train_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "sns.set()\n",
    "\n",
    "import common\n",
    "import evaluate\n",
    "import nba.importer as importer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_file = f\"output\\\\nba\\\\test\\\\train.csv\"\n",
    "model_file = f\"models\\\\nba\\\\model.pkl\"\n",
    "\n",
    "#model = common.load_model(model_file)\n",
    "data, y = common.read_data_from_file(test_file, \"home_win\", [\"counter\", \"home_win\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counter</th>\n",
       "      <th>home_win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>103</td>\n",
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       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
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       "      <th>5</th>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
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       "      <th>8</th>\n",
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       "      <th>9</th>\n",
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       "      <th>10</th>\n",
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       "      <th>11</th>\n",
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       "      <th>12</th>\n",
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       "      <th>13</th>\n",
       "      <td>114</td>\n",
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       "      <th>14</th>\n",
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       "      <th>16</th>\n",
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       "      <th>17</th>\n",
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       "      <th>18</th>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>126</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>127</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>1182</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>1183</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>1184</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>1185</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>1186</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>1187</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>1188</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>1189</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>1190</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>1192</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>1193</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>1194</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>1195</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>1196</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>1197</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>1198</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>1199</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>1200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>1201</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>1202</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>1203</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>1204</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>1205</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>1206</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>1207</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>1208</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>1209</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>1210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>1211</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1111 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      counter  home_win\n",
       "0         101         1\n",
       "1         102         1\n",
       "2         103         0\n",
       "3         104         1\n",
       "4         105         1\n",
       "5         106         0\n",
       "6         107         1\n",
       "7         108         1\n",
       "8         109         0\n",
       "9         110         1\n",
       "10        111         0\n",
       "11        112         0\n",
       "12        113         1\n",
       "13        114         1\n",
       "14        115         0\n",
       "15        116         1\n",
       "16        117         0\n",
       "17        118         0\n",
       "18        119         0\n",
       "19        120         1\n",
       "20        121         1\n",
       "21        122         1\n",
       "22        123         1\n",
       "23        124         0\n",
       "24        125         0\n",
       "25        126         1\n",
       "26        127         1\n",
       "27        128         0\n",
       "28        129         0\n",
       "29        130         0\n",
       "...       ...       ...\n",
       "1081     1182         0\n",
       "1082     1183         0\n",
       "1083     1184         1\n",
       "1084     1185         0\n",
       "1085     1186         1\n",
       "1086     1187         0\n",
       "1087     1188         1\n",
       "1088     1189         0\n",
       "1089     1190         0\n",
       "1090     1191         0\n",
       "1091     1192         0\n",
       "1092     1193         0\n",
       "1093     1194         0\n",
       "1094     1195         1\n",
       "1095     1196         0\n",
       "1096     1197         0\n",
       "1097     1198         0\n",
       "1098     1199         1\n",
       "1099     1200         1\n",
       "1100     1201         0\n",
       "1101     1202         1\n",
       "1102     1203         1\n",
       "1103     1204         1\n",
       "1104     1205         0\n",
       "1105     1206         0\n",
       "1106     1207         0\n",
       "1107     1208         1\n",
       "1108     1209         1\n",
       "1109     1210         0\n",
       "1110     1211         1\n",
       "\n",
       "[1111 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150fd10d128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats = common.confidence_stats(model, X, y)\n",
    "\n",
    "ranges = []\n",
    "pcts = []\n",
    "cords = []\n",
    "\n",
    "counter = 1\n",
    "for s in stats:\n",
    "    ranges.append(s.conf_range[0])\n",
    "    pcts.append(s.pct)\n",
    "    cords.append(counter)\n",
    "    counter += 3\n",
    "\n",
    "plt.figure(figsize=(18, 8), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.bar(cords, pcts, width=1, tick_label=ranges)\n",
    "#plt.xticks(rotation=70)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def team_summary(data):\n",
    "    \n",
    "    def update_records(team_stat):\n",
    "        pct = team_stat[\"correct\"] / team_stat[\"total\"]\n",
    "        real_pct = team_stat[\"real\"] / team_stat[\"total\"]\n",
    "\n",
    "        team_stat[\"record\"] = real_pct\n",
    "        team_stat[\"betting\"] = pct\n",
    "\n",
    "    total = 0\n",
    "    correct = 0\n",
    "\n",
    "    teams = {}\n",
    "\n",
    "    for index, row in data.iterrows():\n",
    "\n",
    "        features = [X.iloc[index]]\n",
    "\n",
    "        predicted = model.predict(features)[0]\n",
    "        real = y.iloc[index]\n",
    "\n",
    "        confidences = model.predict_proba(features)[0]\n",
    "        max_confidence = max(confidences)\n",
    "\n",
    "        total += 1\n",
    "\n",
    "        if real == predicted: correct += 1\n",
    "\n",
    "        home = row[\"home\"]\n",
    "        away = row[\"away\"]\n",
    "\n",
    "        if home not in teams: teams[home]={\"correct\":0, \"total\":0, \"real\":0}\n",
    "        if away not in teams: teams[away]={\"correct\":0, \"total\":0, \"real\":0}\n",
    "\n",
    "        home_team = teams[home]\n",
    "        away_team = teams[away]\n",
    "\n",
    "        if real == 1 and predicted == 1:\n",
    "            home_team[\"correct\"] += 1\n",
    "            away_team[\"correct\"] += 1\n",
    "        if real == 0 and predicted == 0:\n",
    "            home_team[\"correct\"] += 1\n",
    "            away_team[\"correct\"] += 1\n",
    "        if real == 1:\n",
    "            home_team[\"real\"] += 1\n",
    "        if real == 0:\n",
    "            away_team[\"real\"] += 1\n",
    "\n",
    "        home_team[\"total\"] += 1\n",
    "        away_team[\"total\"] += 1\n",
    "\n",
    "        update_records(home_team)\n",
    "        update_records(away_team)\n",
    "        \n",
    "        #print(f\"{correct}/{total} = {correct/total:.4f}\")\n",
    "        \n",
    "    return teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "teams = team_summary(data)\n",
    "\n",
    "names = []\n",
    "betting = []\n",
    "record = []\n",
    "cords = []\n",
    "cords_betting = []\n",
    "counter = 0\n",
    "\n",
    "for k in sorted(teams.items(), key=lambda d: d[1][\"correct\"]/d[1][\"total\"]):\n",
    "    team_name = k[0]\n",
    "    team_stat = k[1]\n",
    "    \n",
    "    pct = team_stat[\"correct\"] / team_stat[\"total\"]\n",
    "    real_pct = team_stat[\"real\"] / team_stat[\"total\"]\n",
    "    \n",
    "    names.append(team_name)\n",
    "    betting.append(pct)\n",
    "    record.append(real_pct)\n",
    "    \n",
    "    cords.append(counter * 10)\n",
    "    cords_betting.append(counter * 10 + 2)\n",
    "    counter += 1\n",
    "    \n",
    "    #print(f\"{team_name:<30} {real_pct:.2f}  {pct:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150fd0eadd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 10), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.bar(cords, record, width=2, tick_label=names)\n",
    "plt.bar(cords_betting, betting, width=2, tick_label=names)\n",
    "plt.xticks(rotation=70)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>34</th>\n",
       "      <th>87</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>41</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>38</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>38</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>41</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>39</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>34</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>44</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>45</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>38</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>39</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>34</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>41</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>45</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>50</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>43</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>40</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>49</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>41</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>44</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>29</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>41</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>44</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>41</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>25</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>41</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>42</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>40</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>39</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>41</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>35</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>44</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>35</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>32</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>32</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>41</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>42</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>35</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>37</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>41</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>37</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>48</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>38</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>35</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>48</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>48</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>39</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>42</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>46</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>31</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>46</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>43</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>38</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>232 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     34   87\n",
       "0    33   91\n",
       "1    42   85\n",
       "2    40   82\n",
       "3    52   98\n",
       "4    25   84\n",
       "5    33   87\n",
       "6    41   90\n",
       "7    38   97\n",
       "8    38   88\n",
       "9    41   79\n",
       "10   39   91\n",
       "11   34   85\n",
       "12   44  101\n",
       "13   45   93\n",
       "14   38   97\n",
       "15   39   97\n",
       "16   34   95\n",
       "17   41   83\n",
       "18   38   87\n",
       "19   45   93\n",
       "20   50   96\n",
       "21   43   92\n",
       "22   40   99\n",
       "23   49   87\n",
       "24   41  101\n",
       "25   44   84\n",
       "26   29   77\n",
       "27   41   86\n",
       "28   44   96\n",
       "29   41   93\n",
       "..   ..  ...\n",
       "202  25   80\n",
       "203  41   93\n",
       "204  42   83\n",
       "205  40   94\n",
       "206  39   88\n",
       "207  41   86\n",
       "208  35   83\n",
       "209  44   93\n",
       "210  35   77\n",
       "211  32   92\n",
       "212  32   76\n",
       "213  41  103\n",
       "214  42   97\n",
       "215  35   93\n",
       "216  37   80\n",
       "217  41   94\n",
       "218  37   88\n",
       "219  48   98\n",
       "220  41   88\n",
       "221  38   90\n",
       "222  35   78\n",
       "223  48   95\n",
       "224  48   89\n",
       "225  39   93\n",
       "226  42   94\n",
       "227  46  102\n",
       "228  31   87\n",
       "229  46   95\n",
       "230  43   90\n",
       "231  38   85\n",
       "\n",
       "[232 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_file = \"input\\\\nba\\\\2018.csv\"\n",
    "\n",
    "_, stats = importer.generate_output_and_stats(2018, result_file)\n",
    "\n",
    "results = pd.read_csv(result_file)\n",
    "results.head()\n",
    "\n",
    "results[[\"34\", \"87\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x150fd313240>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150fd1c5f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(results['away_points'], bins=10, kde=False, color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x150fd31df60>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150fd369c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(results['home_points'], bins=10, kde=False, color=\"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x150fd588cf8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150fd3a0208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(stats[\"hou\"][\"scored\"], bins=10, kde=False, color=\"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x150fd3e1e10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD3CAYAAAANMK+RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAADGVJREFUeJzt3W9sXfdZwPGvfU2bZXWCiW5A2jRVo+zRpsG0jpKgdVqiBVWFhqIqLxASL6BU2hTBKm0KCCYtaEKomtYpY2UV6f6waRJCCR30RaCaZqo2UMqgYxSqp1toBVvZfBu5sUeS0VybF3bFTeQ/16fXO87T7+fVOccn5z6Roq9/Offf2OLiIpKkq9942wNIkkbDoEtSEQZdkoow6JJUhEGXpCIm2nzwXm/el9hI0gZ1u5NjKx13hS5JRRh0SSrCoEtSEQZdkoow6JJUhEGXpCIMuiQVYdAlqYhW31ikreO++44xPf3ltsfYEubn5wCYnNzR8iRbw/79Bzh8+P1tj6EhuEKXrnDhwkUuXLjY9hjSho21+QUXvvVfW9GhQwcBOHHioZYnkVbmW/8lqTiDLklFGHRJKsKgS1IRBl2SijDoklSEQZekIgy6JBVh0CWpiKE+yyUi9gD3ZOa+gWMfBzIz71/e/wTwTmB++ZTbM/PcaMeVJK1m3aBHxBHgV4H/Wd7vAp8H3gR8dODUG4FbMvOFTZhTkrSOYW65nAHuGNi/DjgKfOHlAxExDvwE8CcRcToifn2UQ0qS1rfuCj0zT0bE9QP7zwLPRsStA6e9Fvgj4F6gA0xHxFcz8+trXXtqajsTE51Gg0ubpdNZWud0u5MtTyJtzKg+D/08cCwzzwNExFeAtwFrBn129vyIHl4anX5/AYBeb36dM6V2rLbYGFXQ3wT8WUTcyNJtnJuBPx3RtTfN+953J73eTNtjaIuZmVn6N/Hyx+hKL+t2d/OpT3267TFWNZKgZ+bTEfFF4HHgJeDzmflvo7j2Zur1Zvjv7z7PwraFtkfRFjI2vvRR098+962WJ9FWMn5x67/K+1X9BReHDh3k2+e+xdn9Z9scQ9JVYNf0Ll638/Vb4otP/IILSSrOoEtSEQZdkoow6JJUhEGXpCIMuiQVYdAlqQiDLklFGHRJKsKgS1IRBl2SijDoklSEQZekIgy6JBVh0CWpCIMuSUUYdEkqwqBLUhEGXZKKMOiSVIRBl6QiDLokFWHQJakIgy5JRRh0SSrCoEtSEUMFPSL2RMTfXnHs4xHx3oH9uyLiqxHxeETcNuI5JUnrWDfoEXEEeADYtrzfjYhTwC8OnPNjwG8B7wRuAf4wIq7dlIklSSsaZoV+BrhjYP864CjwhYFjPwOczszvZ+Y54JvAT41qSEnS+ibWOyEzT0bE9QP7zwLPRsStA6ftAM4N7M8DO9e79tTUdiYmOsNPO2Kdjk8hSBpepzNOtzvZ9hirWjfoQ5oDBv+Wk8CL6/2h2dnzI3r4Zvr9hVYfX9LVpd9foNebb3uMVX+pjCroTwB/EBHbgGuBNwNPjejakqQhjOSeQ2Z+B/gE8CjwFeD3MvPiKK4tSRrOUCv0zHwO2HvFsaNX7B8Hjo9qMEnSxvisoCQVYdAlqQiDLklFGHRJKsKgS1IRBl2SijDoklSEQZekIgy6JBVh0CWpCIMuSUUYdEkqwqBLUhEGXZKKMOiSVIRBl6QiDLokFWHQJakIgy5JRRh0SSrCoEtSEQZdkoow6JJUhEGXpCIMuiQVYdAlqYiJYU6KiD3APZm5LyJuAD4HLAJPAYczcyEi/grYBbwEXMjMWzdpZknSCtZdoUfEEeABYNvyoXuBD2Xmu4Ax4Pbl4zcAN2fmPmMuST94w9xyOQPcMbD/DuCR5e1TwIGI+FHgh4GHIuKxiLhttGNKktaz7i2XzDwZEdcPHBrLzMXl7XlgJ3AN8DHgGPAjwOmIeCIzZ9a69tTUdiYmOo0GH4VOx6cQJA2v0xmn251se4xVDXUP/QoLA9uTwIvAd4D7M/MSMBMRTwIBrBn02dnzDR5+dPr9hfVPkqRl/f4Cvd5822Os+kulyRL1yYjYt7x9K/AocAD4c4CIuA54K/B0g2tLkhpqskL/AHA8Iq5hKdonMrMfEbdExOMsreB/NzNfGOWgkqS1DRX0zHwO2Lu8/Qzw7hXOuXukk0mSNsRnBSWpiCa3XMqYm5tj/OI4u6Z3tT2KpC1u/OI4c2NzbY+xJlfoklTEq3qFvmPHDuYX5zi7/2zbo0ja4nZN72LHjh1tj7EmV+iSVIRBl6QiDLokFWHQJakIgy5JRRh0SSrCoEtSEQZdkoow6JJUhEGXpCIMuiQVYdAlqQiDLklFGHRJKsKgS1IRBl2SijDoklSEQZekIgy6JBVh0CWpCIMuSUUYdEkqYmKYkyJiD3BPZu6LiBuAzwGLwFPA4cxciIgPA78AXALuzswnNmlmSdIK1l2hR8QR4AFg2/Khe4EPZea7gDHg9oi4EXg3sAf4ZeC+zRlXkrSaYW65nAHuGNh/B/DI8vYp4ABwM/BwZi5m5n8CExHRHemkkqQ1rXvLJTNPRsT1A4fGMnNxeXse2AnsAM4OnPPy8d5a156a2s7ERGdDA49Sp+NTCJKG1+mM0+1Otj3Gqoa6h36FhYHtSeBFYG55+8rja5qdPd/g4Uen319Y/yRJWtbvL9Drzbc9xqq/VJosUZ+MiH3L27cCjwKngVsiYjwi3gCMZ+YLTQaVJDXTZIX+AeB4RFwDPA2cyMx+RDwK/D1LvyQOj3BGSdIQhgp6Zj4H7F3efoalV7Rcec5R4OjoRpMkbYTPCkpSEQZdkoow6JJUhEGXpCIMuiQVYdAlqQiDLklFGHRJKsKgS1IRBl2SijDoklSEQZekIgy6JBVh0CWpCIMuSUUYdEkqwqBLUhEGXZKKMOiSVIRBl6QiDLokFWHQJakIgy5JRRh0SSrCoEtSERNtD9C28Yvj7Jre1fYY2kLGXhoDYPGHFlueRFvJ+MVx2Nn2FGt7VQe9293d9gjagmZmZgDYvdN/Hxqwc+s3Y2xxceOrkIi4Fvgs8EZgDjgM/CTwUeC/lk/7cGY+stZ1er15l0Dacg4dOgjAiRMPtTyJtLJud3JspeNNV+h3Ad/LzL0REcAngX8EjmTmyYbXlCS9Ak2D/hbgFEBmZkS8GVgA3h4RdwNPAL+dmZfWusjU1HYmJjoNR5A2R6ez9FqBbney5UmkjWka9K8Bt0XEl4A9wOuAY8CDwLPA/cB7WVq5r2p29nzDh5c2T7+/AECvN9/yJNLKVltsNH3Z4mdYunc+DRwE/gn4dGb+R2YuAn8JvL3htSVJDTQN+k3AY5m5j/9flX89Il6//PP3sBR5SdIPSNNbLt8APhIRHwReBO4E3gr8RURcAP4dOD6aESVJw2gU9Mx8AThwxeHngYdf8USSpEZ8678kFWHQJakIgy5JRRh0SSrCoEtSEQZdkoow6JJUhEGXpCIMuiQVYdAlqQiDLklFGHRJKsKgS1IRBl2SijDoklSEQZekIgy6JBVh0CWpCIMuSUUYdEkqwqBLUhEGXZKKMOiSVIRBl6QiDLokFWHQJamIiSZ/KCKuBT4LvBGYAw4Du4BjwCXg4cz8/VENKUlaX9MV+l3A9zJzL/CbwCeB+4FfAW4G9kTEjaMZUZI0jEYrdOAtwCmAzMyIuAn4bmaeAYiIvwHeA/zzWheZmtrOxESn4QjS5uh0ltY53e5ky5NIG9M06F8DbouILwF7gJ3AmYGfz7N0O2ZNs7PnGz68tHn6/QUAer35lieRVrbaYqPpLZfPsHTvfBo4CPwL8NqBn08CLza8tiSpgaZBvwl4LDP3AQ8CzwD/GxE/HhFjwC3Ao6MZUZI0jKa3XL4BfCQiPsjSSvxO4A3AF4EOS69y+YfRjChJGkajoGfmC8CBKw4/D+x9xRNJkhrxjUWSVIRBl6QiDLokFWHQJakIgy5JRRh0SSrCoEtSEWOLi4utPXivN9/eg+sy9913jOnpL7c9xpYwMzMDwO7du1ueZGvYv/8Ahw+/v+0xNKDbnRxb6XjTd4pKZb3mNdvaHkFqxBW6JF1lVluhew9dkoow6JJUhEGXpCIMuiQVYdAlqQiDLklFGHRJKsKgS1IRrb6xSJI0Oq7QJakIgy5JRRh0SSrCoEtSEQZdkoow6JJUhEGXpCL8xiJpQESMA38MvA34PvAbmfnNdqeShuMKXbrcLwHbMvNngd8BPtbyPNLQDLp0uZuBvwbIzMeBn253HGl4Bl263A7g3MB+PyK8NamrgkGXLjcHTA7sj2fmpbaGkTbCoEuXOw38PEBE7AX+td1xpOH5X0npcg8CPxcRfweMAb/W8jzS0Pz4XEkqwlsuklSEQZekIgy6JBVh0CWpCIMuSUUYdEkqwqBLUhH/B/jmg0cEdukAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150fd415588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=stats[\"orl\"][\"scored\"], color=\"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def team_summary(stats, team_name):\n",
    "    print(team_name)\n",
    "    print(stats[team_name][\"scored\"])\n",
    "    print(stats[team_name][\"allowed\"])\n",
    "    print([\"W\" if x == 1 else \"L\" for x in stats[team_name][\"wins\"]])\n",
    "    sns.boxplot(data=stats[team_name][\"scored\"], color=\"blue\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "por\n",
      "[128, 121, 124, 128, 111, 103, 104, 132, 110, 111, 118, 116, 100, 117]\n",
      "[119, 108, 125, 114, 120, 93, 85, 119, 114, 81, 103, 105, 94, 126]\n",
      "['W', 'W', 'L', 'W', 'L', 'W', 'W', 'W', 'L', 'W', 'W', 'W', 'W', 'L']\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150fd58bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "vs\n",
      "\n",
      "min\n",
      "[108, 131, 136, 101, 105, 95, 124, 128, 99, 81, 109, 110, 110, 120, 107]\n",
      "[112, 123, 140, 91, 112, 125, 120, 125, 116, 111, 120, 114, 121, 113, 100]\n",
      "['L', 'W', 'L', 'W', 'L', 'L', 'W', 'W', 'L', 'L', 'L', 'L', 'L', 'W', 'W']\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150fd24f438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "team_summary(stats, \"por\")\n",
    "print()\n",
    "print(\"vs\")\n",
    "print()\n",
    "team_summary(stats, \"min\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.boxplot(data=stats[\"sas\"][\"scored\"], color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats[\"San Antonio Spurs\"][\"scored\"]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}