{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NBA Team Net Ratings\n",
    "\n",
    "Let's do some basic exploritory analysis on NBA games.\n",
    "\n",
    "Each basketball game is won by a margin, say 8 points. To normalize for the pace of the game, there's \"net rating\" which is the delta in points scored per 100 possessions. This makes different games that were played at faster or slower paces comparable. A team's average net rating is the best single number for quantifying performance (save the win/loss record).\n",
    "\n",
    "To start with we need a record of every NBA game this season and its net rating.\n",
    "\n",
    "I boiled this down into a csv with the date, team name and net rating (each game will have two entries, but this is ok as we only look at per team stats). This csv was produced with a separate ruby script, but if you are curious, these two JSON endpoints from stats.nba.com were used:\n",
    "\n",
    "- [The season game log](http://stats.nba.com/stats/leaguegamelog?Counter=1500&Direction=DESC&LeagueID=00&PlayerOrTeam=T&Season=2015-2016&SeasonType=Regular+Season&Sorter=PTS)\n",
    "- [The advanced box score for a single game](http://stats.nba.com/stats/boxscoreadvancedv2?EndPeriod=10&EndRange=40800&GameID=0021500391&RangeType=0&Season=2015-16&SeasonType=Regular+Season&StartPeriod=1&StartRange=0)\n",
    "\n",
    "Note that these endpoints are unofficial at best, and were sniffed out by looking at XHR requests in Chrome's developer tools while browsing stats.nba.com.\n",
    "\n",
    "Let's load the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import itertools\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "nba_games = []\n",
    "fname = 'nba-games.csv'\n",
    "with open(fname,\"r\") as f:\n",
    "    # game_id,game_date,team_id,team_name,net_rating\n",
    "    reader = csv.reader(f)\n",
    "    next(reader)\n",
    "    nba_games = list(reader)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, a function that extracts the net rating for a team. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def team_net_ratings(the_team_name):\n",
    "    \"\"\"\n",
    "    team name is one of \n",
    "    [\"76ers\", \"Bucks\", \"Bulls\", \"Cavaliers\", \"Celtics\", \n",
    "    \"Clippers\", \"Grizzlies\", \"Hawks\", \"Heat\", \n",
    "    \"Hornets\", \"Jazz\", \"Kings\", \"Knicks\", \"Lakers\", \n",
    "    \"Magic\", \"Mavericks\", \"Nets\", \"Nuggets\", \"Pacers\", \n",
    "    \"Pelicans\", \"Pistons\", \"Raptors\", \"Rockets\", \"Spurs\", \n",
    "    \"Suns\", \"Thunder\", \"Timberwolves\", \"Trail Blazers\", \"Warriors\", \"Wizards\"]    \n",
    "    \"\"\"\n",
    "    return [\n",
    "        float(net_rating)\n",
    "        for game_id,game_date,team_id,team_name,net_rating in nba_games \n",
    "        if team_name == the_team_name]\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Pistons\n",
    "\n",
    "With this, we can make a histogram of the net ratings for a team. I like the Pistons, so let's check them out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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kzd48JwGfBc4CHh8Rd+4NjIgtgDdU/753FoFJkiRJs7TWnhj8cODh1b/XBx5A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\nzMzMauZ/m5FYFuy5hkkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107e53d68>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 280,
       "width": 384
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.mlab as mlab\n",
    "\n",
    "plt.hist(team_net_ratings('Pistons'), bins=15)\n",
    "plt.title(\"Pistons Net Rating for 2015/2016 Games\")\n",
    "plt.xlabel(\"Net Rating\")\n",
    "plt.ylabel(\"Num Games\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As experienced as a fan this year, we are a bit bi-modal, sometimes playing great, even beating some of the leagu's best teams, other times getting blown out (that -40 net rating was most recently against The Wizards).\n",
    "\n",
    "### Best and worst teams\n",
    "\n",
    "Now let's compare this to the best and worst teams in the league"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fO++8k5kzZxIRjBkzhk033ZQDDjiAY489ltGjR7f317XGMQCQJK00bVrP57kG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\nJKmXmzdvHrfccgtQfvAvZPP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AgxFxIfAolacBnV6lPCVJkiR1ULUCgLuLfv52K+kS0K9K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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107f0c0b8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 280,
       "width": 384
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(team_net_ratings(\"Warriors\"), bins=15, color='b', label='Warriors')\n",
    "plt.hist(team_net_ratings(\"76ers\"), bins=15, color='r', alpha=0.5, label='76ers')\n",
    "plt.title(\"Net Rating for 2015/2016 Games\")\n",
    "plt.xlabel(\"Net Rating\")\n",
    "plt.ylabel(\"Num Games\")\n",
    "plt.legend()\n",
    "plt.show()    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yep, the warriors usually win, and the 76ers usually lose. Still striking to see how many games the warriors win by a safe margin.\n",
    "\n",
    "### Box Plots\n",
    "\n",
    "Box plots are a nice way to visually compare multiple team's distributions giving a quick snapshot of the median, range and interquartile range. Let's compare the top 9 seeds in the Eastern Conference (I'm hoping the Pistons fight their way into the 8th seed, they are currently 9th)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZBvg88ETgFVV1cFVVklsBpwEvAs4F/hl4N/Ad4PHAzgt/mpIkSdJ0m7b7oKSd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\nzKxnuIJiZmZmZmY94/8B+FAG5OdjaGYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1084c98d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 404
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def box_plot_teams(team_names):\n",
    "    reversed_names = list(reversed(team_names))\n",
    "    data = [team_net_ratings(team_name) for team_name in reversed_names]\n",
    "    plt.figure()\n",
    "    plt.xlabel('Game Net Ratings')\n",
    "    plt.boxplot(data, labels=reversed_names, vert=False)\n",
    "    plt.show()\n",
    "    \n",
    "box_plot_teams(['Cavaliers', 'Raptors', 'Celtics', 'Heat', 'Hawks', 'Hornets', 'Pacers', 'Bulls', 'Pistons'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that The Pistons have the largest spread, but have a median slightly better than The Bulls (they are in fact neck and neck) with potentially more upside. The 3rd quartile net rating of close to 10 is what makes us Pistons fans feel like we could have a shot against most teams in The Eastern Conference.\n",
    "\n",
    "Another note: the standard boxplot plots the dashed line up to 1.5 the IQR range, beyond that data points are considered outliers and plotted individually. The Pistons do not have any outliers by this standard; so on a given night we can get blown out or win big and it shouldn't really surprise use :)\n",
    "\n",
    "Finally, let's look at the mean and standard deviations of each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Cavaliers', 6.1484848484848493, 13.741699890702536),\n",
       " ('Raptors', 4.3430769230769233, 11.409415399717622),\n",
       " ('Celtics', 3.6560606060606062, 12.31363039773748),\n",
       " ('Heat', 2.1358208955223885, 13.594969454742598),\n",
       " ('Hawks', 4.1597014925373132, 14.22600162726523),\n",
       " ('Hornets', 2.5166666666666671, 13.604918710941023),\n",
       " ('Pacers', 1.7560606060606059, 12.550252065413581),\n",
       " ('Bulls', -1.5261538461538462, 13.330615738829598),\n",
       " ('Pistons', -0.50447761194029828, 14.165223684804399)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mean_std(team_name):\n",
    "    nrs = team_net_ratings(team_name)\n",
    "    return (team_name, np.mean(nrs), np.std(nrs))\n",
    "\n",
    "[mean_std(team_name) for team_name in \n",
    " ['Cavaliers', 'Raptors', 'Celtics', 'Heat', 'Hawks', 'Hornets', 'Pacers', 'Bulls', 'Pistons']]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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