voronoi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.spatial import Voronoi, voronoi_plot_2d\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Credit: Savvas Tjortjoglou  http://savvastjortjoglou.com/nba-shot-sharts.html\n",
    "# Josh Shifman modified the above code in our coordinate sytems\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Circle, Rectangle, Arc, CirclePolygon\n",
    "# Credit: Savvas Tjortjoglou  http://savvastjortjoglou.com/nba-shot-sharts.html\n",
    "\n",
    "def draw_court(ax=None, color='black', lw=2, outer_lines=False):\n",
    "    \n",
    "\n",
    "    # If an axes object isn't provided to plot onto, just get current one\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "\n",
    "    # Create the various parts of an NBA basketball court\n",
    "\n",
    "    # Create the basketball hoop\n",
    "    # Diameter of a hoop is 18\" so it has a radius of 9\", which is a value\n",
    "    # 7.5 in our coordinate system\n",
    "    hoop = Circle((4, 25), radius=.75, linewidth=lw, color=color, fill=False)\n",
    "\n",
    "    # Create backboard\n",
    "    backboard = Rectangle((4, 22), -.5, 6, linewidth=lw, color=color)\n",
    "\n",
    "    # The paint\n",
    "    # Create the outer box 0f the paint, width=16ft, height=19ft\n",
    "    outer_box = Rectangle((0, 17), 18, 16, linewidth=lw, color=color,\n",
    "                          fill=False)\n",
    "    \n",
    "    # Create free throw top arc\n",
    "    top_free_throw = CirclePolygon((19, 25), radius=5.5, linewidth=lw, color=color, fill=False)\n",
    "    \n",
    "    # Three point line\n",
    "    # Create the side 3pt lines, they are 14ft long before they begin to arc\n",
    "    corner_three_a = Rectangle((0, 3), 14, 0, linewidth=lw,color=color)\n",
    "    corner_three_b = Rectangle((0, 47), 14, 0, linewidth=lw,color=color)\n",
    "    # 3pt arc - center of arc will be the hoop, arc is 23'9\" away from hoop\n",
    "    # I just played around with the theta values until they lined up with the \n",
    "    # threes\n",
    "    three_arc = Arc((4, 25), 47.5, 47.5, theta1=-68, theta2=68, linewidth=lw, color=color)\n",
    "\n",
    "\n",
    "    # List of the court elements to be plotted onto the axes\n",
    "    court_elements = [hoop, backboard, outer_box, top_free_throw, corner_three_a, corner_three_b, three_arc]\n",
    "\n",
    "    if outer_lines:\n",
    "        # Draw the half court line, baseline and side out bound lines\n",
    "        outer_lines = Rectangle((0,0), 50, 50, linewidth=lw,\n",
    "                                color=color, fill=False)\n",
    "        court_elements.append(outer_lines)\n",
    "\n",
    "    # Add the court elements onto the axes\n",
    "    for element in court_elements:\n",
    "        ax.add_patch(element)\n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Testing draw court\n",
    "plt.figure()\n",
    "draw_court(outer_lines=True)\n",
    "plt.xlim(0,50)\n",
    "plt.ylim(0,50)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fn = \"./Data/Hackathon_sv_raw_playoff_2016.txt\"\n",
    "df = pd.read_csv(fn, sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# peek the data and drop nan\n",
    "df = df.dropna()\n",
    "# first game in playoff\n",
    "df = df[df.SV_Game_ID==2016041705]\n",
    "\n",
    "# final game in playoff\n",
    "# df[df.SV_Game_ID==2016061909]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# faster loading\n",
    "# df[df.SV_Game_ID==2016041705].to_csv(\"./Data/first_game.csv\")\n",
    "df = pd.read_csv(\"./Data/first_game.csv\")\n",
    "\n",
    "\n",
    "# peek the data and drop nan\n",
    "df = df.dropna()\n",
    "# first game in playoff\n",
    "df = df[df.SV_Game_ID==2016041705]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# only look at time frames where everybody is in the same half court\n",
    "lst = []\n",
    "for row in (range(len(df))):\n",
    "    all_in_same = True\n",
    "    smaller_half = (df.iloc[row][\"P1X\"] > 47)\n",
    "    for j in range(2,11):\n",
    "        if (df.iloc[row][\"P{}X\".format(j)] > 47) != smaller_half:\n",
    "            all_in_same = False\n",
    "            break\n",
    "    if all_in_same:\n",
    "        lst.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# pick a particular frame\n",
    "df_frame = df.iloc[200]\n",
    "\n",
    "# get the team id\n",
    "teams = [int(np.unique(df.Player1Team)[0]), int(np.unique(df.Player1Team)[1])]\n",
    "teams = sorted(teams)\n",
    "\n",
    "def get_frame_data(df_frame, teams):\n",
    "    '''\n",
    "    convert dataframe into our data structure\n",
    "    '''\n",
    "    from scipy.spatial import Voronoi, voronoi_plot_2d\n",
    "    frame_data = {teams[0]:[], teams[1]:[]}\n",
    "    for i in range(1,11):\n",
    "        # reduce to half court\n",
    "        tmp_x = df_frame['P{}X'.format(i)]\n",
    "        if tmp_x > 47:\n",
    "            tmp_x = 94 - tmp_x\n",
    "        tmp_pos = (tmp_x, df_frame['P{}Y'.format(i)], df_frame['Player{}'.format(i)],)\n",
    "        frame_data[int(df_frame[\"Player{}Team\".format(i)])].append(tmp_pos)\n",
    "\n",
    "    for key in frame_data:\n",
    "        frame_data[key] = sorted(frame_data[key], key=lambda x: x[-1])\n",
    "\n",
    "    frame_data['ball'] = [df_frame[\"BX\"], df_frame[\"YB\"], ]\n",
    "    return frame_data\n",
    "\n",
    "frame_data = get_frame_data(df_frame, teams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1610612739: [(27.244209999999995, 21.841650000000001, 'JR Smith'),\n",
       "  (22.836299999999994, 31.08728, 'Kevin Love'),\n",
       "  (26.986599999999996, 31.813359999999999, 'Kyrie Irving'),\n",
       "  (32.179530000000007, 32.810590000000005, 'LeBron James'),\n",
       "  (21.596999999999994, 14.909750000000001, 'Tristan Thompson')],\n",
       " 1610612765: [(29.796779999999998, 20.69735, 'Andre Drummond'),\n",
       "  (19.47587, 9.6649700000000003, 'Kentavious Caldwell-Pope'),\n",
       "  (27.467219999999998, 41.609169999999999, 'Marcus Morris'),\n",
       "  (22.829630000000002, 32.7211, 'Reggie Jackson'),\n",
       "  (41.690950000000001, 35.109830000000002, 'Tobias Harris')],\n",
       " 'ball': [22.9618, 32.191220000000001]}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def ds2points(frame_data, teams):\n",
    "    '''\n",
    "    define a function for converting my data structure to raw data points\n",
    "    '''\n",
    "\n",
    "    points = np.zeros((10, 2))\n",
    "    for i,team in enumerate(teams):\n",
    "        for j in range(5):\n",
    "            points[5*i+j,0] = frame_data[team][j][0]\n",
    "            points[5*i+j,1] = frame_data[team][j][1]\n",
    "    return points\n",
    "\n",
    "points = ds2points(frame_data, teams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 27.24421,  21.84165],\n",
       "       [ 22.8363 ,  31.08728],\n",
       "       [ 26.9866 ,  31.81336],\n",
       "       [ 32.17953,  32.81059],\n",
       "       [ 21.597  ,  14.90975],\n",
       "       [ 29.79678,  20.69735],\n",
       "       [ 19.47587,   9.66497],\n",
       "       [ 27.46722,  41.60917],\n",
       "       [ 22.82963,  32.7211 ],\n",
       "       [ 41.69095,  35.10983]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Voronoi Shit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# points = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2], [2, 0], [2, 1], [2, 2]])\n",
    "from scipy.spatial import Voronoi, voronoi_plot_2d\n",
    "vor = Voronoi(points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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1OjoapRS1atUivlQ8l5+57HTnEWQpktBWSjUEAoA2wOuAD3AN2Au8rrW+lsc2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\nGGwPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bb8add8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "voronoi_plot_2d(vor)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  8,  5,  6,  2,  1,  4, 10,  9,  0])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# input a position and we'll tell you which idx of domain it belongs to \n",
    "vor.point_region"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# import Chavis spot values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "Missing parentheses in call to 'print' (generate_spot_values.py, line 141)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/Users/Chengyu/GitHub/Kaggle/MDST/NBA/generate_spot_values.py\"\u001b[0;36m, line \u001b[0;32m141\u001b[0m\n\u001b[0;31m    print m\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'\n"
     ]
    }
   ],
   "source": [
    "import generate_spot_values as gen\n",
    "from itertools import product\n",
    "players = [frame_data[team][j][2] for team,j in product(teams, range(5))]\n",
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight_func = lambda x: 1.0 / (1.0 + x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.spatial import cKDTree\n",
    "from functools import reduce\n",
    "import generate_spot_values_backup as gen\n",
    "# important!! get the consistent order for the players names\n",
    "players = []\n",
    "for team in teams:\n",
    "    for i in range(5):\n",
    "        players.append(frame_data[team][i][2])\n",
    "\n",
    "def get_all_value(players, points, teams, frame_data):\n",
    "    '''\n",
    "    calculate every player's land value given the current frame's players' positions\n",
    "    '''\n",
    "    grid_centers, grid_vals = gen.get_midpoints_and_values()\n",
    "    # calculate original land values\n",
    "    player_value_orig_lst = {}\n",
    "    vor_points = ds2points(frame_data, teams)\n",
    "    kdtree_orig = cKDTree(points)\n",
    "    for player in players:\n",
    "        player_value_orig_lst[player] = []\n",
    "\n",
    "    for i in range(10):\n",
    "        for j in range(10):\n",
    "            center = grid_centers[i][j]\n",
    "            test_point_dist, test_point_regions = kdtree_orig.query(np.array(center).reshape((1,2)), k=1)\n",
    "            player = players[test_point_regions]\n",
    "            player_value_orig_lst[player].append((i,j,test_point_dist[0]))\n",
    "\n",
    "    player_val_orig = {}\n",
    "    for player in players:\n",
    "        player_val_orig[player] = 0\n",
    "\n",
    "    for player in players:\n",
    "        for spot_i, spot_j, spot_dist in player_value_orig_lst[player]:\n",
    "            player_val_orig[player] += weight_func(spot_dist) * grid_vals[spot_i][spot_i]\n",
    "            \n",
    "    return player_val_orig       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Chengyu/anaconda3/envs/python34/lib/python3.4/site-packages/ipykernel/__main__.py:26: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Andre Drummond': 50.463260369823011,\n",
       " 'JR Smith': 30.096063883525218,\n",
       " 'Kentavious Caldwell-Pope': 85.22224634241141,\n",
       " 'Kevin Love': 22.204632255322768,\n",
       " 'Kyrie Irving': 28.876219925639127,\n",
       " 'LeBron James': 54.014775928875331,\n",
       " 'Marcus Morris': 72.465922611028475,\n",
       " 'Reggie Jackson': 72.224451215664729,\n",
       " 'Tobias Harris': 32.562601700645125,\n",
       " 'Tristan Thompson': 52.989019085546246}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_val_orig = get_all_value(players, points, teams, frame_data)\n",
    "player_val_orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# a function to return the team of a player:\n",
    "def get_player_team(player, teams, frame_data):\n",
    "    for team in teams:\n",
    "        for i in range(len(frame_data[team])):\n",
    "#             print(frame_data[team][i][2])\n",
    "            if frame_data[team][i][2] == player:\n",
    "                return team\n",
    "            \n",
    "def get_other_team(player, teams, frame_data):\n",
    "    player_team = get_player_team(player, teams, frame_data)\n",
    "    return [t for t in teams if t!=player_team][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metric 1: the Diff in total offensive team land values if the player is not on court\n",
    "# exciting part, leave one player out of the court and test reduction in the other team's land value!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# leave one out\n",
    "\n",
    "out_player = \"Andre Drummond\"\n",
    "out_player_idx = players.index(out_player)\n",
    "\n",
    "team_4 = get_player_team(out_player, teams, frame_data)\n",
    "team_5 = get_other_team(out_player, teams, frame_data)\n",
    "\n",
    "# print(out_player_idx)\n",
    "# print(team_4, team_5)\n",
    "# print(teams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['JR Smith',\n",
       " 'Kevin Love',\n",
       " 'Kyrie Irving',\n",
       " 'LeBron James',\n",
       " 'Tristan Thompson',\n",
       " 'Andre Drummond',\n",
       " 'Kentavious Caldwell-Pope',\n",
       " 'Marcus Morris',\n",
       " 'Reggie Jackson',\n",
       " 'Tobias Harris']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.spatial import cKDTree\n",
    "from functools import reduce\n",
    "\n",
    "# get the leave one out player score\n",
    "\n",
    "def get_leave1out_value(players, points, teams, frame_data, out_index):\n",
    "    '''\n",
    "    calculate every other player's land value leaving one player out of the court\n",
    "    '''\n",
    "    ##### modified from the all value funtion\n",
    "    points = points[[x for x in range(10) if x!=out_index],:]\n",
    "    grid_centers, grid_vals = gen.get_midpoints_and_values()\n",
    "    # calculate original land values\n",
    "    player_value_orig_lst = {}\n",
    "    vor_points = ds2points(frame_data, teams)\n",
    "    kdtree_orig = cKDTree(points)\n",
    "    for player in players:\n",
    "        player_value_orig_lst[player] = []\n",
    "\n",
    "    for i in range(10):\n",
    "        for j in range(10):\n",
    "            center = grid_centers[i][j]\n",
    "            test_point_dist, test_point_regions = kdtree_orig.query(np.array(center).reshape((1,2)), k=1)\n",
    "            \n",
    "            ##### modified from the all value funtion\n",
    "            if test_point_regions >= out_index:\n",
    "                test_point_regions += 1\n",
    "            player = players[test_point_regions]\n",
    "            \n",
    "            player_value_orig_lst[player].append((i,j,test_point_dist[0]))\n",
    "\n",
    "    player_val_orig = {}\n",
    "    for player in players:\n",
    "        player_val_orig[player] = 0\n",
    "\n",
    "    for player in players:\n",
    "        for spot_i, spot_j, spot_dist in player_value_orig_lst[player]:\n",
    "            player_val_orig[player] += weight_func(spot_dist) * grid_vals[spot_i][spot_i]\n",
    "    \n",
    "#     print(players)\n",
    "    out_player = players[out_index]\n",
    "    player_val_orig[out_player] = 0\n",
    "    \n",
    "    return player_val_orig       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def value_of_player(players, points, teams, frame_data, out_player):\n",
    "    '''\n",
    "    value of the player is how important he is on court than if he is not\n",
    "    calculate the team's total land values in two cases and subtract to get it\n",
    "    '''\n",
    "    if type(out_player) == str:\n",
    "        out_index = players.index(out_player)\n",
    "    else:\n",
    "        raise Exception()\n",
    "        \n",
    "    player2team = {}\n",
    "    for player in players:\n",
    "        player2team[player] = get_player_team(player, teams, frame_data)\n",
    "\n",
    "    team4 = get_player_team(out_player, teams, frame_data)\n",
    "    team5 = get_other_team(out_player, teams, frame_data)\n",
    "    val_orig = get_all_value(players, points, teams, frame_data)\n",
    "    val_out  = get_leave1out_value(players, points, teams, frame_data, out_index)\n",
    "    \n",
    "    val_team_orig = {}\n",
    "    val_team_out = {}\n",
    "    for player in val_orig:\n",
    "        his_team = player2team[player]\n",
    "        if his_team not in val_team_orig:\n",
    "            val_team_orig[his_team] = val_orig[player]\n",
    "        else:\n",
    "            val_team_orig[his_team] += val_orig[player]\n",
    "\n",
    "    for player in val_out:\n",
    "        his_team = player2team[player]\n",
    "        if his_team not in val_team_out:\n",
    "            val_team_out[his_team] = val_out[player]\n",
    "        else:\n",
    "            val_team_out[his_team] += val_out[player]\n",
    "#     print(team5)\n",
    "#     print(val_team_orig)\n",
    "    result = {\"team4\":val_team_out[team4] - val_team_orig[team4],\n",
    "              \"team5\":val_team_out[team5] - val_team_orig[team5],\n",
    "             }\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Chengyu/anaconda3/envs/python34/lib/python3.4/site-packages/ipykernel/__main__.py:28: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Andre Drummond': 0,\n",
       " 'JR Smith': 66.54104265125163,\n",
       " 'Kentavious Caldwell-Pope': 85.648316762260322,\n",
       " 'Kevin Love': 22.204632255322768,\n",
       " 'Kyrie Irving': 28.876219925639127,\n",
       " 'LeBron James': 54.014775928875331,\n",
       " 'Marcus Morris': 72.465922611028475,\n",
       " 'Reggie Jackson': 72.224451215664729,\n",
       " 'Tobias Harris': 33.330131132993756,\n",
       " 'Tristan Thompson': 54.633710763524292}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get_all_value(players, points, teams, frame_data)\n",
    "get_leave1out_value(players, points, teams, frame_data, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Chengyu/anaconda3/envs/python34/lib/python3.4/site-packages/ipykernel/__main__.py:26: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n",
      "/Users/Chengyu/anaconda3/envs/python34/lib/python3.4/site-packages/ipykernel/__main__.py:28: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Andre Drummond': 87.35933096332991,\n",
       " 'JR Smith': 44.852876750241364,\n",
       " 'Kentavious Caldwell-Pope': 144.33394134039537,\n",
       " 'Kevin Love': 41.278024142856282,\n",
       " 'Kyrie Irving': 29.916419944292102,\n",
       " 'LeBron James': 50.071120461660115,\n",
       " 'Marcus Morris': 74.255366246042229,\n",
       " 'Reggie Jackson': 119.96527615358934,\n",
       " 'Tobias Harris': 42.337033863810461,\n",
       " 'Tristan Thompson': 69.180604698629082}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_of_player(players, points, teams, frame_data, \"Marcus Morris\")\n",
    "frame_result = {}\n",
    "for player in players:\n",
    "    val = value_of_player(players, points, teams, frame_data, player)\n",
    "    frame_result[player] = np.abs(val['team4']) + np.abs(val['team5'])\n",
    "\n",
    "frame_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1610612739: 'r', 1610612765: 'b'}\n",
      "['r', 'r', 'r', 'r', 'r', 'b', 'b', 'b', 'b', 'b']\n"
     ]
    },
    {
     "data": {
      "image/png": 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MQ8ePH2fFihXcfPPNVFZWBm9h6yRI7C0sbvgAPB3BW1uEpba2NtavX8+sWbOw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5AAAg\nAElEQVSm0Wh46KGHWLlyJcePHyc4OJhhw4bRvXt3lixZws2bN9UuURBszuDBg9m+fTtz5szhH//4\nR+02JC++CQXXQdKCaxNkWSYmJkYs0ikIQo2JMGVDAgMDmTp1KufOnWP69OmsWbOG4OBgXn75ZY4e\nPap2eYJgU1q3bs2ePXs4d+4cjzzySM2XICkdlXJtChodmzdvBmDgwIFmrlQQBEtJS0uzia3dRJiy\nQVqttmxT5UOHDuHn58egQYPo27cvX331FXl5eWqXKAg2wWAw8OOPP9K9e3e6d+9es1867lj1PDo6\nmsmTJyNJkgUqFQTBEjZv3kyPHj3Ys2ePqnWIMGXjgoKCePfdd7lw4QL/93//x7JlywgODmby5MmW\nXxlaEOyAVqtl1qxZvPfeewwYMIBVq1bd+0WmIsi/otx3C+bo0aMcPXqUp59+2rLFCoJgVqNGjeKz\nzz5jyJAhfP3116rVIcKUndDpdAwdOpSff/6ZvXv34uLiwv33388DDzzAihUrbH5BM0GwtKeffpoN\nGzbw6quvMn369Ltv55R3SdlGxrkR6NyIiYnhpZdewtnZ2XoFC4JgFkOGDGHLli28/fbbTJkyxbqb\npJcQYcoONW/enBkzZpCUlMTf//53Fi1aRFBQEFOmTOHcuXNqlycIqunSpQt79+5l8+bNjBgxourl\nRm5b9fzq1ausXr2aCRMmWK9QQRDMqn379uzdu5fdu3ezdOlSq59fhCk75uTkxJNPPsmvv/7K9u3b\nKS4upkePHjzyyCOsWrWqdlc4CYKdCwgIYMuWLfj5+dG7d++Kv2DIxnJLIixYsIDRo0fj5+dn/WIF\nQTAbf39/Nm3apMqemiJMOYhWrVrx4YcfcvHiRZ577jnmzp1LSEgI//rXv0hKSlK7PEGwKmdnZxYt\nWsSECRPo3bs3W7ZsufXBm+fBVABOftws1BMbGysW6RQEB+Hk5IRWq7X6eXVWOcu5RVY5jQAuwJhe\nMKbXsxw7fYXY5duI6DiX3p1bEDU6ksH3t0erFRlacHySJPHSSy/Rtm1bRo8ezdSpU3n55ZeRsk8q\nT/BszdL//pc+ffrQsmVLdYsVBMGuiZ+qDqxdq8Z8/M4oLsbN4olHInhvwXpa9J/K//tkHVdSMtUu\nTxCs4oEHHmDXrl0sXryYvz4/loLM8yBpMbmFMnfuXLFIpyA4uOTkZKZNm2bR1hfJ0pvsSpIki418\nbcehQ4eIjY1lxYoV9O/fnwkTJvDQQw+h0YhcLTi2nJwcxo5+jKuXL7Dqvx+w56ye9957j71794q1\npQTBgd24cYNRo0ZRWFjId999h6+vb9VPPrcIKTQKWZZr9E1B/AStZzp16sRnn33GhQsXGDRoEFOm\nTKFly5Z88MEHXLt2Te3yBMFiPNzd+O7jcQy6vx3dB09k+vTpvPbaayJICYKD8/b25qeffiIiIoLu\n3btz4sQJs59DhKl6ytPTk/HjxxMfH8/y5cs5ffo0rVu3ZtSoUfz222+I0UTB4eRdRiPn8fak0Xz0\n0cfk5eURERGhdlWCIFiBVqvlww8/5F//+hf3338/69evN+vxRZiq5yRJKttUOTExkb59+/KPf/yD\nNm3aEBMTQ1pamtolCoJ53NZ4/sTw4URFRTFu3DiKi4vVrUsQBKsZO3Ysq1ev5uTJk2Y9ruiZEiqQ\nZZmdO3eycOFC1q5dy5AhQ8ouMRdTIoJdMubBhW8AGYKfBp07JpOJRx55hH79+vH222+rXaEgCLZA\n9EwJ5iJJEn369OGrr77i7NmzRERE8Pzzz9OhQwfmz5/PjRs31C5REGom+wxgArcg0LkDoNFoWLp0\nKQsWLGD37t3q1icIgl0TYUq4Kz8/PyZPnszJkyf5+OOPiYuLIyQkhBdeeIF9+/aJ3irB9snybVN8\nYeU+1LhxYz799FOeeeaZqreeEQShXqjLHrciTAnVIkkSDzzwAN9++y2nTp2iVatWjBw5ki5durBo\n0SLxg0iwXQXXoCgTtK7gFlzhw8OHDycyMlKsgi4I9VhRURGdO3fmu/XxtXq96JkSas1kMrF582Zi\nY2PZunUrI0eOJCoqik6dOqldmlBHsiyTm5tb1pyt1+txdXW1z565679D9inw7gh+PSp9SnZ2NhER\nEcyZM4dhw4ZZuUBBEGzBwYMHGfrnh0i6kl7jnikRpgSzuHLlCkuWLGHx4sU0btyYqKgoRo4ciZub\nm9qlCdVUWFhI8tWrpCYmcjM1FafiYvQlHysCCnU6PBo2xD8khIDAQPR6/d0OZxtMhXDha5CLoelT\n4GSo8qm7d+9m6NChHDhwgMaNG1uxSEEQbEXK3g8J6PG6CFOCuoxGIz///DMLFy5k165djBkzhqio\nKNq1a6d2aUIVioqKOHfyJNdPnKCB0UgjT088XV0rbBZabDSSnZtLck4OaTodjdq1o3mrVuh01tni\ns1ayTkLqNnAJgMaP3fPp06dPZ+fOnWzYsEHsCiAI9ZG4mk+wBVqtlj//+c/89NNPHDhwAG9vbwYO\nHEhkZCTffPMN+fn5apco3CYtLY39GzeiPXaMnn5+tA4MxODhUemu6zqtFh9PT9oEBtLDxwfT4cPs\n37yZjIwMFSqvpioaz6sybdo0srOz+eSTTyxYlCAIjkaMTAkWV1RUxNq1a4mNjeXgwYM899xzjB8/\nnlatWqldWr12+dIlkrZvp42nJwYPj1odIz0ri5O5ubS4/34CAgPNXGEdFabDpZUg6aHZM6Cp3rTk\n2bNn6dmzJ1u2bCE8PNzCRQqCYFPEyJRgq/R6PU888QQbN25k165d6HQ6+vXrx4MPPsh3331Xp8tR\nhdpJvnqVi7//ToSfX62DFICvlxedDAYSt27l+vXrZqzQDLJPKbce91U7SAGEhobywQcfMGbMGDGS\nKghCtYgwJVhVaGgos2bNIikpifHjx/Ppp58SHBzMW2+9RWJiotrl1Qu5ubmcjYujg58fLk5OdT6e\nm4sL4QYDp7dvt53wYSqG7NPKfc/WNX75uHHjaNmyJVOnTjVzYYIgOCIRpgRVODs7M3LkSH777Te2\nbt1Kfn4+3bp1Y/DgwaxevVrsl2Yhsixz6sABQiQJNxcXsx3Xw9WVpsXFnD582GzHrJPsU2AqACd/\ncG5Q45dLksSiRYtYsWIFv/zyiwUKFATBkYgwJaguLCyMmJgYLl68yNNPP82cOXMICQnhnXfe4eLF\ni2qX51DS0tIwXbhAYz8/sx87uEED8s+eVb8hXTbBjZJQ5xMBtVwby8/Pj//85z+MGzdObPgtCMJd\niTAl2AxXV1eeffZZduzYwc8//0xaWhqdOnXi8ccfZ/369RiNRrVLtHuXz5yhqYUW35QkiSZ6PVfO\nnTP7sWsk5w8ozgG9AdxC6nSogQMHMmLECKKiosTWSYIgVEmEKcEmhYeHM3/+fJKSknjssceYPn06\noaGhvP/++1y9elXt8uxSQUEBORcu0MBQ9cKVddXI15eMs2cpKiqy2DnuSjZB5kHlvqFTrUel/n97\ndx4fdXXvf/x1JivZE0LYZF9lE6wIFFFEUNSqpXDlFqIsWoPVK1qXa7H96dVWqq21XJWbpFZEoQpq\nUVxZZFFUkMqmyCIgBAiEJZCE7DNzfn9MxF2zzMx3Jnk/Hw8ezAzM9/vWQyafnO/5fs5XzZw5k507\ndzJ37twGH0tEGicVUxLS4uPjue666/jwww/517/+RV5eHr169WLcuHEsX74cr9frdMSwUVxcTBIE\ntBllZEQECV4vp06dCtg5flDpXqgugsgE3118fhAbG8v8+fO588472b17t1+OKSKNi4opCRtnn302\nOTk57Nu3j5EjR3LHHXfQvXt3/vznP4febfkhqKSoiMS6vqcsgg+2JlFS9u0mnt8nwVpKiovreCY/\nsBZObvI9Tj4LjP8+3vr27cuMGTO45pprdHOEiHyLiikJO0lJSUybNo2NGzcyb948Pv30U7p168aE\nCRNYvXq11rZ8j4qiIprVoRVCSVkEw24ZwPnT+zPslgG1LqjioqKoKCmpb8z6Kz8AVccgolm92iH8\nmOnTpxMfH8+DDz7o92OLSHhTMSVhyxjD4MGDmTNnDp9//jmDBw/mxhtvpFevXvztb3+jsLDQ6Ygh\nxXq9dbrE98nn8WzdG4fb4+LTfc3Yuje+Vu8zxjhz+fX0rFRfcPl/v0CXy8XcuXN54oknWLt2rd+P\nLyLhS8WUNAqpqanccsstbN26ldzcXNavX0+XLl2YPHkyH3zwgWarAFdkJJ46FDl9OpXSu2MZUZEe\nenUop3fH0lq9z+v1EhHszY8rDkPFIXBFQ1KvgJ2mTZs2zJ49m8zMTOfWhYlIyFExJY2KMYZhw4Yx\nf/58PvvsM/r06cO1115L//79mT17NsVOrOUJEc1SUiirw9Y9iXEe3v3fjbwzazPv/u9GEuNq15qi\n1O2mWVJSfWPWz4maO/iSevsKqgAaO3Ys559/PrfeemtAzyMi4UPFlDRa6enp3HHHHezYsYO//vWv\nrFq1ig4dOvCrX/2Kjz76yOl4QZeYlERdVzIlxnkY3Ku41oUUQIkxJCbWdal7A1Qeg/L9YCJ9l/iC\nYNasWaxatYpFixYF5XwiEtpUTEmj53K5uOiii1i4cCHbtm2jc+fOjBs3jnPOOYcnn3yyyVyuSUpK\nosTlwh3A5qdV1dWUR0WR0IDNk+vsi7VSiT0hwn9b5PyQxMRE5s2bx4033kh+fn5QzikioUvFlDQp\nrVq14re//S27du3igQce4LXXXqN9+/bcdNNNbNmyxel4ARUVFUVat24cDuDC/PzCQjJ69iQiovat\nFBqk6iSU7gFckNIvOOesMXjwYKZNm8bkyZPV70ykiVMxJU1SRETE6U2Vt2zZQkZGBpdddhk//elP\nmTt3LuXl5U5HDIi2nTtzoLo6IN/83R4P+V4vbTp08Puxv9cXe/Aldvc16gyy3/3ud5SUlPDYY48F\n/dwiEjpUTEmTd8YZZ3Dvvfeyd+9e7r77bhYsWEC7du247bbb2L59u9Px/Co5OZmkXr34PABNTncf\nOUJ6v37Bu8TnPgUlOwEDKWcF55zfEBkZybx58/jDH/7Axx9/7EgGEXGeiimRGpGRkVx55ZW88cYb\n/Pvf/yYuLo4LL7yQ4cOH89xzz1FZWel0RL/o1rs3BXFxnPBjY81jRUWcSEmhcw//N8v8Xie3ABbi\nO0NUcvDO+w1dunThoYceYuLEiVRUVDiWQ0Sco2JK5Dt07NiRP/7xj+Tl5XHzzTfz1FNP0b59e+66\n6y527drldLwGiYqKotewYXxaUcFJPyy+LywuZofHQ+/zziMyWP2lPOVQss33OKV/cM75A6ZMmUK3\nbt245557nI4iIg5QMSXyA6Kiohg3bhzLli1jzZo1APz0pz9l1KhRvPTSS1RXVzucsH5SUlLoNXIk\nWysrOXjsWL2amlpryTt6lO3W0nfkyOC2Qzi5CawH4tpDTPPgnfd7GGPIzc1lwYIFLF++3Ok4IhJk\nJtCdoY0xVt2npTGprKzkpZdeIicnh507d3Ldddfxq1/9ig7BXHjtJ2VlZWz/6CNceXl0TkkhKb52\nW8YUnTrF7qIiXJ0703PAAGJjg9OSAPDdwXfgBcBC219ATHrwzv0jli1bxtSpU9m8eTNpaWlOxxGR\nutqTi+mShbXW1OVtKqZEGuDTTz8lNzeXZ599lsGDB5OVlcVll10WvMtdfmCtJf/gQQ588gmRhYW0\njIoisVkzEpo1O93iwO3xcKq8nJKyMg673dj0dNr16UOr1q0xpk6fOQ13eAmU7fNtZtziguCeuxZu\nu+02Dhw4wMKFC4P//0ZEGkbFlIhzysrKeOGFF8jOzubAgQNcf/31XH/99bRt29bpaLVmraWwsJDj\nhw9TUlBA6fHj4Hb7/jAykoQWLUjMyCC9dWtSU1OdCVl2AA6/ASYK2o2HyDhncvyAiooKzj33XH7z\nm98wefJkp+OISF2omBIJDZs3byYnJ4fnn3+eCy64gKysLC6++GJcrvBaomitPd2PyuVyOT/LYr1w\n4CWoPgGpAyF1gLN5fsDHH3/MiBEjWLduHZ07d3Y6jojUVj2LqfD6dBcJA2eddRazZ88mLy+Pyy67\njHvuuYeuXbsyc+ZMCgoKnI5Xa8YYIiIiiIiIcL6QAijZ7iukIhODtgdfffXt25cZM2aQmZmJ+4vZ\nPRFptFRMiQRIQkLC6U2VFy5cyJ49e+jZsydXX301K1asqNcddE2WpxIK/+17nDYIXKG/Jm369OnE\nx8fz4IMPOh1FRAJMl/lEgqioqIh58+aRk5NDRUUFWVlZTJo0ifT00LkjLSQdXwtFWyC2FbS+AkJh\npqwW8vPzGTBgAK+88gqDBw92Oo6I/Bhd5hMJfcnJydx0001s3ryZp59+mi1bttC1a1cyMzN59913\nNVv1XaqLoOgT3+PmPw2bQgqgTZs2zJ49m8zMTE75oUGqiIQmzUyJOKywsJBnnnmG7OxsIiIiyMrK\n4tprryUlJcXpaKEhxFsh1MbUqVNxuVw8+eSTTkcRkR+imSmR8JSWlsatt97Ktm3bmD17Nh988AGd\nOnVi6tSprFu3rmnPVpUd8BVSJsp3B1+YmjVrFitXrmTRokVORxGRAFAxJRIijDFccMEFPPfcc+zY\nsYOePXsyceJEzj77bLKzsynx48bEYcF64fgHvscp/UOyp1RtJSYmMm/ePKZNm0Z+fr7TcUTEz1RM\niYSgjIwM7rrrLnbu3MnDDz/MsmXLaN++PVlZWWzcuNHpeMERRq0QamPIkCHceOONTJky5XT/LhFp\nHFRMiYQwl8t1elPlrVu30q5dO37+859z7rnn8tRTT1FaWup0xMDwVoVdK4Ta+N3vfkdxcTGPPfaY\n01FExI+0AF0kzHg8Ht566y1ycnJ47733mDBhAllZWfTp08fpaP4Tpq0QamP37t0MHjyYlStXNq4x\nE2kMtABdpGmIiIjg8ssvZ/HixWzcuJHU1FQuueQSzjvvPJ599lkqKiqcjtgwX2uFMKRRFVIAXbp0\n4aGHHmLixIlUVlY6HUdE/EDFlEgYa9++Pffffz979+7l9ttvZ/78+bRr147bb7+dHTt2OB2vfo6v\nBbyQ0B1iWjidJiCmTJlC165dmTFjhtNRRMQPVEyJNAJRUVGMGTOGt956i3Xr1hEdHc3555/PiBEj\nWLBgAVVVVU5HrJ2yvC9bIaSd63SagDHGkJuby4IFC3j77bedjiMiDaRiSqSR6dy5MzNnzmT//v1M\nmzaNnJwc2rdvz913382ePXucjvf9vFVw9F3f49Szw7oVQm00b96cOXPmMHnyZAoLC52OIyINoGJK\npJGKjo4+vany6tWrqa6uZtCgQVxyySUsWrSI6upqpyN+3fG14Cn1XdprBK0QamPUqFGMGzeOrKys\npt2cVSTMqZgSaQJ69OjBI488wv79+7nmmmv461//SseOHfl//+//kZeX53Q8KD/o6yuFy7dljGk6\nH00zZ85k+/btzJ071+koIlJPTecTS0SIjY09vanykiVLOHHiBAMGDOCKK67gtddew+PxBD+UtxqO\nvuN7nHo2RKcFP4ODYmNjmT9/PnfeeWdoX4YVke+lYkqkierTpw+PPfYYeXl5jBkzhgceeIDOnTvz\nhz/8IbhbnhSuB3cJRDf3bRvTBPXr148ZM2aQmZmJ2+12Oo6I1JGKKZEmLj4+/vSmyi+//DIHDhyg\nd+/ejB07lqVLlwZ265OKw1D8CWCa3OW9b5o+fTrx8fE8+OCDTkcRkTpSB3QR+ZaSkhL++c9/kp2d\nTXFxMTfccANTpkwhIyPDfyfxuuHgS74mnSkDIG2g/44dpvLz8xkwYACLFy9m0KBBTscRaXrUAV1E\n/CUxMZGsrCw2bNjAc889x44dO+jRowf/+Z//yapVq/xz59mJf/sKqagU31opoU2bNsyePZvMzExO\nnTrldBwRqSXNTIlIrZw8eZJnn32WnJwc3G43WVlZTJo0ibS0eiwYLz8Eh14FDLS5CmL9OOPVCEyd\nOhWXy8WTTz7pdBSRpkUzUyISSCkpKfzXf/0XH3/8MU8++SQfffQRnTt35tprr+W9996r/WyVtwqO\nrqw5aH8VUt9h1qxZrFy5kkWLFjkdRURqQcWUiNSJMYbzzjuPefPmsWvXLs466yymTJlCv379ePzx\nxykqKvrhAxx7H9ynIDpdl/e+R2JiIvPmzePGG28M7p2VIlIvuswnEmYqKiooKSmhrKyM6moPXq8l\nMtJFdHQUCQkJJCYmEhEREdRM1lpWrlxJTk4OS5cuZezYsWRlZXHOOedgzFdmy0v3QsFSMBHQ9hcQ\nnRrUnOHmvvvu44MPPuDNN9/E5dLPviIBV8/LfCqmREKctZYTJ06we3c+Bw8WUV5uMCYRa+NxuSIx\nxuD1erG2CmNKMKaUlJQYOndOp127NsTGxgY1b0FBAXPmzCEnJ4e0tDSysrKYMGECCbEuOPAieCug\n+ZAms2VMQ7jdbs477zwmTJjALbfc4nQckcZPxZRI4+LxeDh4MJ9PP82nsDCSmJg2JCU1Jyoq+gff\nZ62loqKU4uICrD1Mp05JdO9+BqmpwZ0F8nq9LFu2jOzsbFavXs34K4Yw7eqzOevswdDqMjB1+qxq\nsnbt2sWQIUNYuXIlffr0cTqOSOOmYkqk8SgqKmLduu0cO5ZAcnI74uOT6nUcj8dDUdFRKir20qdP\nCr16dSUyMtLPaX/cwe2r+UfOI/x9wRratuvCtBtv4uqrryYuLi7oWcLRU089xaxZs/jwww+JiYlx\nOo5I46ViSiT8eb1edu7cw8aNR4iL605ycrpfjuvxeDh6dA+JiccYMqRH/doZ1Fd1ERz4F9hq3KnD\neHPNLrKzs1m7di2ZmZlkZWXRq1ev4OUJQ9Zaxo0bR8eOHXnkkUecjiPSeKk1gkh483g8rFu3hX//\nu4IWLQb6rZACiIiIoFWrbni9PVmyZAcHDwbpDjFvNRxeCrYa4jsTmdKTK664gtdff50NGzaQmJjI\nyJEjOf/885k/fz4VFRXByRVmjDHk5uayYMEC3n77bafjiMg3aGZKJAR4PB7Wrt1CXl48rVp1+/od\ncH5WWVnOsWObueCCdpxxRtuAnQdr4cgKKN0NUcnQdgy4vr3eq7q6msWLF5OTk8OmTZuYNGkSN9xw\nA926dQtctjC1bNkypk6dyubNm4M7uyjSVGhmSiQ8WWv56KOt5OU1C3ghBRAT04wWLfrzzjt5HD16\nNHAnKvrYV0iZKGh58XcWUgBRUVGnN1V+//33cblcDB06lJEjR/LCCy9QVVUVuIxhZtSoUYwbN46s\nrCz/bOkjIn6hYkrEYQcOHOSzzzy0atUj4IXUF6KjY0lJ6cOaNTuprKz0/wnK86Fwne9xxvBa95Pq\n2rUrDz30EPv37+f666/niSeeoEOHDsyYMYPPP//c/znD0MyZM9m+fTvPPPOM01FEpIaKKREHlZeX\ns27dPtLTewatkPpCXFwiVVVt2LJlp38P7D4FBcsB69suJr5TnQ8RExNzelPlFStWUF5ezsCBA7n0\n0kt55ZVXcLvd/s0cRmJjY5k/fz533HEHe/bscTqOiKBiSsRRGzZsx9oOxMQ0c+T8LVp0YOfOCgoK\nCvxzQOuBgmW+xpzN2kLqOQ0+5Jlnnsmjjz7K/v37+eUvf8nDDz9Mx44due+++zhw4IAfQoeffv36\nMWPGDDIzM5t0YSkSKlRMiTjk5MmT7NtXTXp6ABeB/wiXy0Vycje2bNnnnwMeew8qj0JkAmRcBMZ/\nHzHNmjU7v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khTpmJKJBxVHPEVUp4y3z57rS7RhsUSULfddhvJyckMHz6cN954g/79+zsd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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116220b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "vor = Voronoi(points)\n",
    "voronoi_plot_2d(vor, show_vertices=0)\n",
    "\n",
    "size_circ = []\n",
    "color_circ = []\n",
    "team2color = {}\n",
    "flag = False\n",
    "for team in teams:\n",
    "    team2color[team] = 'r' if not flag else 'b'\n",
    "    flag = True\n",
    "    \n",
    "print(team2color)\n",
    "\n",
    "for i in range(10):\n",
    "    player = players[i]\n",
    "    val = frame_result[player]\n",
    "    size_circ.append(val)\n",
    "    color_circ.append(team2color[get_player_team(player, teams, frame_data)])\n",
    "    \n",
    "size_circ = [x*10 for x in size_circ]\n",
    "print(color_circ)\n",
    "\n",
    "\n",
    "\n",
    "# plt.figure()\n",
    "draw_court(color='#ffdd99',outer_lines=True)\n",
    "plt.xlim(0,50)\n",
    "plt.ylim(0,50)\n",
    "# plt.show() \n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(points[:,0], points[:,1], s=size_circ, c=color_circ, alpha=0.25)\n",
    "plt.xlim(0, 50)\n",
    "plt.ylim(0, 50)\n",
    "plt.gcf().set_size_inches(10, 10, forward=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1610612739: 'r', 1610612765: 'b'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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dupTbb7+dkpIS/13YOhbiuwuLaz8BT6v/ri1CUnNzM2vWrGHq1KnYbDaOHDnC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Ax0nATRCe7k+B6VlZWcc845/Pa3v2XatGluxxGR+lAxJRIeNm/eTG5uLk8++STnn38+2dnZXHjh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\nmIEWzMkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147be278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_player = \"Kevin Love\"\n",
    "out_index = players.index(out_player)\n",
    "out_points = points[[x for x in range(10) if x!=out_index],:]\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "vor = Voronoi(out_points)\n",
    "voronoi_plot_2d(vor, show_vertices=0)\n",
    "size_circ = []\n",
    "color_circ = []\n",
    "team2color = {}\n",
    "\n",
    "flag = False\n",
    "for team in teams:\n",
    "    team2color[team] = 'r' if not flag else 'b'\n",
    "    flag = True\n",
    "    \n",
    "print(team2color)\n",
    "\n",
    "for i in range(10):\n",
    "    player = players[i]\n",
    "    val = frame_result[player]\n",
    "    size_circ.append(val)\n",
    "    color_circ.append(team2color[get_player_team(player, teams, frame_data)])\n",
    "    \n",
    "del size_circ[out_index]\n",
    "del color_circ[out_index]\n",
    "    \n",
    "    \n",
    "draw_court(color='#ffdd99',outer_lines=True)\n",
    "plt.xlim(0,50)\n",
    "plt.ylim(0,50)\n",
    "# plt.show() \n",
    "    \n",
    "    \n",
    "size_circ = [x*10 for x in size_circ]\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(out_points[:,0],out_points[:,1],s=size_circ,c=color_circ,alpha=0.25)\n",
    "plt.xlim(0, 50)\n",
    "plt.ylim(0, 50)\n",
    "plt.gcf().set_size_inches(10, 10, forward=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# take 100 samples in time slices\n",
    "\n",
    "N_sampels = 300\n",
    "sample_rows = np.random.randint(0,len(df)-1,size=(N_sampels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-2c5e18575c43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mgame_score_player\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplayer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN_sampels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mgame_stat_player\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplayer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgame_stat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplayer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m         \u001b[0mgame_score_player\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplayer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mgame_stat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplayer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "# sum over all the values of the player\n",
    "\n",
    "game_stat_player = {}\n",
    "game_score_player = {}\n",
    "for player in players:\n",
    "    game_stat_player[player] = []\n",
    "    game_score_player[player] = 0\n",
    "    for i in range(N_sampels):\n",
    "        game_stat_player[player].append(game_stat[i][player])        \n",
    "        game_score_player[player] += game_stat[i][player]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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shpWqAxjDWGezXnC/2AW53FtNnIvEuShPkcvsbUfEVn7/ReAJ4ARwE3A6H3YauHlWQZqZ\n2XjG6rlLagPLwLeApd51UyNiG7iy7OCaJqs6gBpxbzVxLhLnojyFZ4WUdDnwFeCjEfGipL3zbA2d\nd+vV/3z064fg//wK8NqiEZmZ2TCFirukS+gW9i9ExL356o6kpYjoSGoBPxr2/PfuwBvz+68B3gb8\nzXz5j/KfXzkMX/tPsPP/8hXrxd5Alv9cGbFcdHxv3ajXG3e5t26c8QA8tXfF3mfMLt5Jxu+NqPdN\nrNdL9fLg5Z66xFPVcm9dXeKZ53KWZWxsbADQbreZliJGT3Qr6R7gzyPi433rPg08HxGflvRJ4HhE\n3DbguQW2AB+6BD7/8hjXGaXZYy/45bZO/uxBr6JaxBt99/uXiny+zOxikogITfr8kT13STcAvwr8\nXUlnJD0m6Ubg08C7JD0JvAP41KRBLIqs6gBqxL3VxLlInIvyjGzLRMQfAoeHPPzOcsMxM7MyFGrL\nTLUBt2UmGuu2jNlim3lbxmrgMPTKZvdn/60eLowmLUlCEq1Wu5K4zBaVi/scZZM+8RW6394H3Wpj\n//NYO52nLxjt3mriXCTORXlc3M3MGsjFfY5Wqg6gRjyHSOJcJM5FeVzczcwayMV9jrKqA6gR91YT\n5yJxLsrj4m5m1kAu7nO0UnUANeLeauJcJM5FeVzczcwayMV9jrKqA6gR91YT5yJxLsrj4m5m1kAu\n7nO0UnUANeLeauJcJM5FeVzczcwayMV9jrKqA6gR91YT5yJxLspT+BqqZvsbPUOllMYcf91xnv/x\n87MMyGyhjSzukj4H/D2gExHX5utOAR8mXTf1joj4+syibIiVqgOYpfXxhr+w/sJMwjiI3GdOnIvy\nFGnLfB74xQHr74yI6/ObC7uZWY2MLO4R8U1g0Nes+lwp4oDIqg7Aasl95sS5KM80O1RvlbQl6W5J\nV5QWkZmZTW3SHaqfAX4nIkLS7wJ3Ar8xbPAa0M7vHwOWSf3nbM/Y3vLex+c1vrdu1OuNu9xbV2T8\nEeCldS60d7lPVkJ8e5cZ8fiw8TyV/7x6xHLv+fk3tV6vdVGXe+oST1XLvXV1iWeey1mWsbGxAUC7\n3WZahS6QLekq4P7eDtWij+WP+wLZCzB27Ev+reOLZ5vtY14XyL7g+seSWn2PvQ/43qQBLJKs6gCs\nltxnTpyL8hQ5FPKLdP8K/0uSngFOAauSloHzwFngIzOM0czMxlSoLTPVBtyWWYixbsuYlWtebRmz\n0kna53bpiMfFpSMeH3Zrt1qjgzM74Fzc5yirOoDaiX1uOyMejwIjBt+e7nTm8u6Kcp85cS7K4+Ju\nZtZALu5ztFJ1AFZLnk8lcS7K4+JuZtZALu5zlFUdQJ0cgnT6xKDbkMcPN29nqPvMiXNRHs/nbtU4\nz/iHTwKs12tnqFld+Zv7HK1UHYDVkvvMiXNRHhd3M7MGcnGfo6zqAKyW3GdOnIvyuOduUzsK7KwX\nHHsZ7HxiltGYGbi4z9VK1QHMSO9M0SL0k1lGcjC5z5w4F+VxW8bMrIFc3OcoqzoAqyX3mRPnojwu\n7mZmDeSe+xytVB1AY0w8xXUtuc+cOBflGfnNXdLnJHUkfbdv3XFJD0p6UtIDkq6YbZhmSW/qXjMb\nrkhb5vPAL+5ZdxvwcES8GXgEuL3swJooqzoAqyX3mRPnojwji3tEfBN4Yc/qm4DT+f3TwM0lx2Vm\nZlOYdIfqlRHRAYiIbeDK8kJqrpWqA7Bacp85cS7KU9YO1X1boGtAO79/DFgmFbpsz9je8t7H5zW+\nt27U6427XHT7447vrZs2vkm3P+54AJ4acP/qgstc+H5fS/fPyqN0T6Ya5Uj+s9Vqs719drcN0Csq\nXvZyVctZlrGxsQFAu91mWipyBXpJVwH3R8S1+fITwEpEdCS1gM2IeOuQ5xa6xv2HLoHPvzzGmY4c\nvLGbFPv2Xpd4ZzWW9b7buNbTtvYeM1MkBu2OFEU++/OQZZm/seaci0QSETHxoWFF2zL9V1AAuI/u\nF3KADwL3ThqAmZmVr8ihkF8E/gj4y5KekfTrwKeAd0l6EnhHvmwjrFQdgNWSv6kmzkV5RvbcI+L9\nQx56Z8mxmJlZSTz9wBxlVQfQEHt7hAedj+1OnIvyePoBO3A22dy9v8pqhZGY1Ze/uc/RStUBWC25\nz5w4F+VxcTczayAX9znKqg7Aasl95sS5KI+Lu5lZA7m4z9FK1QFYLbnPnDgX5XFxNzNrIBf3Ocqq\nDsBqyX3mxLkoj4u7mVkDFZoVcqoNeFZIj+1zKcWm5u2N/b8Fx1Iwhv5ZIQdZWlpie3t7jK2azca0\ns0L6DFWbqx1mOJVwCTqdTkmvZFYtt2XmKKs6gBrJqg6gRtxnTpyL8ri4m5k1kHvuHrtQY0f13IHa\nXKHJFtu8rsRk1ghHgVEdeklI4tL8Z+vkSQDardbuY0Vv7VZr5u/JbJCpiruks5L+WNIZSd8pK6im\nyqoOoEayirbb26Fb5LYDsLlJ59w5AJ7udAo/t3d7usAOWveZE+eiPNMeLXOe7oWyXygjGDMzK8e0\nbRmV8BoLY6XqAGpkpeoAasTzqSTORXmmLcwBPCTpUUkfLiMgMzOb3rRtmRsi4oeSXk+3yD8REd/c\nO2gNaOf3jwHLpG9u2Z6xveW9j89rfG/dqNcbd3nv/SLji7x+b9208U26/XHH7103aXzTbr/o67Pa\nvYyfdGn3J8MtHTnCl1566YLnH6G7g3ZcVy0tcXZ7e7cH3ftG2/Tlu+66i+Xl5drEM8/lLMvY2NgA\noN1uM63SDoWUdAr43xFx5571PhQyH7tJsXZEXeKd5diM0bmY1aGQ4531Gmlpc3P4YIDV1Yteu8j2\nMi7OhVjMQzKzLHNrJlfZoZCSXi3p8vz+ZcC7ge9N+nqLYKXqAGpkpeoAamSl6gBqxIW9PNO0ZZaA\nr0qK/HX+Y0Q8WE5YZmY2jYm/uUfEUxGxHBHXRcRfiYhPlRlYE2VVB1AjWdUB1EhWdQA14uPcy+NZ\nIc321dfyzHeu7qd1GLZfmWE4ZgV5bhmPXaixY+9QXS84uGf9wtcfZ3t7t72IO1Qt8dwyZmZ2ERf3\nOcqqDqBGsqoDqJGs6gBqxD338ri4m5k1kHeoztFK1QHUyErVAcxQt0margA7qmm6xFG2C19Zttl8\nnHt5XNzNSte7GEixHaKd0q4Aa5a4LTNHWdUB1EhWdQBWS+65l8fF3cysgVzc52il6gBqZKXqAKyW\n3HMvj4u7WQ1ozw3StVxPnhx8HdbWifGv6apLRo9ptdrzedM2U96hOkcZ/sbak+Fc7DVsRuHV1cHX\nYe0815noDNpRO3o7nep28HrK3/L4m7uZWQO5uM/RStUB1MhK1QFYLflbe3lc3M3MGmiqWSEl3Qjc\nRfeXxOci4tMDxnhWSHyZvb1jM+p/mb1LofB5o+l81NGODnrdw8CAqYIHjh32upfBzidGDFqH0Rm4\nsOd+6NCrOX/+pwWjgKWlq9jePkvrRKu7X2BcQ3LRu67soph2VsiJd6hKOgT8O+AdwP8EHpV0b0R8\nf9LXbLot3I7oOQi52GF2v+Au2BH6X4EHGPgKO6j46/6k4MBC0lbPnx9v4uLeDtmJdvjmuRi0NXUm\n+EWxwKZpy7wd+EFEPB0RLwFfAm4qJ6xm+ouqA6gR56JP0a/8i8C5KM00xf0NwLm+5WfzdWZmVrG5\nHOf+dwp0jf7s5dnHUbWzVQdQI2erDqBO/GdM4lyUZuIdqpL+OrAeETfmy7cBsXenqiRfK8zMbALT\n7FCdprgfBp6ku0P1h8B3gH8QEU9MGoyZmZVj4rZMRLwi6VbgQdKhkC7sZmY1MNVx7mZmVk8zO0NV\n0o2Svi/pzyR9clbbqStJZyX9saQzkr6Trzsu6UFJT0p6QNIVVcc5C5I+J6kj6bt964a+d0m3S/qB\npCckvbuaqGdjSC5OSXpW0mP57ca+x5qcixOSHpH0J5Iel/SP8/UL99kYkIt/lK8v77MREaXf6P7S\n+O/AVcARuuesvGUW26rrDfgfwPE96z4N/NP8/ieBT1Ud54ze+98CloHvjnrvwM8DZ+i2CNv550ZV\nv4cZ5+IU8PEBY9/a8Fy0gOX8/uV099m9ZRE/G/vkorTPxqy+ufsEp+6JiHvzexNwOr9/Grh5rhHN\nSUR8E3hhz+ph7/3vA1+KiJcj4izwA7qfn0YYkgsYfN3sm2h2LrYjYiu//yLwBHCCBfxsDMlF7zyh\nUj4bsyruPsGpewb1Q5IelfSb+bqliOhA9x8XuLKy6ObvyiHvfe9n5TkW47Nyq6QtSXf3tSEWJheS\n2nT/ovkWw/9fLEQ++nLx7XxVKZ8Nzwo5OzdExPXAe4DfkvS3uXjKjEXem73I7/0zwDURsQxsA/+q\n4njmStLlwFeAj+bfWhf2/8WAXJT22ZhVcX8OONm3fCJftzAi4of5zx8DX6P7J1RH0hKApBbwo+oi\nnLth7/054I194xr/WYmIH0feSAU+S/rzuvG5kHQJ3WL2hYi4N1+9kJ+NQbko87Mxq+L+KPAmSVdJ\n+hngFuC+GW2rdiS9Ov+NjKTLgHcDj9PNwVo+7IPAvQNfoBn6LwcKw9/7fcAtkn5G0tXAm+ieENck\nF+QiL2A97wO+l99fhFz8B+BPI+Jf961b1M/GRbko9bMxw73BN9LdA/wD4Laq907P8wZcTfcIoTN0\ni/pt+frXAg/neXkQOFZ1rDN6/1+kOw30DvAM8OvA8WHvHbid7t7/J4B3Vx3/HHJxD/Dd/DPyNbo9\n50XIxQ10Z2rv/d94LK8TQ/9fNDUf++SitM+GT2IyM2sg71A1M2sgF3czswZycTczayAXdzOzBnJx\nNzNrIBd3M7MGcnE3M2sgF3czswb6/xPQsiH7vCg9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232698b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for player in players:\n",
    "    pd.Series(game_stat_player[player]).hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17622fc88>"
      ]
     },
     "execution_count": 598,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5A9RIuQMMkXIHGFBif9SZmikxE5Sbq42OFnczs8XmnvvUx5eQYf7Hu+duXeOeu5mZDeho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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17622f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(game_stat_player[\"Andre Drummond\"]).hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22510b128>"
      ]
     },
     "execution_count": 580,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x126852940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(game_stat_player[\"LeBron James\"]).hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Andre Drummond': 16399.46105701114,\n",
       " 'JR Smith': 17852.610626505801,\n",
       " 'Kentavious Caldwell-Pope': 17503.88761278295,\n",
       " 'Kevin Love': 17717.418610339799,\n",
       " 'Kyrie Irving': 18007.276116227902,\n",
       " 'LeBron James': 17343.091684050829,\n",
       " 'Marcus Morris': 18021.836690089378,\n",
       " 'Reggie Jackson': 16010.067066034761,\n",
       " 'Tobias Harris': 16368.583132916268,\n",
       " 'Tristan Thompson': 16553.480925519776}"
      ]
     },
     "execution_count": 581,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_score_player"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metric 2: entropy metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from scipy.stats import entropy\n",
    "\n",
    "def get_player_entropy(player_points):\n",
    "    '''\n",
    "    list of the points a player stood in\n",
    "    returns the entropy of places that a player covers\n",
    "    operates by folding the half-court on the basket\n",
    "    '''\n",
    "    folded_court_rep = []\n",
    "    for p in player_points:\n",
    "        if p[1] > 4:\n",
    "            p[1] = 4 - (p[1]-5)\n",
    "        folded_court_rep.append(str(p[0]) + str(p[1]))\n",
    "    probs = []\n",
    "    for u in set(folded_court_rep):\n",
    "        p_u = float(folded_court_rep.count(u)) / float(len(folded_court_rep))\n",
    "        probs.append(p_u)\n",
    "    return entropy(probs) \n",
    "\n",
    "\n",
    "def get_all_player_entropy(player_to_points):\n",
    "    '''\n",
    "    accepts a dictionary mapping player to the points they've stood in\n",
    "    returns a dictionary mapping player to entropy of that player's points\n",
    "    '''\n",
    "    player_to_entropy = {}\n",
    "    for p in player_to_points:\n",
    "        player_to_entropy[p] = get_player_entropy(player_to_points[p])\n",
    "    return player_to_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ball': [60.485130000000005, 38.396270000000001],\n",
       " 1610612739: [(30.189509999999999, 7.9699399999999994, 'JR Smith'),\n",
       "  (20.869050000000001, 47.918219999999998, 'Kyrie Irving'),\n",
       "  (34.619019999999992, 38.211870000000005, 'LeBron James'),\n",
       "  (28.253579999999999, 37.27487, 'Matthew Dellavedova'),\n",
       "  (24.908119999999997, 18.90042, 'Tristan Thompson')],\n",
       " 1610612765: [(13.665730000000011, 24.101779999999998, 'Andre Drummond'),\n",
       "  (15.40419, 40.850879999999997, 'Kentavious Caldwell-Pope'),\n",
       "  (20.349769999999992, 12.710239999999999, 'Marcus Morris'),\n",
       "  (28.584569999999999, 33.133299999999998, 'Stanley Johnson'),\n",
       "  (22.739509999999996, 30.560300000000002, 'Steve Blake')]}"
      ]
     },
     "execution_count": 584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def transf_frame_data(frame_data, players, teams):\n",
    "    '''\n",
    "    transform data structure for entropy calculation\n",
    "    '''\n",
    "    new_data = {}\n",
    "    for player in players:\n",
    "        for team in teams:\n",
    "            for k in range(5):\n",
    "                raw_data = frame_data[team][k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/300 [00:00<?, ?it/s]/Users/Chengyu/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:16: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n",
      "100%|██████████| 300/300 [00:01<00:00, 231.54it/s]\n"
     ]
    }
   ],
   "source": [
    "grid_centers, grid_vals = gen.get_midpoints_and_values()\n",
    "            \n",
    "player_spot_lst = {}\n",
    "for player in players:\n",
    "    player_spot_lst[player] = []\n",
    "\n",
    "entropy_stat = []\n",
    "for sample in tqdm(sample_rows):\n",
    "    frame_data = get_frame_data(df.iloc[sample], teams)\n",
    "    points = ds2points(frame_data, teams)\n",
    "    kdtree_orig = cKDTree(points)\n",
    "    for i in range(10):\n",
    "        for j in range(10):\n",
    "            center = grid_centers[i][j]\n",
    "            test_point_dist, test_point_regions = kdtree_orig.query(np.array(center).reshape((1,2)), k=1)\n",
    "            player = players[test_point_regions]\n",
    "            player_spot_lst[player].append([i,j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Andre Drummond': 3.6934732788915241,\n",
       " 'JR Smith': 3.8751497704295153,\n",
       " 'Kentavious Caldwell-Pope': 3.8749978107795542,\n",
       " 'Kevin Love': 3.8905072964556422,\n",
       " 'Kyrie Irving': 3.8852025633440435,\n",
       " 'LeBron James': 3.8932905331228298,\n",
       " 'Marcus Morris': 3.8640898689640215,\n",
       " 'Reggie Jackson': 3.8721031791284171,\n",
       " 'Tobias Harris': 3.8945519096433991,\n",
       " 'Tristan Thompson': 3.8740878218059867}"
      ]
     },
     "execution_count": 597,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_all_player_entropy(player_spot_lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metric 3 Raw Land Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import generate_spot_values as gen\n",
    "'''\n",
    "accepts a list of the indices in the values matrix that the player owns\n",
    "returns a summation of the value of the player's land\n",
    "'''\n",
    "def total_value_of_player_land(idxs):\n",
    "    pos_values = gen.gen_position_values()\n",
    "    total_val = 0.0\n",
    "    for idx in idxs:\n",
    "        total_val += pos_values[idx[0]][idx[1]]\n",
    "    return total_val  \n",
    "\n",
    "\n",
    "'''\n",
    "accepts a dictionary mapping player name to a list of the points they own\n",
    "returns a dictionary mapping player name to the total value of the points they own\n",
    "'''\n",
    "def all_players_land_value(players_dict):\n",
    "    player_to_value = {}\n",
    "    for p_name in players_dict:\n",
    "        p_land = players_dict[p_name]\n",
    "        player_to_value[p_name] = total_value_of_player_land(p_land)\n",
    "    return player_to_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Andre Drummond': 103785.8999999998,\n",
       " 'JR Smith': 120375.0999999995,\n",
       " 'Kentavious Caldwell-Pope': 110647.49999999939,\n",
       " 'Kevin Love': 116461.69999999943,\n",
       " 'Kyrie Irving': 122227.49999999965,\n",
       " 'LeBron James': 117621.69999999974,\n",
       " 'Marcus Morris': 115579.99999999946,\n",
       " 'Reggie Jackson': 115684.49999999921,\n",
       " 'Tobias Harris': 114495.89999999959,\n",
       " 'Tristan Thompson': 99340.1999999997}"
      ]
     },
     "execution_count": 604,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_players_land_value(player_spot_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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