    "\n",
    "rects1 = plt.bar(index, East1718Top8 , bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Playoff Teams')\n",
    "\n",
    "rects2 = plt.bar(index + bar_width, leagueAVG1718Index, bar_width,\n",
    "alpha=opacity,\n",
    "color='r',\n",
    "label='League Average')\n",
    "\n",
    "plt.xlabel('Playoff Seeds')\n",
    "plt.ylabel('Free Throws Per Game')\n",
    "plt.title('Eastern Conference Playoff Teams 17-18')\n",
    "plt.xticks(index + bar_width-.17, playOffSeed)\n",
    "plt.legend()\n",
    "plt.figure(figsize=(15,15))\n",
    "ax.legend(loc='best', bbox_to_anchor=(1,1))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 3 & 4: These two bar graphs show the average of free throws made by each team that made it to Eastern Conference playoffs compared to the regular season average of free throws made in 2012-2013 in plot 3 and 2017-2018 in plot 4. From the plot we can see that not all the teams that made it to playoffs had above average free throws made even though these teams are the 8 teams with the highest rate of wins in that season. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "index = np.arange(8)\n",
    "bar_width = 0.3\n",
    "opacity = 0.8\n",
    "\n",
    "rects1 = plt.bar(index, West1213Top8 , bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Playoff Teams')\n",
    "\n",
    "rects2 = plt.bar(index + bar_width, leagueAVG1213Index, bar_width,\n",
    "alpha=opacity,\n",
    "color='r',\n",
    "label='League Average')\n",
    "\n",
    "plt.xlabel('Playoff Seeding')\n",
    "plt.ylabel('Free throws Per Game')\n",
    "plt.title('Western Conference Playoff Teams 12-13')\n",
    "plt.xticks(index + bar_width-.17, playOffSeed)\n",
    "plt.legend()\n",
    "plt.figure(figsize=(15,15))\n",
    "ax.legend(loc='best', bbox_to_anchor=(1,1))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 5 above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "index = np.arange(8)\n",
    "bar_width = 0.3\n",
    "opacity = 0.8\n",
    "\n",
    "rects1 = plt.bar(index, West1718Top8 , bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Playoff Teams')\n",
    "\n",
    "rects2 = plt.bar(index + bar_width, leagueAVG1718Index, bar_width,\n",
    "alpha=opacity,\n",
    "color='r',\n",
    "label='League Average')\n",
    "\n",
    "plt.xlabel('Playoff Seeding')\n",
    "plt.ylabel('Free throws Per Game')\n",
    "plt.title('Western Conference Playoff Teams 17-18')\n",
    "plt.xticks(index + bar_width-.17, playOffSeed)\n",
    "plt.legend()\n",
    "plt.figure(figsize=(15,15))\n",
    "ax.legend(loc='best', bbox_to_anchor=(1,1))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 5 & 6: These two bar graphs show the average of free throws made by each team that made it to Western Conference playoffs compared to the regular season average of free throws made in 2012-2013 in plot 3 and 2017-2018 in plot 4. From the plot we can see that not all the teams that made it to playoffs had above average free throws made even though these teams are the 8 teams with the highest rate of wins in that season. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "playOffSeed = ['Seed 1', 'Seed 2', 'Seed 3', 'Seed 4', 'Seed 5', 'Seed 6', 'Seed 7', 'Seed 8']\n",
    "\n",
    "# create plt\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(8)\n",
    "bar_width = 0.3\n",
    "opacity = 0.8\n",
    "\n",
    "rects1 = plt.bar(index, East1213Top8DIFF , bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Playoff Teams')\n",
    "\n",
    "#rects2 = plt.bar(index + bar_width, leagueAVGIndex, bar_width,\n",
    "# alpha=opacity,\n",
    "# color='r',\n",
    "# label='League Average')\n",
    "\n",
    "plt.xlabel('Playoff Seeds')\n",
    "plt.ylabel('Free Throws Per Game')\n",
    "plt.title('East Conference Playoff Teams 12-13 FT to League AVG')\n",
    "plt.xticks(index + bar_width-.17, playOffSeed)\n",
    "plt.legend()\n",
    "plt.figure(figsize=(15,15))\n",
    "ax.legend(loc='best', bbox_to_anchor=(1,1))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 7 above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "playOffSeed = ['Seed 1', 'Seed 2', 'Seed 3', 'Seed 4', 'Seed 5', 'Seed 6', 'Seed 7', 'Seed 8']\n",
    "\n",
    "# create plt\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(8)\n",
    "bar_width = 0.3\n",
    "opacity = 0.8\n",
    "\n",
    "rects1 = plt.bar(index, East1718Top8DIFF , bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Playoff Teams')\n",
    "\n",
    "#rects2 = plt.bar(index + bar_width, leagueAVGIndex, bar_width,\n",
    "# alpha=opacity,\n",
    "# color='r',\n",
    "# label='League Average')\n",
    "\n",
    "plt.xlabel('Playoff Seeds')\n",
    "plt.ylabel('Free Throws Per Game')\n",
    "plt.title('East Conference Playoff Teams 17-18 FT to League AVG')\n",
    "plt.xticks(index + bar_width-.17, playOffSeed)\n",
    "plt.legend()\n",
    "plt.figure(figsize=(15,15))\n",
    "ax.legend(loc='best', bbox_to_anchor=(1,1))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 7 & 8: These two bar graphs represent the same concept as plot 3 & 4. However, in order to be able to better visualize the results, the average of free throws made by each playoff team was subtracted by the league average hence explaining the negative values. The negative values indicates the Eastern Conference teams that made it to playoffs but were below average when it comes to free throws attempted.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "index = np.arange(8)\n",
    "bar_width = 0.3\n",
    "opacity = 0.8\n",
    "\n",
    "rects1 = plt.bar(index, West1213Top8DIFF , bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Playoff Teams')\n",
    "\n",
    "# rects2 = plt.bar(index + bar_width, leagueAVGIndex, bar_width,\n",
    "# alpha=opacity,\n",
    "# color='r',\n",
    "# label='League Average')\n",
    "\n",
    "plt.xlabel('Playoff Seeding')\n",
    "plt.ylabel('Free throws Per Game')\n",
    "plt.title('West Conference Playoff Teams 12-13 FT to League AVG')\n",
    "plt.xticks(index + bar_width-.17, playOffSeed)\n",
    "plt.legend()\n",
    "plt.figure(figsize=(15,15))\n",
    "ax.legend(loc='best', bbox_to_anchor=(1,1))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 9 above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "index = np.arange(8)\n",
    "bar_width = 0.3\n",
    "opacity = 0.8\n",
    "\n",
    "rects1 = plt.bar(index, West1718Top8DIFF , bar_width,\n",
    "alpha=opacity,\n",
    "color='b',\n",
    "label='Playoff Teams')\n",
    "\n",
    "# rects2 = plt.bar(index + bar_width, leagueAVGIndex, bar_width,\n",
    "# alpha=opacity,\n",
    "# color='r',\n",
    "# label='League Average')\n",
    "\n",
    "plt.xlabel('Playoff Seeding')\n",
    "plt.ylabel('Free throws Per Game')\n",
    "plt.title('West Conference Playoff Teams 17-18 FT to League AVG ')\n",
    "plt.xticks(index + bar_width-.17, playOffSeed)\n",
    "plt.legend()\n",
    "plt.figure(figsize=(15,15))\n",
    "ax.legend(loc='best', bbox_to_anchor=(1,1))\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 9 & 10: These two bar graphs represent the same concept as plot 5 & 6. However, in order to be able to better visualize the results, the average of free throws made by each playoff team was subtracted by the league average hence explaining the negative values. The negative values indicates the Western Conference teams that made it to playoffs but were below average when it comes to free throws attempted.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2012-2013 T-Test Data\n",
    "\n",
    "East1213Stats = scipy.stats.ttest_1samp(East1213Top8, leagueAVG1213)\n",
    "West1213Stats = scipy.stats.ttest_1samp(West1213Top8, leagueAVG1213)\n",
    "\n",
    "dataEast1213 = [[East1213Stats[0], East1213Stats[1]/2]]\n",
    "East1213_statTable = pd.DataFrame(dataEast1213, columns=['Test Statistic', 'p-value'])\n",
    "\n",
    "dataWest1213 = [[West1213Stats[0], West1213Stats[1]/2]]\n",
    "West1213_statTable = pd.DataFrame(dataWest1213, columns=['Test Statistic', 'p-value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2017-2018 T-Test Data\n",
    "\n",
    "East1718Stats = scipy.stats.ttest_1samp(East1718Top8, leagueAVG1718)\n",
    "West1718Stats = scipy.stats.ttest_1samp(West1718Top8, leagueAVG1718)\n",
    "\n",
    "dataEast1718 = [[East1718Stats[0], East1718Stats[1]/2]]\n",
    "East1718_statTable = pd.DataFrame(dataEast1718, columns=['Test Statistic', 'p-value'])\n",
    "\n",
    "dataWest1718 = [[West1718Stats[0], West1718Stats[1]/2]]\n",
    "West1718_statTable = pd.DataFrame(dataWest1718, columns=['Test Statistic', 'p-value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "East Playoff Teams 12-13 T-Test Results:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test Statistic</th>\n",
       "      <th>p-value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.759076</td>\n",
       "      <td>0.236294</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Test Statistic   p-value\n",
       "0       -0.759076  0.236294"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "East1213_statTable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "East Playoff Teams 17-18 T-Test Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Test Statistic   p-value\n",
       "0        1.314815  0.115004"
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     "execution_count": 26,
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   "source": [
    "East1718_statTable"
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   "cell_type": "markdown",
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   "source": [
    "West Playoff Teams 12-13 T-Test Results:"
   ]
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   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Test Statistic   p-value\n",
       "0        1.961229  0.045328"
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     "execution_count": 27,
     "metadata": {},
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   "source": [
    "West1213_statTable"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "West Playoff Teams 17-18 T-Test Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Test Statistic   p-value\n",
       "0        0.876004  0.205039"
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     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "West1718_statTable"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "T-test Explanation: We decided to go more in depth about whether there was a correlation between the league average free throw attempts and the average free throw attempts for making the playoffs(top 8) in the Eastern and Western Conferences for the first season of our data set(2012-2013) and the last season of our data set(2017-2018). We carried this out by running a one-sample T-Test on the league average free throw attempts per game and either the top 8 teams of the Western Conference or Eastern Conference in a respective season. After getting the p-value from doing the T-Test, we compare it to our alpha value of 0.05. We discovered that for the top 8 Eastern conference teams from the 2012-2013 and 2017-2018 season and the Western Conference playoff teams from the 2017-2018 season, there was no correlation between free throw attempts and making the playoffs as the p-values were all way above .05 (i.e .236, .115, .205). However, the test for the top 8 Western Conference teams of the 2012-2013 season was below .05 (0.04) but we see this as a coincidence.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics and Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we were not violating any ethical codes because we are only analyzing basketball data that has been made public by the NBA. The dataset that was chosen is official box score statistics which is simply every piece of statistical data from an NBA game with no bias as it just records all pieces of data from the game. One unintended consequence that our findings could have is that the NBA teams could use this analysis in order to strategize when it comes to committing fouls and giving away free throws since we have proven that more free throw attempts does not correlate with more wins. “Intentionally fouling” strategy already exists where teams that are behind intentionally commit fouls by the end of the game as soon as the other team gains possession in the hopes that the opposition would miss their free throws and therefore we believe that taking steps to guard our findings is redundant. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion\n",
    "Our results indicate that there is a higher chance of winning a single game with more number of free throws attempted but there is no correlation between having more free throws leading into a higher win percentage during the regular season. It was shown that our hypothesis was partly inaccurate since we expected that playoff teams who have the most wins during the regular season to have more average free throws attempted compared to the league average since more free throws in a game results in a higher chance of winning that game but it was proven otherwise. In the seasons that we analyzed there were quite a few seeds that were below the league average of free throws in both conferences. \n",
    "Some limitations of our analysis was that we only went truly in depth on the first and last seasons of our data set to see the correlation between winning enough games to make the playoffs and the amount of free throw attempts per game. \n"
   ]
  }
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