01.1UpsetPrediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UPSET DETECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most important part of training an effective March Madness model is predicting upsets. The tendency for the model is just to spit out the higher seed because obviously the higher seed wins more often ($73\\%$ since 2003) of games. But $27\\%$ is not an insignificant percentage. The purpose of this notebook is to analyze trends in tournament upsets and generate a model to predict when there will be an upset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from concurrent.futures import ProcessPoolExecutor\n",
    "import sys\n",
    "import os\n",
    "import json\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV, KFold, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier, LassoCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "sys.path.append(\"/Users/atticussoane/Desktop/atticus_tools/\")\n",
    "from recursive_selection import FeatureSelector\n",
    "from dataclasses import dataclass, field"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Historical Seed Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_results = pd.read_csv(\"./MDataFiles_Stage1/MNCAATourneyCompactResults.csv\")\n",
    "historical_seeds = pd.read_csv(\"./MDataFiles_Stage1/MNCAATourneySeeds.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Seed</th>\n",
       "      <th>TeamID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>W01</td>\n",
       "      <td>1207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>W02</td>\n",
       "      <td>1210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>W03</td>\n",
       "      <td>1228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985</td>\n",
       "      <td>W04</td>\n",
       "      <td>1260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>W05</td>\n",
       "      <td>1374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season Seed  TeamID\n",
       "0    1985  W01    1207\n",
       "1    1985  W02    1210\n",
       "2    1985  W03    1228\n",
       "3    1985  W04    1260\n",
       "4    1985  W05    1374"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_seeds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>DayNum</th>\n",
       "      <th>WTeamID</th>\n",
       "      <th>WScore</th>\n",
       "      <th>LTeamID</th>\n",
       "      <th>LScore</th>\n",
       "      <th>WLoc</th>\n",
       "      <th>NumOT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1116</td>\n",
       "      <td>63</td>\n",
       "      <td>1234</td>\n",
       "      <td>54</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1120</td>\n",
       "      <td>59</td>\n",
       "      <td>1345</td>\n",
       "      <td>58</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1207</td>\n",
       "      <td>68</td>\n",
       "      <td>1250</td>\n",
       "      <td>43</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1229</td>\n",
       "      <td>58</td>\n",
       "      <td>1425</td>\n",
       "      <td>55</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1242</td>\n",
       "      <td>49</td>\n",
       "      <td>1325</td>\n",
       "      <td>38</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season  DayNum  WTeamID  WScore  LTeamID  LScore WLoc  NumOT\n",
       "0    1985     136     1116      63     1234      54    N      0\n",
       "1    1985     136     1120      59     1345      58    N      0\n",
       "2    1985     136     1207      68     1250      43    N      0\n",
       "3    1985     136     1229      58     1425      55    N      0\n",
       "4    1985     136     1242      49     1325      38    N      0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['W01', 'W02', 'W03', 'W04', 'W05', 'W06', 'W07', 'W08', 'W09',\n",
       "       'W10', 'W11', 'W12', 'W13', 'W14', 'W15', 'W16', 'X01', 'X02',\n",
       "       'X03', 'X04', 'X05', 'X06', 'X07', 'X08', 'X09', 'X10', 'X11',\n",
       "       'X12', 'X13', 'X14', 'X15', 'X16', 'Y01', 'Y02', 'Y03', 'Y04',\n",
       "       'Y05', 'Y06', 'Y07', 'Y08', 'Y09', 'Y10', 'Y11', 'Y12', 'Y13',\n",
       "       'Y14', 'Y15', 'Y16', 'Z01', 'Z02', 'Z03', 'Z04', 'Z05', 'Z06',\n",
       "       'Z07', 'Z08', 'Z09', 'Z10', 'Z11', 'Z12', 'Z13', 'Z14', 'Z15',\n",
       "       'Z16', 'Y16a', 'Y16b', 'W16a', 'W16b', 'X16a', 'X16b', 'Z16a',\n",
       "       'Z16b', 'W12a', 'W12b', 'Z11a', 'Z11b', 'X12a', 'X12b', 'Z14a',\n",
       "       'Z14b', 'Y11a', 'Y11b', 'Z13a', 'Z13b', 'Y12a', 'Y12b', 'W11a',\n",
       "       'W11b', 'X11a', 'X11b'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_seeds.Seed.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_results = historical_results.merge(historical_seeds, how = \"left\",\n",
    "                                             left_on = [\"Season\", \"WTeamID\"],\n",
    "                                             right_on = [\"Season\", \"TeamID\"])\n",
    "historical_results = historical_results.drop(columns = [\"TeamID\"]).rename(columns = {\"Seed\" : \"WSeed\"})\n",
    "historical_results = historical_results.merge(historical_seeds, how = \"left\",\n",
    "                                             left_on = [\"Season\", \"LTeamID\"],\n",
    "                                             right_on = [\"Season\", \"TeamID\"])\n",
    "historical_results = historical_results.drop(columns = [\"TeamID\"]).rename(columns = {\"Seed\" : \"LSeed\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Season</th>\n",
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       "      <th>0</th>\n",
       "      <td>1985</td>\n",
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       "      <td>1234</td>\n",
       "      <td>54</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>X09</td>\n",
       "      <td>X08</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1120</td>\n",
       "      <td>59</td>\n",
       "      <td>1345</td>\n",
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       "      <td>N</td>\n",
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       "      <td>Z11</td>\n",
       "      <td>Z06</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1207</td>\n",
       "      <td>68</td>\n",
       "      <td>1250</td>\n",
       "      <td>43</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>W01</td>\n",
       "      <td>W16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1229</td>\n",
       "      <td>58</td>\n",
       "      <td>1425</td>\n",
       "      <td>55</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>Y09</td>\n",
       "      <td>Y08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1242</td>\n",
       "      <td>49</td>\n",
       "      <td>1325</td>\n",
       "      <td>38</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>Z03</td>\n",
       "      <td>Z14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Season  DayNum  WTeamID  WScore  LTeamID  LScore WLoc  NumOT WSeed LSeed\n",
       "0    1985     136     1116      63     1234      54    N      0   X09   X08\n",
       "1    1985     136     1120      59     1345      58    N      0   Z11   Z06\n",
       "2    1985     136     1207      68     1250      43    N      0   W01   W16\n",
       "3    1985     136     1229      58     1425      55    N      0   Y09   Y08\n",
       "4    1985     136     1242      49     1325      38    N      0   Z03   Z14"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Season</th>\n",
       "      <th>DayNum</th>\n",
       "      <th>WTeamID</th>\n",
       "      <th>WScore</th>\n",
       "      <th>LTeamID</th>\n",
       "      <th>LScore</th>\n",
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       "      <td>1985</td>\n",
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       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
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       "      <td>1345</td>\n",
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       "      <td>N</td>\n",
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       "      <td>11</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1207</td>\n",
       "      <td>68</td>\n",
       "      <td>1250</td>\n",
       "      <td>43</td>\n",
       "      <td>N</td>\n",
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       "      <td>1</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1229</td>\n",
       "      <td>58</td>\n",
       "      <td>1425</td>\n",
       "      <td>55</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1242</td>\n",
       "      <td>49</td>\n",
       "      <td>1325</td>\n",
       "      <td>38</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
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      ],
      "text/plain": [
       "   Season  DayNum  WTeamID  WScore  LTeamID  LScore WLoc  NumOT  WseedN  \\\n",
       "0    1985     136     1116      63     1234      54    N      0       9   \n",
       "1    1985     136     1120      59     1345      58    N      0      11   \n",
       "2    1985     136     1207      68     1250      43    N      0       1   \n",
       "3    1985     136     1229      58     1425      55    N      0       9   \n",
       "4    1985     136     1242      49     1325      38    N      0       3   \n",
       "\n",
       "   LseedN  \n",
       "0       8  \n",
       "1       6  \n",
       "2      16  \n",
       "3       8  \n",
       "4      14  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def prep_seeds(s):\n",
    "    s = s[1:]\n",
    "    s = s.replace(\"a\", \"\", 1)\n",
    "    s = s.replace(\"b\", \"\", 1)\n",
    "    return(s)\n",
    "\n",
    "historical_results[\"WseedN\"] = historical_results.WSeed.map(lambda x: prep_seeds(x)).astype(\"int32\")\n",
    "historical_results[\"LseedN\"] = historical_results.LSeed.map(lambda x: prep_seeds(x)).astype(\"int32\")\n",
    "\n",
    "historical_results = historical_results.drop(columns = [\"WSeed\", \"LSeed\"])\n",
    "historical_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "round_map = {\n",
    "    134 : 0,\n",
    "    135 : 0,\n",
    "    136 : 1,\n",
    "    137 : 1,\n",
    "    138 : 2,\n",
    "    139 : 2,\n",
    "    143 : 3,\n",
    "    144 : 3,\n",
    "    145 : 4,\n",
    "    146 : 4,\n",
    "    152 : 5,\n",
    "    154 : 6\n",
    "    \n",
    "}\n",
    "\n",
    "historical_results[\"tourney_round\"] = historical_results.DayNum.map(round_map)\n",
    "\n",
    "upsets = []\n",
    "for k in range(len(historical_results)):\n",
    "    if historical_results.iloc[k][\"WseedN\"] > historical_results.iloc[k][\"LseedN\"]:\n",
    "        upsets.append(1)\n",
    "    else:\n",
    "        upsets.append(0)\n",
    "historical_results[\"is_upset\"] = pd.Series(upsets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>DayNum</th>\n",
       "      <th>WTeamID</th>\n",
       "      <th>WScore</th>\n",
       "      <th>LTeamID</th>\n",
       "      <th>LScore</th>\n",
       "      <th>WLoc</th>\n",
       "      <th>NumOT</th>\n",
       "      <th>WseedN</th>\n",
       "      <th>LseedN</th>\n",
       "      <th>tourney_round</th>\n",
       "      <th>is_upset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1116</td>\n",
       "      <td>63</td>\n",
       "      <td>1234</td>\n",
       "      <td>54</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1120</td>\n",
       "      <td>59</td>\n",
       "      <td>1345</td>\n",
       "      <td>58</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1207</td>\n",
       "      <td>68</td>\n",
       "      <td>1250</td>\n",
       "      <td>43</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1229</td>\n",
       "      <td>58</td>\n",
       "      <td>1425</td>\n",
       "      <td>55</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1985</td>\n",
       "      <td>136</td>\n",
       "      <td>1242</td>\n",
       "      <td>49</td>\n",
       "      <td>1325</td>\n",
       "      <td>38</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season  DayNum  WTeamID  WScore  LTeamID  LScore WLoc  NumOT  WseedN  \\\n",
       "0    1985     136     1116      63     1234      54    N      0       9   \n",
       "1    1985     136     1120      59     1345      58    N      0      11   \n",
       "2    1985     136     1207      68     1250      43    N      0       1   \n",
       "3    1985     136     1229      58     1425      55    N      0       9   \n",
       "4    1985     136     1242      49     1325      38    N      0       3   \n",
       "\n",
       "   LseedN  tourney_round  is_upset  \n",
       "0       8              1         1  \n",
       "1       6              1         1  \n",
       "2      16              1         0  \n",
       "3       8              1         1  \n",
       "4      14              1         0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "def upset_analysis_by_round(df):\n",
    "    \n",
    "    def handshake_problem(l):\n",
    "        if type(l) == set:\n",
    "            l = list(l)\n",
    "        all_combos = []\n",
    "        l = sorted(l)\n",
    "        for i in range(len(l)- 1):\n",
    "            j = i\n",
    "            all_combos.append((l[i], l[j]))\n",
    "            while j < len(l) -1 :\n",
    "                j += 1\n",
    "                all_combos.append((l[i], l[j]))\n",
    "        return(all_combos)\n",
    "    by_round = {}\n",
    "    all_rounds = [1, 2, 3, 4, 5, 6]\n",
    "    for rnd in all_rounds:\n",
    "        \n",
    "        all_seeds = set(list(df[df.tourney_round == rnd].WseedN.unique()) +\\\n",
    "        list(df[df.tourney_round == rnd].LseedN.unique()))\n",
    "        \n",
    "        possible_matchups = handshake_problem(all_seeds)\n",
    "        \n",
    "        matchup_results = []\n",
    "        \n",
    "        round_games = df[df.tourney_round == rnd]\n",
    "        \n",
    "        for pair in possible_matchups:\n",
    "            all_matchups = round_games[((round_games.WseedN == pair[0]) & (round_games.LseedN == pair[1])) |\n",
    "                                      ((round_games.WseedN == pair[1]) & (round_games.LseedN == pair[0]))]\n",
    "            if len(all_matchups) != 0:\n",
    "                d = {}\n",
    "                n_games = len(all_matchups)\n",
    "                n_upsets = len(all_matchups[all_matchups.is_upset == 1])\n",
    "                d[\"matchup\"] = \"{}v{}\".format(pair[0], pair[1])\n",
    "                d[\"total_upsets\"] = n_upsets\n",
    "                d[\"total_matchups\"] = n_games\n",
    "                d[\"upset_pct\"] = n_upsets / n_games\n",
    "                d[\"roundNum\"] = rnd\n",
    "                matchup_results.append(d)\n",
    "            else:\n",
    "                continue\n",
    "\n",
    "        round_df = pd.DataFrame(matchup_results).sort_values(by = [\"total_matchups\",\n",
    "                                                                         \"upset_pct\"], ascending = False)\n",
    "        by_round[rnd] = round_df\n",
    "    return(by_round)\n",
    "        \n",
    "all_rounds = upset_analysis_by_round(historical_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_round(df, rnd):\n",
    "    fig, ax1 = plt.subplots(figsize = (10, 10))\n",
    "    pos = np.arange(len(df.matchup))\n",
    "    rects = ax1.barh(pos, df.upset_pct,\n",
    "                     align='center',\n",
    "                     height=0.5,\n",
    "                    tick_label = df.total_matchups)\n",
    "\n",
    "    ax1.set_title(\"Round {} Seeds Analysis\".format(str(rnd)), weight = \"bold\")\n",
    "    ax1.xaxis.set_major_locator(plt.MaxNLocator(11))\n",
    "    ax1.set_xlim([0, 1.0])\n",
    "    ax1.xaxis.grid(True, linestyle='--', which='major',\n",
    "                   color='grey', alpha=.25)\n",
    "    ax1.set_ylabel(\"Total Matchups Since 1985\")\n",
    "    ax1.axvline(0.5, color='grey', alpha=0.75)\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.set_yticks(pos)\n",
    "    ax2.set_ylim(ax1.get_ylim())\n",
    "    ax2.set_yticklabels(df.matchup, weight = \"bold\")\n",
    "    ax2.set_ylabel('Matchup')\n",
    "\n",
    "    r_labels = []\n",
    "    for rect in rects:\n",
    "        width = rect.get_width()\n",
    "        lbl = \"{}%\".format(str(round(width*100, 2)))\n",
    "        if width < 0.85:\n",
    "            xshift = 5\n",
    "            c = \"black\"\n",
    "            align = \"left\"\n",
    "        else:\n",
    "            xshift = -5\n",
    "            c = \"white\"\n",
    "            align = \"right\"\n",
    "\n",
    "        yshift = rect.get_y() + rect.get_height() / 2\n",
    "\n",
    "        label = ax1.annotate(lbl, xy=(width, yshift), xytext=(xshift, 0),\n",
    "                            textcoords=\"offset points\",\n",
    "                            ha=align, va='center',\n",
    "                            color=c, weight='bold', clip_on=True)\n",
    "        r_labels.append(label)\n",
    "    return {'fig': fig,\n",
    "            'ax': ax1,\n",
    "            'ax_right': ax2,\n",
    "            'bars': rects,\n",
    "            'perc_labels': r_labels}\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for rnd in all_rounds:\n",
    "    plot_round(all_rounds[rnd], rnd);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UPSET PREDICTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# APPEND RANKINGS, CREATE TARGET VARIABLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All data used in this notebook was created from the Google Cloud / Kaggle data available via the Kaggle March Madness 2020 Tournament contest. (see: __00DataSetCreation-FeatureEngineering.ipynb__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./engineered_data/tournament_modeling2003-2019.csv.gz\", compression = \"gzip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Season</th>\n",
       "      <th>DayNum</th>\n",
       "      <th>WTeamID</th>\n",
       "      <th>WScore</th>\n",
       "      <th>LTeamID</th>\n",
       "      <th>LScore</th>\n",
       "      <th>WseedN</th>\n",
       "      <th>WDRB_DIF</th>\n",
       "      <th>WORB_DIF</th>\n",
       "      <th>WPPG</th>\n",
       "      <th>WPPP</th>\n",
       "      <th>WTRB_DIF</th>\n",
       "      <th>Wast_TO</th>\n",
       "      <th>We_fg</th>\n",
       "      <th>Wfg</th>\n",
       "      <th>Wfree_throw_pct</th>\n",
       "      <th>Wopp_PPG</th>\n",
       "      <th>Wopp_PPP</th>\n",
       "      <th>Wopp_ast_TO</th>\n",
       "      <th>Wopp_efg</th>\n",
       "      <th>Wopp_fg</th>\n",
       "      <th>Wopp_three_point_pct</th>\n",
       "      <th>Wopp_trueshooting</th>\n",
       "      <th>Wthree_point_pct</th>\n",
       "      <th>Wtotal_point_differential</th>\n",
       "      <th>Wtrue_shooting</th>\n",
       "      <th>Wacc</th>\n",
       "      <th>Wbig_east</th>\n",
       "      <th>Wbig_ten</th>\n",
       "      <th>Wbig_twelve</th>\n",
       "      <th>Wsec</th>\n",
       "      <th>Wpac12</th>\n",
       "      <th>WMOR</th>\n",
       "      <th>WAP_top10</th>\n",
       "      <th>WAP_top25</th>\n",
       "      <th>LseedN</th>\n",
       "      <th>LDRB_DIF</th>\n",
       "      <th>LORB_DIF</th>\n",
       "      <th>LPPG</th>\n",
       "      <th>LPPP</th>\n",
       "      <th>LTRB_DIF</th>\n",
       "      <th>Last_TO</th>\n",
       "      <th>Le_fg</th>\n",
       "      <th>Lfg</th>\n",
       "      <th>Lfree_throw_pct</th>\n",
       "      <th>Lopp_PPG</th>\n",
       "      <th>Lopp_PPP</th>\n",
       "      <th>Lopp_ast_TO</th>\n",
       "      <th>Lopp_efg</th>\n",
       "      <th>Lopp_fg</th>\n",
       "      <th>Lopp_three_point_pct</th>\n",
       "      <th>Lopp_trueshooting</th>\n",
       "      <th>Lthree_point_pct</th>\n",
       "      <th>Ltotal_point_differential</th>\n",
       "      <th>Ltrue_shooting</th>\n",
       "      <th>Lacc</th>\n",
       "      <th>Lbig_east</th>\n",
       "      <th>Lbig_ten</th>\n",
       "      <th>Lbig_twelve</th>\n",
       "      <th>Lsec</th>\n",
       "      <th>Lpac12</th>\n",
       "      <th>LMOR</th>\n",
       "      <th>LAP_top10</th>\n",
       "      <th>LAP_top25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>134</td>\n",
       "      <td>1421</td>\n",
       "      <td>92</td>\n",
       "      <td>1411</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>-42</td>\n",
       "      <td>71.206897</td>\n",
       "      <td>0.866162</td>\n",
       "      <td>-32</td>\n",
       "      <td>0.804255</td>\n",
       "      <td>0.486339</td>\n",
       "      <td>0.429265</td>\n",
       "      <td>0.762768</td>\n",
       "      <td>78.448276</td>\n",
       "      <td>0.937078</td>\n",
       "      <td>1.236559</td>\n",
       "      <td>0.518382</td>\n",
       "      <td>0.455882</td>\n",
       "      <td>0.367110</td>\n",
       "      <td>0.553323</td>\n",
       "      <td>0.360153</td>\n",
       "      <td>-210</td>\n",
       "      <td>0.539424</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>58</td>\n",
       "      <td>37</td>\n",
       "      <td>72.800000</td>\n",
       "      <td>0.878704</td>\n",
       "      <td>95</td>\n",
       "      <td>0.932166</td>\n",
       "      <td>0.501206</td>\n",
       "      <td>0.447527</td>\n",
       "      <td>0.619952</td>\n",
       "      <td>70.833333</td>\n",
       "      <td>0.853660</td>\n",
       "      <td>0.960465</td>\n",
       "      <td>0.487307</td>\n",
       "      <td>0.424945</td>\n",
       "      <td>0.325180</td>\n",
       "      <td>0.515957</td>\n",
       "      <td>0.320721</td>\n",
       "      <td>59</td>\n",
       "      <td>0.538334</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1112</td>\n",
       "      <td>80</td>\n",
       "      <td>1436</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>58</td>\n",
       "      <td>85.214286</td>\n",
       "      <td>0.931304</td>\n",
       "      <td>180</td>\n",
       "      <td>1.193237</td>\n",
       "      <td>0.514946</td>\n",
       "      <td>0.461413</td>\n",
       "      <td>0.701429</td>\n",
       "      <td>70.250000</td>\n",
       "      <td>0.786410</td>\n",
       "      <td>0.917373</td>\n",
       "      <td>0.456378</td>\n",
       "      <td>0.407510</td>\n",
       "      <td>0.316637</td>\n",
       "      <td>0.484664</td>\n",
       "      <td>0.350534</td>\n",
       "      <td>419</td>\n",
       "      <td>0.555400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>112</td>\n",
       "      <td>98</td>\n",
       "      <td>67.793103</td>\n",
       "      <td>0.863235</td>\n",
       "      <td>210</td>\n",
       "      <td>1.009804</td>\n",
       "      <td>0.491667</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.657848</td>\n",
       "      <td>63.137931</td>\n",
       "      <td>0.841808</td>\n",
       "      <td>1.021220</td>\n",
       "      <td>0.477458</td>\n",
       "      <td>0.413275</td>\n",
       "      <td>0.329582</td>\n",
       "      <td>0.509154</td>\n",
       "      <td>0.340757</td>\n",
       "      <td>135</td>\n",
       "      <td>0.525815</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1113</td>\n",
       "      <td>84</td>\n",
       "      <td>1272</td>\n",
       "      <td>71</td>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "      <td>75.965517</td>\n",
       "      <td>0.921603</td>\n",
       "      <td>158</td>\n",
       "      <td>1.110837</td>\n",
       "      <td>0.513333</td>\n",
       "      <td>0.478182</td>\n",
       "      <td>0.669737</td>\n",
       "      <td>69.172414</td>\n",
       "      <td>0.862499</td>\n",
       "      <td>0.906667</td>\n",
       "      <td>0.494424</td>\n",
       "      <td>0.445477</td>\n",
       "      <td>0.342733</td>\n",
       "      <td>0.534705</td>\n",
       "      <td>0.317808</td>\n",
       "      <td>197</td>\n",
       "      <td>0.555080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>69</td>\n",
       "      <td>50</td>\n",
       "      <td>74.517241</td>\n",
       "      <td>0.888511</td>\n",
       "      <td>119</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>0.496264</td>\n",
       "      <td>0.437931</td>\n",
       "      <td>0.653614</td>\n",
       "      <td>65.827586</td>\n",
       "      <td>0.802290</td>\n",
       "      <td>0.883295</td>\n",
       "      <td>0.453218</td>\n",
       "      <td>0.402265</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.491392</td>\n",
       "      <td>0.348797</td>\n",
       "      <td>252</td>\n",
       "      <td>0.531700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1141</td>\n",
       "      <td>79</td>\n",
       "      <td>1166</td>\n",
       "      <td>73</td>\n",
       "      <td>11</td>\n",
       "      <td>127</td>\n",
       "      <td>-48</td>\n",
       "      <td>79.344828</td>\n",
       "      <td>0.967538</td>\n",
       "      <td>79</td>\n",
       "      <td>0.856333</td>\n",
       "      <td>0.570026</td>\n",
       "      <td>0.505236</td>\n",
       "      <td>0.765753</td>\n",
       "      <td>73.241379</td>\n",
       "      <td>0.875414</td>\n",
       "      <td>0.733906</td>\n",
       "      <td>0.497619</td>\n",
       "      <td>0.454762</td>\n",
       "      <td>0.357320</td>\n",
       "      <td>0.541759</td>\n",
       "      <td>0.380769</td>\n",
       "      <td>177</td>\n",
       "      <td>0.622161</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>-6</td>\n",
       "      <td>79.242424</td>\n",
       "      <td>0.995114</td>\n",
       "      <td>54</td>\n",
       "      <td>1.258503</td>\n",
       "      <td>0.568829</td>\n",
       "      <td>0.499473</td>\n",
       "      <td>0.692890</td>\n",
       "      <td>64.333333</td>\n",
       "      <td>0.806783</td>\n",
       "      <td>0.724689</td>\n",
       "      <td>0.475630</td>\n",
       "      <td>0.431544</td>\n",
       "      <td>0.341102</td>\n",
       "      <td>0.513189</td>\n",
       "      <td>0.389053</td>\n",
       "      <td>492</td>\n",
       "      <td>0.597895</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1143</td>\n",
       "      <td>76</td>\n",
       "      <td>1301</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>74.482759</td>\n",
       "      <td>0.914077</td>\n",
       "      <td>42</td>\n",
       "      <td>1.128954</td>\n",
       "      <td>0.520258</td>\n",
       "      <td>0.465649</td>\n",
       "      <td>0.685512</td>\n",
       "      <td>69.758621</td>\n",
       "      <td>0.856971</td>\n",
       "      <td>1.073903</td>\n",
       "      <td>0.492082</td>\n",
       "      <td>0.431672</td>\n",
       "      <td>0.331190</td>\n",
       "      <td>0.524735</td>\n",
       "      <td>0.376518</td>\n",
       "      <td>137</td>\n",
       "      <td>0.553267</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>-24</td>\n",
       "      <td>72.400000</td>\n",
       "      <td>0.945927</td>\n",
       "      <td>-6</td>\n",
       "      <td>1.032864</td>\n",
       "      <td>0.530937</td>\n",
       "      <td>0.456250</td>\n",
       "      <td>0.770358</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.882032</td>\n",
       "      <td>0.858770</td>\n",
       "      <td>0.494981</td>\n",
       "      <td>0.441029</td>\n",
       "      <td>0.337255</td>\n",
       "      <td>0.544337</td>\n",
       "      <td>0.354074</td>\n",
       "      <td>132</td>\n",
       "      <td>0.580699</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season  DayNum  WTeamID  WScore  LTeamID  LScore  WseedN  WDRB_DIF  \\\n",
       "0    2003     134     1421      92     1411      84      16        10   \n",
       "1    2003     136     1112      80     1436      51       1       122   \n",
       "2    2003     136     1113      84     1272      71      10        80   \n",
       "3    2003     136     1141      79     1166      73      11       127   \n",
       "4    2003     136     1143      76     1301      74       8        40   \n",
       "\n",
       "   WORB_DIF       WPPG      WPPP  WTRB_DIF   Wast_TO     We_fg       Wfg  \\\n",
       "0       -42  71.206897  0.866162       -32  0.804255  0.486339  0.429265   \n",
       "1        58  85.214286  0.931304       180  1.193237  0.514946  0.461413   \n",
       "2        78  75.965517  0.921603       158  1.110837  0.513333  0.478182   \n",
       "3       -48  79.344828  0.967538        79  0.856333  0.570026  0.505236   \n",
       "4         2  74.482759  0.914077        42  1.128954  0.520258  0.465649   \n",
       "\n",
       "   Wfree_throw_pct   Wopp_PPG  Wopp_PPP  Wopp_ast_TO  Wopp_efg   Wopp_fg  \\\n",
       "0         0.762768  78.448276  0.937078     1.236559  0.518382  0.455882   \n",
       "1         0.701429  70.250000  0.786410     0.917373  0.456378  0.407510   \n",
       "2         0.669737  69.172414  0.862499     0.906667  0.494424  0.445477   \n",
       "3         0.765753  73.241379  0.875414     0.733906  0.497619  0.454762   \n",
       "4         0.685512  69.758621  0.856971     1.073903  0.492082  0.431672   \n",
       "\n",
       "   Wopp_three_point_pct  Wopp_trueshooting  Wthree_point_pct  \\\n",
       "0              0.367110           0.553323          0.360153   \n",
       "1              0.316637           0.484664          0.350534   \n",
       "2              0.342733           0.534705          0.317808   \n",
       "3              0.357320           0.541759          0.380769   \n",
       "4              0.331190           0.524735          0.376518   \n",
       "\n",
       "   Wtotal_point_differential  Wtrue_shooting  Wacc  Wbig_east  Wbig_ten  \\\n",
       "0                       -210        0.539424     0          0         0   \n",
       "1                        419        0.555400     0          0         0   \n",
       "2                        197        0.555080     0          0         0   \n",
       "3                        177        0.622161     0          0         0   \n",
       "4                        137        0.553267     0          0         0   \n",
       "\n",
       "   Wbig_twelve  Wsec  Wpac12   WMOR  WAP_top10  WAP_top25  LseedN  LDRB_DIF  \\\n",
       "0            0     0       0  277.0          0          0      16        58   \n",
       "1            0     0       1    4.0          1          0      16       112   \n",
       "2            0     0       1   22.0          0          0       7        69   \n",
       "3            0     0       0   50.0          0          0       6        60   \n",
       "4            0     0       1   43.0          0          0       9        18   \n",
       "\n",
       "   LORB_DIF       LPPG      LPPP  LTRB_DIF   Last_TO     Le_fg       Lfg  \\\n",
       "0        37  72.800000  0.878704        95  0.932166  0.501206  0.447527   \n",
       "1        98  67.793103  0.863235       210  1.009804  0.491667  0.444444   \n",
       "2        50  74.517241  0.888511       119  1.205000  0.496264  0.437931   \n",
       "3        -6  79.242424  0.995114        54  1.258503  0.568829  0.499473   \n",
       "4       -24  72.400000  0.945927        -6  1.032864  0.530937  0.456250   \n",
       "\n",
       "   Lfree_throw_pct   Lopp_PPG  Lopp_PPP  Lopp_ast_TO  Lopp_efg   Lopp_fg  \\\n",
       "0         0.619952  70.833333  0.853660     0.960465  0.487307  0.424945   \n",
       "1         0.657848  63.137931  0.841808     1.021220  0.477458  0.413275   \n",
       "2         0.653614  65.827586  0.802290     0.883295  0.453218  0.402265   \n",
       "3         0.692890  64.333333  0.806783     0.724689  0.475630  0.431544   \n",
       "4         0.770358  68.000000  0.882032     0.858770  0.494981  0.441029   \n",
       "\n",
       "   Lopp_three_point_pct  Lopp_trueshooting  Lthree_point_pct  \\\n",
       "0              0.325180           0.515957          0.320721   \n",
       "1              0.329582           0.509154          0.340757   \n",
       "2              0.322034           0.491392          0.348797   \n",
       "3              0.341102           0.513189          0.389053   \n",
       "4              0.337255           0.544337          0.354074   \n",
       "\n",
       "   Ltotal_point_differential  Ltrue_shooting  Lacc  Lbig_east  Lbig_ten  \\\n",
       "0                         59        0.538334     0          0         0   \n",
       "1                        135        0.525815     0          0         0   \n",
       "2                        252        0.531700     0          0         0   \n",
       "3                        492        0.597895     0          0         0   \n",
       "4                        132        0.580699     1          0         0   \n",
       "\n",
       "   Lbig_twelve  Lsec  Lpac12   LMOR  LAP_top10  LAP_top25  \n",
       "0            0     0       0  293.0          0          0  \n",
       "1            0     0       0  187.0          0          0  \n",
       "2            0     0       0   23.0          0          1  \n",
       "3            0     0       0   33.0          0          1  \n",
       "4            0     0       0   31.0          0          0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"max.columns\", 150)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_rankings = pd.read_csv(\"./engineered_data/finalrankings.csv.gz\", compression = \"gzip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003</td>\n",
       "      <td>1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>307.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>313.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>305.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>310.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>314.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>313.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>307.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>297.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>310.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>307.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>313.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>314.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>289.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>310.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003</td>\n",
       "      <td>1105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>252.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>269.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>266.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>245.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>279.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>245.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>272.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>248.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>274.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>264.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>276.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>294.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>279.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>254.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003</td>\n",
       "      <td>1106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 166 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   7OT  ACU  ADE  AP    ARG  AUS  AWS  BBT  BCM  BD    BIH  BKM  BLS  BNM  \\\n",
       "0  NaN  NaN  NaN NaN  141.0  NaN  NaN  NaN  NaN NaN  172.0  NaN  NaN  NaN   \n",
       "1  NaN  NaN  NaN NaN  180.0  NaN  NaN  NaN  NaN NaN  177.0  NaN  NaN  NaN   \n",
       "2  NaN  NaN  NaN NaN   37.0  NaN  NaN  NaN  NaN NaN   40.0  NaN  NaN  NaN   \n",
       "3  NaN  NaN  NaN NaN  307.0  NaN  NaN  NaN  NaN NaN  312.0  NaN  NaN  NaN   \n",
       "4  NaN  NaN  NaN NaN  252.0  NaN  NaN  NaN  NaN NaN  269.0  NaN  NaN  NaN   \n",
       "\n",
       "   BNT    BOB  BPI    BRZ  BUR  BWE  CJB  CMV  CNG    COL  COX  CPA  CPR  CRO  \\\n",
       "0  NaN  146.0  NaN  161.0  NaN  NaN  NaN  NaN  NaN  162.0  NaN  NaN  NaN  NaN   \n",
       "1  NaN  179.0  NaN  174.0  NaN  NaN  NaN  NaN  NaN  172.0  NaN  NaN  NaN  NaN   \n",
       "2  NaN   35.0  NaN   34.0  NaN  NaN  NaN  NaN  NaN   43.0  NaN  NaN  NaN  NaN   \n",
       "3  NaN  313.0  NaN  305.0  NaN  NaN  NaN  NaN  NaN  310.0  NaN  NaN  NaN  NaN   \n",
       "4  NaN  265.0  NaN  219.0  NaN  NaN  NaN  NaN  NaN  256.0  NaN  NaN  NaN  NaN   \n",
       "\n",
       "   CRW  D1A  DAV  DC  DC2  DCI  DDB  DES  DII  DOK    DOL    DUN    DWH  EBP  \\\n",
       "0  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN  NaN  NaN  175.0   97.0  149.0  NaN   \n",
       "1  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN  NaN  NaN  174.0  165.0  168.0  NaN   \n",
       "2  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN  NaN  NaN   39.0   43.0   33.0  NaN   \n",
       "3  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN  NaN  NaN  315.0  306.0  315.0  NaN   \n",
       "4  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN  NaN  NaN  266.0  305.0  269.0  NaN   \n",
       "\n",
       "     ECK    ENT    ERD  ESR  FAS  FMG  FSH    GRN    GRS  HAS  HAT    HER  \\\n",
       "0  156.0  155.0  147.0  NaN  NaN  NaN  NaN  161.0  166.0  NaN  NaN  148.0   \n",
       "1  194.0  176.0  128.0  NaN  NaN  NaN  NaN  168.0  172.0  NaN  NaN  176.0   \n",
       "2   51.0   34.0   36.0  NaN  NaN  NaN  NaN   40.0   50.0  NaN  NaN   37.0   \n",
       "3  314.0  309.0  309.0  NaN  NaN  NaN  NaN  313.0  298.0  NaN  NaN  307.0   \n",
       "4  245.0  259.0  283.0  NaN  NaN  NaN  NaN  279.0  227.0  NaN  NaN  245.0   \n",
       "\n",
       "   HKB  HKS    HOL  HRN    IMS  INC  INP  ISR  JCI  JEN  JJK  JNG  JON  JRT  \\\n",
       "0  NaN  NaN  185.0  NaN  198.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1  NaN  NaN  167.0  NaN  152.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2  NaN  NaN   60.0  NaN   47.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3  NaN  NaN  297.0  NaN  310.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4  NaN  NaN  198.0  NaN  272.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "   KBM  KEL  KLK  KMV  KOS  KPI  KPK  ...  MSX  MUZ  MvG  NET  NOL  NOR  OCT  \\\n",
       "0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "   OMY  PEQ  PGH  PH  PIG  PIR  PKL  PMC    POM  PPR  PRR  PTS  REI  REN  REW  \\\n",
       "0  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN  160.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN  163.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "2  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN   33.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN  307.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN  263.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "   RIS  RM  ROG  ROH    RPI  RSE  RSL  RT  RTB    RTH  RTP  RTR    SAG  SAP  \\\n",
       "0  NaN NaN  NaN  NaN  158.0  NaN  NaN NaN  NaN  146.0  NaN  NaN  149.0  NaN   \n",
       "1  NaN NaN  NaN  NaN  182.0  NaN  NaN NaN  NaN  168.0  NaN  NaN  172.0  NaN   \n",
       "2  NaN NaN  NaN  NaN   38.0  NaN  NaN NaN  NaN   31.0  NaN  NaN   37.0  NaN   \n",
       "3  NaN NaN  NaN  NaN  313.0  NaN  NaN NaN  NaN  312.0  NaN  NaN  312.0  NaN   \n",
       "4  NaN NaN  NaN  NaN  248.0  NaN  NaN NaN  NaN  274.0  NaN  NaN  268.0  NaN   \n",
       "\n",
       "     SAU  SCR     SE    SEL  SFX  SGR  SIM  SMN  SMS  SP  SPR  SPW  STF  STH  \\\n",
       "0  154.0  NaN  162.0  155.0  NaN  NaN  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN   \n",
       "1  170.0  NaN  167.0  169.0  NaN  NaN  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN   \n",
       "2   32.0  NaN   41.0   38.0  NaN  NaN  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN   \n",
       "3  312.0  NaN  314.0  312.0  NaN  NaN  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN   \n",
       "4  264.0  NaN  276.0  267.0  NaN  NaN  NaN  NaN  NaN NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "   STM    STR  STS  TBD  TMR  TPR  TRK  TRP  TRX    TSR  TW  UCS  UPS  USA  \\\n",
       "0  NaN  168.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  146.0 NaN  NaN  NaN  NaN   \n",
       "1  NaN  147.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  172.0 NaN  NaN  NaN  NaN   \n",
       "2  NaN   33.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN   35.0 NaN  NaN  NaN  NaN   \n",
       "3  NaN  289.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  315.0 NaN  NaN  NaN  NaN   \n",
       "4  NaN  294.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  279.0 NaN  NaN  NaN  NaN   \n",
       "\n",
       "   WIL    WLK  WMR  WMV    WOB    WOL    WTE  YAG  ZAM  season  teamID  \n",
       "0  NaN  165.0  NaN  NaN  155.0  157.0  156.0  NaN  NaN    2003    1102  \n",
       "1  NaN  172.0  NaN  NaN  177.0  171.0  161.0  NaN  NaN    2003    1103  \n",
       "2  NaN   36.0  NaN  NaN   37.0   37.0   28.0  NaN  NaN    2003    1104  \n",
       "3  NaN  310.0  NaN  NaN  312.0  311.0  299.0  NaN  NaN    2003    1105  \n",
       "4  NaN  254.0  NaN  NaN  265.0  270.0  256.0  NaN  NaN    2003    1106  \n",
       "\n",
       "[5 rows x 166 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_rankings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./models/regular_season_ranking_features.json\", \"r+\") as f:\n",
    "    rating_cols = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = [\"COL\", \"DOL\", \"MOR\", \"POM\", \"RTH\", \"SAG\", \"WLK\", \"WOL\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "append_ratings = []\n",
    "for k in range(len(df)):\n",
    "    d = {}\n",
    "    winner = df.iloc[k].WTeamID\n",
    "    loser = df.iloc[k].LTeamID\n",
    "    season = df.iloc[k].Season\n",
    "    d[\"WTeamID\"] = winner\n",
    "    d[\"LTeamID\"] = loser\n",
    "    d[\"Season\"] = season\n",
    "    for col in ratings:\n",
    "        d[\"W\"+col] = final_rankings[(final_rankings.teamID == winner) &\n",
    "                           (final_rankings.season == season)][col].values[0]\n",
    "        d[\"L\"+col] = final_rankings[(final_rankings.teamID == loser) &\n",
    "                                   (final_rankings.season == season)][col].values[0]\n",
    "    append_ratings.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "eos_ratings = pd.DataFrame(append_ratings)\n",
    "df = df.merge(eos_ratings, how = \"left\", left_on = [\"WTeamID\", \"LTeamID\", \"Season\"],\n",
    "             right_on = [\"WTeamID\", \"LTeamID\", \"Season\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns = [\"WMOR_y\", \"LMOR_y\"]).rename(columns = {\"WMOR_x\" : \"WMOR\",\n",
    "                                                              \"LMOR_x\" : \"LMOR\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create upset variable\n",
    "upset = []\n",
    "for k in range(len(df)):\n",
    "    if df.iloc[k][\"WseedN\"] > df.iloc[k][\"LseedN\"]:\n",
    "        upset.append(1)\n",
    "    else:\n",
    "        upset.append(0)\n",
    "upset = pd.Series(upset).rename(\"is_upset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    812\n",
       "1    303\n",
       "Name: is_upset, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upset.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, upset], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ADD ROUND NUMBER (I.E.- ROUND OF 64, ROUND OF 32, ROUND OF 16, ETC.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tournament round dates for season 2003 are:\n",
      "[134 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2004 are:\n",
      "[134 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2005 are:\n",
      "[134 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2006 are:\n",
      "[134 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2007 are:\n",
      "[134 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2008 are:\n",
      "[134 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2009 are:\n",
      "[134 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2010 are:\n",
      "[134 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2011 are:\n",
      "[134 135 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2012 are:\n",
      "[134 135 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2013 are:\n",
      "[134 135 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2014 are:\n",
      "[134 135 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2015 are:\n",
      "[134 135 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2016 are:\n",
      "[134 135 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2017 are:\n",
      "[134 135 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2018 are:\n",
      "[134 135 136 137 138 139 143 144 145 146 152 154]\n",
      "The tournament round dates for season 2019 are:\n",
      "[134 135 136 137 138 139 143 144 145 146 152 154]\n"
     ]
    }
   ],
   "source": [
    "for season in df.Season.unique():\n",
    "    print(\"The tournament round dates for season {} are:\".format(str(season)))\n",
    "    print(df[df.Season == season].DayNum.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA EXPLORATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "seed_pairings = []\n",
    "all_upsets = df[df.is_upset == 1]\n",
    "for k in all_upsets.index:\n",
    "    seed_pairings.append((df.iloc[k][\"WseedN\"], df.iloc[k][\"LseedN\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ten most common upsets:\n",
      "\n",
      "((9.0, 8.0), 33)\n",
      "((11.0, 6.0), 29)\n",
      "((12.0, 5.0), 27)\n",
      "((10.0, 7.0), 26)\n",
      "((5.0, 4.0), 18)\n",
      "((2.0, 1.0), 16)\n",
      "((7.0, 2.0), 15)\n",
      "((13.0, 4.0), 14)\n",
      "((3.0, 2.0), 14)\n",
      "((11.0, 3.0), 10)\n"
     ]
    }
   ],
   "source": [
    "upset_count = Counter(seed_pairings)\n",
    "print(\"The ten most common upsets:\")\n",
    "print()\n",
    "for i in range(10):\n",
    "    print(upset_count.most_common()[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly, the 8/9 first round matchup is the most common upset. It also would appear that there are more upsets in the first round because the top 4 all are first round matchups, however: there more games played in the first round so next we will look at upset percentage per round. From this brief glimpse: the two matchups standing out are the 11/3 and the 7/2... quick divergence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The two seed has beaten the seven seed 25 times since 2003\n",
      "The three seed has beaten the eleven seed 19 times since 2003\n"
     ]
    }
   ],
   "source": [
    "two_seven_wins = len(df[(df.WseedN == 2) & (df.LseedN == 7)])\n",
    "three_eleven_wins = len(df[(df.WseedN == 3) & (df.LseedN == 11)])\n",
    "print(\"The two seed has beaten the seven seed {} times since 2003\".format(str(two_seven_wins)))\n",
    "print(\"The three seed has beaten the eleven seed {} times since 2003\".format(str(three_eleven_wins)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right off the bat, this is an interesting note."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: I will be excluding the round zero play-in games for two reasons:\n",
    "1. Nobody cares.\n",
    "2. They typically have the same seed so it's not technically an upset or not an upset. Obviously when St. Bonaventure beats UCLA, we would consider that an upset but for the purpose of this analysis we are looking solely at tournament upsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation = df[df.Season > 2014].reset_index(drop = True)\n",
    "training = df[df.Season <= 2014].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATAPREP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Season</th>\n",
       "      <th>DayNum</th>\n",
       "      <th>WTeamID</th>\n",
       "      <th>WScore</th>\n",
       "      <th>LTeamID</th>\n",
       "      <th>LScore</th>\n",
       "      <th>WseedN</th>\n",
       "      <th>WDRB_DIF</th>\n",
       "      <th>WORB_DIF</th>\n",
       "      <th>WPPG</th>\n",
       "      <th>WPPP</th>\n",
       "      <th>WTRB_DIF</th>\n",
       "      <th>Wast_TO</th>\n",
       "      <th>We_fg</th>\n",
       "      <th>Wfg</th>\n",
       "      <th>Wfree_throw_pct</th>\n",
       "      <th>Wopp_PPG</th>\n",
       "      <th>Wopp_PPP</th>\n",
       "      <th>Wopp_ast_TO</th>\n",
       "      <th>Wopp_efg</th>\n",
       "      <th>Wopp_fg</th>\n",
       "      <th>Wopp_three_point_pct</th>\n",
       "      <th>Wopp_trueshooting</th>\n",
       "      <th>Wthree_point_pct</th>\n",
       "      <th>Wtotal_point_differential</th>\n",
       "      <th>Wtrue_shooting</th>\n",
       "      <th>Wacc</th>\n",
       "      <th>Wbig_east</th>\n",
       "      <th>Wbig_ten</th>\n",
       "      <th>Wbig_twelve</th>\n",
       "      <th>Wsec</th>\n",
       "      <th>Wpac12</th>\n",
       "      <th>WMOR</th>\n",
       "      <th>WAP_top10</th>\n",
       "      <th>WAP_top25</th>\n",
       "      <th>LseedN</th>\n",
       "      <th>LDRB_DIF</th>\n",
       "      <th>LORB_DIF</th>\n",
       "      <th>LPPG</th>\n",
       "      <th>LPPP</th>\n",
       "      <th>LTRB_DIF</th>\n",
       "      <th>Last_TO</th>\n",
       "      <th>Le_fg</th>\n",
       "      <th>Lfg</th>\n",
       "      <th>Lfree_throw_pct</th>\n",
       "      <th>Lopp_PPG</th>\n",
       "      <th>Lopp_PPP</th>\n",
       "      <th>Lopp_ast_TO</th>\n",
       "      <th>Lopp_efg</th>\n",
       "      <th>Lopp_fg</th>\n",
       "      <th>Lopp_three_point_pct</th>\n",
       "      <th>Lopp_trueshooting</th>\n",
       "      <th>Lthree_point_pct</th>\n",
       "      <th>Ltotal_point_differential</th>\n",
       "      <th>Ltrue_shooting</th>\n",
       "      <th>Lacc</th>\n",
       "      <th>Lbig_east</th>\n",
       "      <th>Lbig_ten</th>\n",
       "      <th>Lbig_twelve</th>\n",
       "      <th>Lsec</th>\n",
       "      <th>Lpac12</th>\n",
       "      <th>LMOR</th>\n",
       "      <th>LAP_top10</th>\n",
       "      <th>LAP_top25</th>\n",
       "      <th>WCOL</th>\n",
       "      <th>LCOL</th>\n",
       "      <th>WDOL</th>\n",
       "      <th>LDOL</th>\n",
       "      <th>WPOM</th>\n",
       "      <th>LPOM</th>\n",
       "      <th>WRTH</th>\n",
       "      <th>LRTH</th>\n",
       "      <th>WSAG</th>\n",
       "      <th>LSAG</th>\n",
       "      <th>WWLK</th>\n",
       "      <th>LWLK</th>\n",
       "      <th>WWOL</th>\n",
       "      <th>LWOL</th>\n",
       "      <th>is_upset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>134</td>\n",
       "      <td>1421</td>\n",
       "      <td>92</td>\n",
       "      <td>1411</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>-42</td>\n",
       "      <td>71.206897</td>\n",
       "      <td>0.866162</td>\n",
       "      <td>-32</td>\n",
       "      <td>0.804255</td>\n",
       "      <td>0.486339</td>\n",
       "      <td>0.429265</td>\n",
       "      <td>0.762768</td>\n",
       "      <td>78.448276</td>\n",
       "      <td>0.937078</td>\n",
       "      <td>1.236559</td>\n",
       "      <td>0.518382</td>\n",
       "      <td>0.455882</td>\n",
       "      <td>0.367110</td>\n",
       "      <td>0.553323</td>\n",
       "      <td>0.360153</td>\n",
       "      <td>-210</td>\n",
       "      <td>0.539424</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>58</td>\n",
       "      <td>37</td>\n",
       "      <td>72.800000</td>\n",
       "      <td>0.878704</td>\n",
       "      <td>95</td>\n",
       "      <td>0.932166</td>\n",
       "      <td>0.501206</td>\n",
       "      <td>0.447527</td>\n",
       "      <td>0.619952</td>\n",
       "      <td>70.833333</td>\n",
       "      <td>0.853660</td>\n",
       "      <td>0.960465</td>\n",
       "      <td>0.487307</td>\n",
       "      <td>0.424945</td>\n",
       "      <td>0.325180</td>\n",
       "      <td>0.515957</td>\n",
       "      <td>0.320721</td>\n",
       "      <td>59</td>\n",
       "      <td>0.538334</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1112</td>\n",
       "      <td>80</td>\n",
       "      <td>1436</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>58</td>\n",
       "      <td>85.214286</td>\n",
       "      <td>0.931304</td>\n",
       "      <td>180</td>\n",
       "      <td>1.193237</td>\n",
       "      <td>0.514946</td>\n",
       "      <td>0.461413</td>\n",
       "      <td>0.701429</td>\n",
       "      <td>70.250000</td>\n",
       "      <td>0.786410</td>\n",
       "      <td>0.917373</td>\n",
       "      <td>0.456378</td>\n",
       "      <td>0.407510</td>\n",
       "      <td>0.316637</td>\n",
       "      <td>0.484664</td>\n",
       "      <td>0.350534</td>\n",
       "      <td>419</td>\n",
       "      <td>0.555400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>112</td>\n",
       "      <td>98</td>\n",
       "      <td>67.793103</td>\n",
       "      <td>0.863235</td>\n",
       "      <td>210</td>\n",
       "      <td>1.009804</td>\n",
       "      <td>0.491667</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.657848</td>\n",
       "      <td>63.137931</td>\n",
       "      <td>0.841808</td>\n",
       "      <td>1.021220</td>\n",
       "      <td>0.477458</td>\n",
       "      <td>0.413275</td>\n",
       "      <td>0.329582</td>\n",
       "      <td>0.509154</td>\n",
       "      <td>0.340757</td>\n",
       "      <td>135</td>\n",
       "      <td>0.525815</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1113</td>\n",
       "      <td>84</td>\n",
       "      <td>1272</td>\n",
       "      <td>71</td>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "      <td>78</td>\n",
       "      <td>75.965517</td>\n",
       "      <td>0.921603</td>\n",
       "      <td>158</td>\n",
       "      <td>1.110837</td>\n",
       "      <td>0.513333</td>\n",
       "      <td>0.478182</td>\n",
       "      <td>0.669737</td>\n",
       "      <td>69.172414</td>\n",
       "      <td>0.862499</td>\n",
       "      <td>0.906667</td>\n",
       "      <td>0.494424</td>\n",
       "      <td>0.445477</td>\n",
       "      <td>0.342733</td>\n",
       "      <td>0.534705</td>\n",
       "      <td>0.317808</td>\n",
       "      <td>197</td>\n",
       "      <td>0.555080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>69</td>\n",
       "      <td>50</td>\n",
       "      <td>74.517241</td>\n",
       "      <td>0.888511</td>\n",
       "      <td>119</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>0.496264</td>\n",
       "      <td>0.437931</td>\n",
       "      <td>0.653614</td>\n",
       "      <td>65.827586</td>\n",
       "      <td>0.802290</td>\n",
       "      <td>0.883295</td>\n",
       "      <td>0.453218</td>\n",
       "      <td>0.402265</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.491392</td>\n",
       "      <td>0.348797</td>\n",
       "      <td>252</td>\n",
       "      <td>0.531700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1141</td>\n",
       "      <td>79</td>\n",
       "      <td>1166</td>\n",
       "      <td>73</td>\n",
       "      <td>11</td>\n",
       "      <td>127</td>\n",
       "      <td>-48</td>\n",
       "      <td>79.344828</td>\n",
       "      <td>0.967538</td>\n",
       "      <td>79</td>\n",
       "      <td>0.856333</td>\n",
       "      <td>0.570026</td>\n",
       "      <td>0.505236</td>\n",
       "      <td>0.765753</td>\n",
       "      <td>73.241379</td>\n",
       "      <td>0.875414</td>\n",
       "      <td>0.733906</td>\n",
       "      <td>0.497619</td>\n",
       "      <td>0.454762</td>\n",
       "      <td>0.357320</td>\n",
       "      <td>0.541759</td>\n",
       "      <td>0.380769</td>\n",
       "      <td>177</td>\n",
       "      <td>0.622161</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>-6</td>\n",
       "      <td>79.242424</td>\n",
       "      <td>0.995114</td>\n",
       "      <td>54</td>\n",
       "      <td>1.258503</td>\n",
       "      <td>0.568829</td>\n",
       "      <td>0.499473</td>\n",
       "      <td>0.692890</td>\n",
       "      <td>64.333333</td>\n",
       "      <td>0.806783</td>\n",
       "      <td>0.724689</td>\n",
       "      <td>0.475630</td>\n",
       "      <td>0.431544</td>\n",
       "      <td>0.341102</td>\n",
       "      <td>0.513189</td>\n",
       "      <td>0.389053</td>\n",
       "      <td>492</td>\n",
       "      <td>0.597895</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003</td>\n",
       "      <td>136</td>\n",
       "      <td>1143</td>\n",
       "      <td>76</td>\n",
       "      <td>1301</td>\n",
       "      <td>74</td>\n",
       "      <td>8</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>74.482759</td>\n",
       "      <td>0.914077</td>\n",
       "      <td>42</td>\n",
       "      <td>1.128954</td>\n",
       "      <td>0.520258</td>\n",
       "      <td>0.465649</td>\n",
       "      <td>0.685512</td>\n",
       "      <td>69.758621</td>\n",
       "      <td>0.856971</td>\n",
       "      <td>1.073903</td>\n",
       "      <td>0.492082</td>\n",
       "      <td>0.431672</td>\n",
       "      <td>0.331190</td>\n",
       "      <td>0.524735</td>\n",
       "      <td>0.376518</td>\n",
       "      <td>137</td>\n",
       "      <td>0.553267</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>-24</td>\n",
       "      <td>72.400000</td>\n",
       "      <td>0.945927</td>\n",
       "      <td>-6</td>\n",
       "      <td>1.032864</td>\n",
       "      <td>0.530937</td>\n",
       "      <td>0.456250</td>\n",
       "      <td>0.770358</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.882032</td>\n",
       "      <td>0.858770</td>\n",
       "      <td>0.494981</td>\n",
       "      <td>0.441029</td>\n",
       "      <td>0.337255</td>\n",
       "      <td>0.544337</td>\n",
       "      <td>0.354074</td>\n",
       "      <td>132</td>\n",
       "      <td>0.580699</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season  DayNum  WTeamID  WScore  LTeamID  LScore  WseedN  WDRB_DIF  \\\n",
       "0    2003     134     1421      92     1411      84      16        10   \n",
       "1    2003     136     1112      80     1436      51       1       122   \n",
       "2    2003     136     1113      84     1272      71      10        80   \n",
       "3    2003     136     1141      79     1166      73      11       127   \n",
       "4    2003     136     1143      76     1301      74       8        40   \n",
       "\n",
       "   WORB_DIF       WPPG      WPPP  WTRB_DIF   Wast_TO     We_fg       Wfg  \\\n",
       "0       -42  71.206897  0.866162       -32  0.804255  0.486339  0.429265   \n",
       "1        58  85.214286  0.931304       180  1.193237  0.514946  0.461413   \n",
       "2        78  75.965517  0.921603       158  1.110837  0.513333  0.478182   \n",
       "3       -48  79.344828  0.967538        79  0.856333  0.570026  0.505236   \n",
       "4         2  74.482759  0.914077        42  1.128954  0.520258  0.465649   \n",
       "\n",
       "   Wfree_throw_pct   Wopp_PPG  Wopp_PPP  Wopp_ast_TO  Wopp_efg   Wopp_fg  \\\n",
       "0         0.762768  78.448276  0.937078     1.236559  0.518382  0.455882   \n",
       "1         0.701429  70.250000  0.786410     0.917373  0.456378  0.407510   \n",
       "2         0.669737  69.172414  0.862499     0.906667  0.494424  0.445477   \n",
       "3         0.765753  73.241379  0.875414     0.733906  0.497619  0.454762   \n",
       "4         0.685512  69.758621  0.856971     1.073903  0.492082  0.431672   \n",
       "\n",
       "   Wopp_three_point_pct  Wopp_trueshooting  Wthree_point_pct  \\\n",
       "0              0.367110           0.553323          0.360153   \n",
       "1              0.316637           0.484664          0.350534   \n",
       "2              0.342733           0.534705          0.317808   \n",
       "3              0.357320           0.541759          0.380769   \n",
       "4              0.331190           0.524735          0.376518   \n",
       "\n",
       "   Wtotal_point_differential  Wtrue_shooting  Wacc  Wbig_east  Wbig_ten  \\\n",
       "0                       -210        0.539424     0          0         0   \n",
       "1                        419        0.555400     0          0         0   \n",
       "2                        197        0.555080     0          0         0   \n",
       "3                        177        0.622161     0          0         0   \n",
       "4                        137        0.553267     0          0         0   \n",
       "\n",
       "   Wbig_twelve  Wsec  Wpac12   WMOR  WAP_top10  WAP_top25  LseedN  LDRB_DIF  \\\n",
       "0            0     0       0  277.0          0          0      16        58   \n",
       "1            0     0       1    4.0          1          0      16       112   \n",
       "2            0     0       1   22.0          0          0       7        69   \n",
       "3            0     0       0   50.0          0          0       6        60   \n",
       "4            0     0       1   43.0          0          0       9        18   \n",
       "\n",
       "   LORB_DIF       LPPG      LPPP  LTRB_DIF   Last_TO     Le_fg       Lfg  \\\n",
       "0        37  72.800000  0.878704        95  0.932166  0.501206  0.447527   \n",
       "1        98  67.793103  0.863235       210  1.009804  0.491667  0.444444   \n",
       "2        50  74.517241  0.888511       119  1.205000  0.496264  0.437931   \n",
       "3        -6  79.242424  0.995114        54  1.258503  0.568829  0.499473   \n",
       "4       -24  72.400000  0.945927        -6  1.032864  0.530937  0.456250   \n",
       "\n",
       "   Lfree_throw_pct   Lopp_PPG  Lopp_PPP  Lopp_ast_TO  Lopp_efg   Lopp_fg  \\\n",
       "0         0.619952  70.833333  0.853660     0.960465  0.487307  0.424945   \n",
       "1         0.657848  63.137931  0.841808     1.021220  0.477458  0.413275   \n",
       "2         0.653614  65.827586  0.802290     0.883295  0.453218  0.402265   \n",
       "3         0.692890  64.333333  0.806783     0.724689  0.475630  0.431544   \n",
       "4         0.770358  68.000000  0.882032     0.858770  0.494981  0.441029   \n",
       "\n",
       "   Lopp_three_point_pct  Lopp_trueshooting  Lthree_point_pct  \\\n",
       "0              0.325180           0.515957          0.320721   \n",
       "1              0.329582           0.509154          0.340757   \n",
       "2              0.322034           0.491392          0.348797   \n",
       "3              0.341102           0.513189          0.389053   \n",
       "4              0.337255           0.544337          0.354074   \n",
       "\n",
       "   Ltotal_point_differential  Ltrue_shooting  Lacc  Lbig_east  Lbig_ten  \\\n",
       "0                         59        0.538334     0          0         0   \n",
       "1                        135        0.525815     0          0         0   \n",
       "2                        252        0.531700     0          0         0   \n",
       "3                        492        0.597895     0          0         0   \n",
       "4                        132        0.580699     1          0         0   \n",
       "\n",
       "   Lbig_twelve  Lsec  Lpac12   LMOR  LAP_top10  LAP_top25   WCOL   LCOL  \\\n",
       "0            0     0       0  293.0          0          0  213.0  212.0   \n",
       "1            0     0       0  187.0          0          0    2.0  144.0   \n",
       "2            0     0       0   23.0          0          1   38.0   19.0   \n",
       "3            0     0       0   33.0          0          1   35.0   18.0   \n",
       "4            0     0       0   31.0          0          0   28.0   56.0   \n",
       "\n",
       "    WDOL   LDOL   WPOM   LPOM   WRTH   LRTH   WSAG   LSAG   WWLK   LWLK  \\\n",
       "0  258.0  239.0  273.0  268.0  250.0  256.0  251.0  257.0  237.0  240.0   \n",
       "1    2.0  156.0    3.0  144.0    2.0  182.0    2.0  158.0    2.0  153.0   \n",
       "2   48.0   21.0   32.0   20.0   35.0   19.0   32.0   24.0   34.0   21.0   \n",
       "3   37.0   18.0   50.0   27.0   55.0   22.0   48.0   18.0   32.0   19.0   \n",
       "4   25.0   49.0   40.0   48.0   30.0   42.0   40.0   46.0   33.0   51.0   \n",
       "\n",
       "    WWOL   LWOL  is_upset  \n",
       "0  220.0  246.0         0  \n",
       "1    2.0  177.0         0  \n",
       "2   39.0   19.0         1  \n",
       "3   49.0   17.0         1  \n",
       "4   26.0   47.0         0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain = training[\"is_upset\"]\n",
    "ytest = validation[\"is_upset\"]\n",
    "\n",
    "drop = [\"Season\", \"DayNum\", \"WTeamID\", \"WScore\", \"LTeamID\", \"LScore\", \"WseedN\", \"LseedN\", \"is_upset\"]\n",
    "xtrain = training\n",
    "\n",
    "drop = [\"Season\", \"DayNum\", \"WTeamID\", \"WScore\", \"LTeamID\", \"LScore\", \"WseedN\", \"LseedN\"]\n",
    "training = training.drop(columns = drop)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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