{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NBA Home Court Advantage\n",
    "\n",
    "### Part 1: A First Look at Win Percentages and Point Advantages\n",
    "\n",
    "In this notebook, we'll take a short look at home court advantage in the NBA. The analysis will use data from the 1996-97 season through the 2016-17 season. We scraped this data from [stats.nba.com](http://stats.nba.com/) in [this notebook](http://nbviewer.jupyter.org/github/practicallypredictable/posts/blob/master/notebooks/scrape-stats_nba-team_matchups.ipynb).\n",
    "\n",
    "Let's first import the packages we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.options.display.max_rows = 999\n",
    "pd.options.display.max_columns = 999\n",
    "pd.options.display.float_format = '{:.3f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use [`seaborn`](https://seaborn.pydata.org/) and [`matplotlib`](https://matplotlib.org/) for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set_context('notebook')\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the statistics module from the [`scipy`](https://www.scipy.org/) package in a plot at the end of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_DIR = Path.cwd().parent / 'basketball' / 'nba'\n",
    "INPUT_DIR = PROJECT_DIR / 'data' / 'prepared'\n",
    "INPUT_DIR.mkdir(exist_ok=True, parents=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the historical matchups we scraped in the previous notebook. In that notebook, the scraped data were saved using Python's [pickle](https://docs.python.org/3/library/pickle.html) format. Refer to the [earlier notebook](http://nbviewer.jupyter.org/github/practicallypredictable/posts/blob/master/notebooks/scrape-stats_nba-team_matchups.ipynb) for more information if you need to scape the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_nba_historical_matchups(input_dir):\n",
    "    \"\"\"Load pickle file of NBA matchups prepared for analytics.\"\"\"\n",
    "    PKLFILENAME = 'stats_nba_com-matchups-1996_97-2016_17.pkl'\n",
    "    pklfile = input_dir.joinpath(PKLFILENAME)\n",
    "    return pd.read_pickle(pklfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26787, 41)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matchups = load_nba_historical_matchups(INPUT_DIR)\n",
    "matchups.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 21 complete seasons of matchup data. Let's just use the regular season matchups for this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons = sorted(list(matchups['season'].unique()))\n",
    "len(seasons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_regular_season(matchups):\n",
    "    df = matchups.copy()\n",
    "    df = df[df['season_type'] == 'regular']\n",
    "    df['pt_diff'] = df['pts_h'] - df['pts_a']\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24797, 42)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = prepare_regular_season(matchups)\n",
    "reg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def home_win_percentage(df):\n",
    "    games = len(df)\n",
    "    if games > 0:\n",
    "        return float(df['won'].value_counts()['H'] / games)\n",
    "    else:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5980562164777997"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_win_percentage(reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The home team has won almost 60% of home games over the past 21 complete seasons. Of course, to win basketball games, you have to score more than the other team. Let's examine what this means in terms of point differentials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def home_court_analysis(df):\n",
    "    seasons = sorted(list(df['season'].unique()))\n",
    "    home_win_pct = [\n",
    "        home_win_percentage(df.loc[df['season'] == season]) for season in seasons\n",
    "    ]\n",
    "    pt_diff_mean = [\n",
    "        df.loc[df['season'] == season, 'pt_diff'].mean() for season in seasons\n",
    "    ]\n",
    "    pt_diff_std = [\n",
    "        df.loc[df['season'] == season, 'pt_diff'].std() for season in seasons\n",
    "    ]\n",
    "    return pd.DataFrame({\n",
    "        'season': seasons,\n",
    "        'home_win_pct': home_win_pct,\n",
    "        'pt_diff_mean': pt_diff_mean,\n",
    "        'pt_diff_std': pt_diff_std,\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>home_win_pct</th>\n",
       "      <th>pt_diff_mean</th>\n",
       "      <th>pt_diff_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21.000</td>\n",
       "      <td>21.000</td>\n",
       "      <td>21.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.598</td>\n",
       "      <td>3.105</td>\n",
       "      <td>12.989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.015</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.575</td>\n",
       "      <td>2.407</td>\n",
       "      <td>12.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.589</td>\n",
       "      <td>2.820</td>\n",
       "      <td>12.669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.598</td>\n",
       "      <td>3.149</td>\n",
       "      <td>13.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.608</td>\n",
       "      <td>3.399</td>\n",
       "      <td>13.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.628</td>\n",
       "      <td>3.884</td>\n",
       "      <td>13.686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       home_win_pct  pt_diff_mean  pt_diff_std\n",
       "count        21.000        21.000       21.000\n",
       "mean          0.598         3.105       12.989\n",
       "std           0.015         0.385        0.474\n",
       "min           0.575         2.407       12.030\n",
       "25%           0.589         2.820       12.669\n",
       "50%           0.598         3.149       13.057\n",
       "75%           0.608         3.399       13.342\n",
       "max           0.628         3.884       13.686"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hca = home_court_analysis(reg)\n",
    "hca.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over this 21 season period, the home team on average scored an extra 3.1 points per game by virtue of playing at home. Let's look at how this has fluctuated season-by-season over the period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a81e080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(10, 7))\n",
    "hca.plot(y='home_win_pct', ax=ax1, legend=False)\n",
    "ax1.set_title('Home Team Win Percentage')\n",
    "ax1.set_ylabel('Win Percentage')\n",
    "hca.plot(y='pt_diff_mean', ax=ax2, legend=False)\n",
    "ax2.set_title('Home Team Point Advantage')\n",
    "ax2.set_ylabel('Point Differential')\n",
    "plt.xticks(range(len(hca.season)), hca.season, rotation=90)\n",
    "plt.xlabel('NBA Season')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that there is meaningful variation season-by-season. There may seem to be a hint of a downward trend in the home court win percentage. However, the win percentage is still well within the normal range of variation over this historical period. And looked at in terms of point differential, home court advantage certainly doesn't seem to be trending lower.\n",
    "\n",
    "[NBA home court advantage was even high in the 1970s](https://fivethirtyeight.com/features/home-field-advantage-english-premier-league/), but we are not going to dwell on the earlier time period at this point. Basketball as played in the NBA has changed a lot since the 1970s.\n",
    "\n",
    "Now let's focus on just the most recent completed season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2016-17'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season = seasons[-1]\n",
    "season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = reg[reg['season'] == season]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   1230.000\n",
       "mean       3.149\n",
       "std       13.686\n",
       "min      -37.000\n",
       "25%       -6.000\n",
       "50%        4.000\n",
       "75%       12.000\n",
       "max       49.000\n",
       "Name: pt_diff, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pt_diff'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the average home team point differential was 3.15 points for the 2016-17 season, there was a lot of dispersion around this average. Let's plot the point differential."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bins(df):\n",
    "    min_diff = df['pt_diff'].min()\n",
    "    max_diff = df['pt_diff'].max()\n",
    "    return np.arange(2*(round(min_diff/2)-1), 2*(round(max_diff/2)+2), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_score_diff(matchups, ax, season=None):\n",
    "    if season:\n",
    "        df = matchups[(matchups['season'] == season)].dropna()\n",
    "    else:\n",
    "        df = matchups\n",
    "    ax = sns.distplot(df['pt_diff'], bins=get_bins(df), kde=False, fit=stats.norm, ax=ax)\n",
    "    ax.set_xlabel('Point Differential (Home Team Score - Away Team Score)')\n",
    "    ax.set_ylabel('Frequency')\n",
    "    if season:\n",
    "        title = f'Point Differential Frequency for {season} Season'\n",
    "    else:\n",
    "        title = f'Point Differential Frequency (All Seasons)'\n",
    "    ax.set_title(title)\n",
    "    ax.text(x=35, y=0.008, s='Bins are 2 points wide')\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af590f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 6))\n",
    "ax = plot_score_diff(reg, ax, season='2016-17')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The curve overlaid on the plot is the bell-shaped [_normal distribution_](https://en.wikipedia.org/wiki/Normal_distribution) that has the same [mean](https://en.wikipedia.org/wiki/Mean) and [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) as the point differential data. Notice that we used the `scipy` `stats.norm` function with `seaborn` to automatically fit the normal curve to the data.\n",
    "\n",
    "The fitted curve does a reasonable job of fitting the data away from the center of the distribution, but it does a poor job for the center. Since basketball games can't end in ties, zero is not an allowed point differential. Also, we see that only a little more than 1% of games end within a 2 point margin of victory. So, the normal distribution is too high for small margins of victory, and too low for typical margins of victory.\n",
    "\n",
    "This suggests that will need to be very careful about how we choose to model and predict NBA point differentials.\n",
    "\n",
    "To make sure this effect isn't just a coincidence for the 2016-17 season, let's generate the same plot for the entire data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b288438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 6))\n",
    "ax = plot_score_diff(reg, ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get the same basic message looking at all 21 seasons. The data look very \"normal\" away from the center, but aren't in the middle of the distribution. The normal distribution should give you a reasonable approximation for the probability of big blowouts, but doesn't do an adequate job for typical games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:sports_py36]",
   "language": "python",
   "name": "conda-env-sports_py36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}