march_madness.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "import collections\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "teams_data = pd.read_csv(\"kaggle_data/Teams.csv\")\n",
    "teamList = teams_data['Team_Name'].tolist()\n",
    "reg_season_detail = pd.read_csv(\"kaggle_data/RegularSeasonDetailedResults.csv\")\n",
    "reg_season_compact_pd = pd.read_csv('kaggle_data/RegularSeasonCompactResults.csv')\n",
    "seasons_data = pd.read_csv(\"kaggle_data/Seasons.csv\")\n",
    "tourney_compact = pd.read_csv(\"kaggle_data/TourneyCompactResults.csv\")\n",
    "tourney_detail = pd.read_csv(\"kaggle_data/TourneyDetailedResults.csv\")\n",
    "tourney_seeds_pd = pd.read_csv(\"kaggle_data/TourneySeeds.csv\")\n",
    "tourney_slots = pd.read_csv(\"kaggle_data/TourneySlots.csv\")\n",
    "conference_pd = pd.read_csv('Conference.csv')\n",
    "tourney_results_pd = pd.read_csv('TourneyResults.csv')\n",
    "NCAAChampionsList = tourney_results_pd['NCAA Champion'].tolist()\n",
    "SampleSubmission = pd.read_csv(\"kaggle_data/SampleSubmission.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Season Data\n",
    "Credit to https://github.com/adeshpande3/March-Madness-2017 for the aggregation helper functions, and logic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "listACCteams = ['North Carolina','Virginia','Florida St','Louisville','Notre Dame','Syracuse','Duke','Virginia Tech','Georgia Tech','Miami','Wake Forest','Clemson','NC State','Boston College','Pittsburgh']\n",
    "listPac12teams = ['Arizona','Oregon','UCLA','California','USC','Utah','Washington St','Stanford','Arizona St','Colorado','Washington','Oregon St']\n",
    "listSECteams = ['Kentucky','South Carolina','Florida','Arkansas','Alabama','Tennessee','Mississippi St','Georgia','Ole Miss','Vanderbilt','Auburn','Texas A&M','LSU','Missouri']\n",
    "listBig10teams = ['Maryland','Wisconsin','Purdue','Northwestern','Michigan St','Indiana','Iowa','Michigan','Penn St','Nebraska','Minnesota','Illinois','Ohio St','Rutgers']\n",
    "listBig12teams = ['Kansas','Baylor','West Virginia','Iowa St','TCU','Kansas St','Texas Tech','Oklahoma St','Texas','Oklahoma']\n",
    "listBigEastteams = ['Butler','Creighton','DePaul','Georgetown','Marquette','Providence','Seton Hall','St John\\'s','Villanova','Xavier']\n",
    "\n",
    "def checkPower6Conference(team_id):\n",
    "    teamName = teams_data.values[team_id-1101][1]\n",
    "    if (teamName in listACCteams or teamName in listBig10teams or teamName in listBig12teams\n",
    "       or teamName in listSECteams or teamName in listPac12teams or teamName in listBigEastteams):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def handleDifferentCSV(df):\n",
    "    # The stats CSV is a lit different in terms of naming so below is just some data cleaning\n",
    "    df['School'] = df['School'].replace('(State)', 'St', regex=True) \n",
    "    df['School'] = df['School'].replace('Albany (NY)', 'Albany NY') \n",
    "    df['School'] = df['School'].replace('Boston University', 'Boston Univ')\n",
    "    df['School'] = df['School'].replace('Central Michigan', 'C Michigan')\n",
    "    df['School'] = df['School'].replace('(Eastern)', 'E', regex=True)\n",
    "    df['School'] = df['School'].replace('Louisiana St', 'LSU')\n",
    "    df['School'] = df['School'].replace('North Carolina St', 'NC State')\n",
    "    df['School'] = df['School'].replace('Southern California', 'USC')\n",
    "    df['School'] = df['School'].replace('University of California', 'California', regex=True) \n",
    "    df['School'] = df['School'].replace('American', 'American Univ')\n",
    "    df['School'] = df['School'].replace('Arkansas-Little Rock', 'Ark Little Rock')\n",
    "    df['School'] = df['School'].replace('Arkansas-Pine Bluff', 'Ark Pine Bluff')\n",
    "    df['School'] = df['School'].replace('Bowling Green St', 'Bowling Green')\n",
    "    df['School'] = df['School'].replace('Brigham Young', 'BYU')\n",
    "    df['School'] = df['School'].replace('Cal Poly', 'Cal Poly SLO')\n",
    "    df['School'] = df['School'].replace('Centenary (LA)', 'Centenary')\n",
    "    df['School'] = df['School'].replace('Central Connecticut St', 'Central Conn')\n",
    "    df['School'] = df['School'].replace('Charleston Southern', 'Charleston So')\n",
    "    df['School'] = df['School'].replace('Coastal Carolina', 'Coastal Car')\n",
    "    df['School'] = df['School'].replace('College of Charleston', 'Col Charleston')\n",
    "    df['School'] = df['School'].replace('Cal St Fullerton', 'CS Fullerton')\n",
    "    df['School'] = df['School'].replace('Cal St Sacramento', 'CS Sacramento')\n",
    "    df['School'] = df['School'].replace('Cal St Bakersfield', 'CS Bakersfield')\n",
    "    df['School'] = df['School'].replace('Cal St Northridge', 'CS Northridge')\n",
    "    df['School'] = df['School'].replace('East Tennessee St', 'ETSU')\n",
    "    df['School'] = df['School'].replace('Detroit Mercy', 'Detroit')\n",
    "    df['School'] = df['School'].replace('Fairleigh Dickinson', 'F Dickinson')\n",
    "    df['School'] = df['School'].replace('Florida Atlantic', 'FL Atlantic')\n",
    "    df['School'] = df['School'].replace('Florida Gulf Coast', 'FL Gulf Coast')\n",
    "    df['School'] = df['School'].replace('Florida International', 'Florida Intl')\n",
    "    df['School'] = df['School'].replace('George Washington', 'G Washington')\n",
    "    df['School'] = df['School'].replace('Georgia Southern', 'Ga Southern')\n",
    "    df['School'] = df['School'].replace('Gardner-Webb', 'Gardner Webb')\n",
    "    df['School'] = df['School'].replace('Illinois-Chicago', 'IL Chicago')\n",
    "    df['School'] = df['School'].replace('Kent St', 'Kent')\n",
    "    df['School'] = df['School'].replace('Long Island University', 'Long Island')\n",
    "    df['School'] = df['School'].replace('Loyola Marymount', 'Loy Marymount')\n",
    "    df['School'] = df['School'].replace('Loyola (MD)', 'Loyola MD')\n",
    "    df['School'] = df['School'].replace('Loyola (IL)', 'Loyola-Chicago')\n",
    "    df['School'] = df['School'].replace('Massachusetts', 'MA Lowell')\n",
    "    df['School'] = df['School'].replace('Maryland-Eastern Shore', 'MD E Shore')\n",
    "    df['School'] = df['School'].replace('Miami (FL)', 'Miami FL')\n",
    "    df['School'] = df['School'].replace('Miami (OH)', 'Miami OH')\n",
    "    df['School'] = df['School'].replace('Missouri-Kansas City', 'Missouri KC')\n",
    "    df['School'] = df['School'].replace('Monmouth', 'Monmouth NJ')\n",
    "    df['School'] = df['School'].replace('Mississippi Valley St', 'MS Valley St')\n",
    "    df['School'] = df['School'].replace('Montana St', 'MTSU')\n",
    "    df['School'] = df['School'].replace('Northern Colorado', 'N Colorado')\n",
    "    df['School'] = df['School'].replace('North Dakota St', 'N Dakota St')\n",
    "    df['School'] = df['School'].replace('Northern Illinois', 'N Illinois')\n",
    "    df['School'] = df['School'].replace('Northern Kentucky', 'N Kentucky')\n",
    "    df['School'] = df['School'].replace('North Carolina A&T', 'NC A&T')\n",
    "    df['School'] = df['School'].replace('North Carolina Central', 'NC Central')\n",
    "    df['School'] = df['School'].replace('Pennsylvania', 'Penn')\n",
    "    df['School'] = df['School'].replace('South Carolina St', 'S Carolina St')\n",
    "    df['School'] = df['School'].replace('Southern Illinois', 'S Illinois')\n",
    "    df['School'] = df['School'].replace('UC-Santa Barbara', 'Santa Barbara')\n",
    "    df['School'] = df['School'].replace('Southeastern Louisiana', 'SE Louisiana')\n",
    "    df['School'] = df['School'].replace('Southeast Missouri St', 'SE Missouri St')\n",
    "    df['School'] = df['School'].replace('Stephen F. Austin', 'SF Austin')\n",
    "    df['School'] = df['School'].replace('Southern Methodist', 'SMU')\n",
    "    df['School'] = df['School'].replace('Southern Mississippi', 'Southern Miss')\n",
    "    df['School'] = df['School'].replace('Southern', 'Southern Univ')\n",
    "    df['School'] = df['School'].replace('St. Bonaventure', 'St Bonaventure')\n",
    "    df['School'] = df['School'].replace('St. Francis (NY)', 'St Francis NY')\n",
    "    df['School'] = df['School'].replace('Saint Francis (PA)', 'St Francis PA')\n",
    "    df['School'] = df['School'].replace('St. John\\'s (NY)', 'St John\\'s')\n",
    "    df['School'] = df['School'].replace('Saint Joseph\\'s', 'St Joseph\\'s PA')\n",
    "    df['School'] = df['School'].replace('Saint Louis', 'St Louis')\n",
    "    df['School'] = df['School'].replace('Saint Mary\\'s (CA)', 'St Mary\\'s CA')\n",
    "    df['School'] = df['School'].replace('Mount Saint Mary\\'s', 'Mt St Mary\\'s')\n",
    "    df['School'] = df['School'].replace('Saint Peter\\'s', 'St Peter\\'s')\n",
    "    df['School'] = df['School'].replace('Texas A&M-Corpus Christian', 'TAM C. Christian')\n",
    "    df['School'] = df['School'].replace('Texas Christian', 'TCU')\n",
    "    df['School'] = df['School'].replace('Tennessee-Martin', 'TN Martin')\n",
    "    df['School'] = df['School'].replace('Texas-Rio Grande Valley', 'UTRGV')\n",
    "    df['School'] = df['School'].replace('Texas Southern', 'TX Southern')\n",
    "    df['School'] = df['School'].replace('Alabama-Birmingham', 'UAB')\n",
    "    df['School'] = df['School'].replace('UC-Davis', 'UC Davis')\n",
    "    df['School'] = df['School'].replace('UC-Irvine', 'UC Irvine')\n",
    "    df['School'] = df['School'].replace('UC-Riverside', 'UC Riverside')\n",
    "    df['School'] = df['School'].replace('Central Florida', 'UCF')\n",
    "    df['School'] = df['School'].replace('Louisiana-Lafayette', 'ULL')\n",
    "    df['School'] = df['School'].replace('Louisiana-Monroe', 'ULM')\n",
    "    df['School'] = df['School'].replace('Maryland-Baltimore County', 'UMBC')\n",
    "    df['School'] = df['School'].replace('North Carolina-Asheville', 'UNC Asheville')\n",
    "    df['School'] = df['School'].replace('North Carolina-Greensboro', 'UNC Greensboro')\n",
    "    df['School'] = df['School'].replace('North Carolina-Wilmington', 'UNC Wilmington')\n",
    "    df['School'] = df['School'].replace('Nevada-Las Vegas', 'UNLV')\n",
    "    df['School'] = df['School'].replace('Texas-Arlington', 'UT Arlington')\n",
    "    df['School'] = df['School'].replace('Texas-San Antonio', 'UT San Antonio')\n",
    "    df['School'] = df['School'].replace('Texas-El Paso', 'UTEP')\n",
    "    df['School'] = df['School'].replace('Virginia Commonwealth', 'VA Commonwealth')\n",
    "    df['School'] = df['School'].replace('Western Carolina', 'W Carolina')\n",
    "    df['School'] = df['School'].replace('Western Illinois', 'W Illinois')\n",
    "    df['School'] = df['School'].replace('Western Kentucky', 'WKU')\n",
    "    df['School'] = df['School'].replace('Western Michigan', 'W Michigan')\n",
    "    df['School'] = df['School'].replace('Abilene Christian', 'Abilene Chr')\n",
    "    df['School'] = df['School'].replace('Montana State', 'Montana St')\n",
    "    df['School'] = df['School'].replace('Central Arkansas', 'Cent Arkansas')\n",
    "    df['School'] = df['School'].replace('Houston Baptist', 'Houston Bap')\n",
    "    df['School'] = df['School'].replace('South Dakota St', 'S Dakota St')\n",
    "    df['School'] = df['School'].replace('Maryland-Eastern Shore', 'MD E Shore')\n",
    "    return df\n",
    "\n",
    "def getTeamID(name):\n",
    "    return teams_data[teams_data['Team_Name'] == name].values[0][0]\n",
    "\n",
    "def getTeamName(team_id):\n",
    "    return teams_data[teams_data['Team_Id'] == team_id].values[0][1]\n",
    "\n",
    "def getNumChampionships(team_id):\n",
    "    name = getTeamName(team_id)\n",
    "    return NCAAChampionsList.count(name)\n",
    "\n",
    "def getListForURL(team_list):\n",
    "    team_list = [x.lower() for x in team_list]\n",
    "    team_list = [t.replace(' ', '-') for t in team_list]\n",
    "    team_list = [t.replace('st', 'state') for t in team_list]\n",
    "    team_list = [t.replace('northern-dakota', 'north-dakota') for t in team_list]\n",
    "    team_list = [t.replace('nc-', 'north-carolina-') for t in team_list]\n",
    "    team_list = [t.replace('fl-', 'florida-') for t in team_list]\n",
    "    team_list = [t.replace('ga-', 'georgia-') for t in team_list]\n",
    "    team_list = [t.replace('lsu', 'louisiana-state') for t in team_list]\n",
    "    team_list = [t.replace('maristate', 'marist') for t in team_list]\n",
    "    team_list = [t.replace('stateate', 'state') for t in team_list]\n",
    "    team_list = [t.replace('northernorthern', 'northern') for t in team_list]\n",
    "    team_list = [t.replace('usc', 'southern-california') for t in team_list]\n",
    "    base = 'http://www.sports-reference.com/cbb/schools/'\n",
    "    for team in team_list:\n",
    "        url = base + team + '/'\n",
    "getListForURL(teamList);\n",
    "\n",
    "# Function for handling the annoying cases of Florida and FL, as well as State and St\n",
    "def handleCases(arr):\n",
    "    indices = []\n",
    "    listLen = len(arr)\n",
    "    for i in range(listLen):\n",
    "        if (arr[i] == 'St' or arr[i] == 'FL'):\n",
    "            indices.append(i)\n",
    "    for p in indices:\n",
    "        arr[p-1] = arr[p-1] + ' ' + arr[p]\n",
    "    for i in range(len(indices)): \n",
    "        arr.remove(arr[indices[i] - i])\n",
    "    return arr\n",
    "\n",
    "def checkConferenceChamp(team_id, year):\n",
    "    year_conf_pd = conference_pd[conference_pd['Year'] == year]\n",
    "    champs = year_conf_pd['Regular Season Champ'].tolist()\n",
    "    # For handling cases where there is more than one champion\n",
    "    champs_separated = [words for segments in champs for words in segments.split()]\n",
    "    name = getTeamName(team_id)\n",
    "    champs_separated = handleCases(champs_separated)\n",
    "    if (name in champs_separated):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def checkConferenceTourneyChamp(team_id, year):\n",
    "    year_conf_pd = conference_pd[conference_pd['Year'] == year]\n",
    "    champs = year_conf_pd['Tournament Champ'].tolist()\n",
    "    name = getTeamName(team_id)\n",
    "    if (name in champs):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def getTourneyAppearances(team_id):\n",
    "    return len(tourney_seeds_pd[tourney_seeds_pd['Team'] == team_id].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getSeasonData(team_id, year):\n",
    "    # The data frame below holds stats for every single game in the given year\n",
    "    year_data_pd = reg_season_compact_pd[reg_season_compact_pd['Season'] == year]\n",
    "    # Finding number of points per game\n",
    "    gamesWon = year_data_pd[year_data_pd.Wteam == team_id] \n",
    "    totalPointsScored = gamesWon['Wscore'].sum()\n",
    "    gamesLost = year_data_pd[year_data_pd.Lteam == team_id] \n",
    "    totalGames = gamesWon.append(gamesLost)\n",
    "    numGames = len(totalGames.index)\n",
    "    totalPointsScored += gamesLost['Lscore'].sum()\n",
    "    \n",
    "    # Finding number of points per game allowed\n",
    "    totalPointsAllowed = gamesWon['Lscore'].sum()\n",
    "    totalPointsAllowed += gamesLost['Wscore'].sum()\n",
    "    \n",
    "    stats_SOS_pd = pd.read_csv('MMStats/MMStats_'+str(year)+'.csv')\n",
    "    stats_SOS_pd = handleDifferentCSV(stats_SOS_pd)\n",
    "    ratings_pd = pd.read_csv('RatingStats/RatingStats_'+str(year)+'.csv')\n",
    "    ratings_pd = handleDifferentCSV(ratings_pd)\n",
    "    \n",
    "    name = getTeamName(team_id)\n",
    "    team = stats_SOS_pd[stats_SOS_pd['School'] == name]\n",
    "    team_rating = ratings_pd[ratings_pd['School'] == name]\n",
    "    if (len(team.index) == 0 or len(team_rating.index) == 0): #Can't find the team\n",
    "        total3sMade = 0\n",
    "        totalTurnovers = 0\n",
    "        totalAssists = 0\n",
    "        sos = 0\n",
    "        totalRebounds = 0\n",
    "        srs = 0\n",
    "        totalSteals = 0\n",
    "    else:\n",
    "        total3sMade = team['X3P'].values[0]\n",
    "        totalTurnovers = team['TOV'].values[0]\n",
    "        if (math.isnan(totalTurnovers)):\n",
    "            totalTurnovers = 0\n",
    "        totalAssists = team['AST'].values[0]\n",
    "        if (math.isnan(totalAssists)):\n",
    "            totalAssists = 0\n",
    "        sos = team['SOS'].values[0]\n",
    "        srs = team['SRS'].values[0]\n",
    "        totalRebounds = team['TRB'].values[0]\n",
    "        if (math.isnan(totalRebounds)):\n",
    "            totalRebounds = 0\n",
    "        totalSteals = team['STL'].values[0]\n",
    "        if (math.isnan(totalSteals)):\n",
    "            totalSteals = 0\n",
    "    \n",
    "    #Finding tournament seed for that year\n",
    "    tourneyYear = tourney_seeds_pd[tourney_seeds_pd['Season'] == year]\n",
    "    seed = tourneyYear[tourneyYear['Team'] == team_id]\n",
    "    if (len(seed.index) != 0):\n",
    "        seed = seed.values[0][1]\n",
    "        tournamentSeed = int(seed[1:3])\n",
    "    else:\n",
    "        tournamentSeed = 25 #Not sure how to represent if a team didn't make the tourney\n",
    "    \n",
    "    # Finding number of wins and losses\n",
    "    numWins = len(gamesWon.index)\n",
    "    # There are some teams who may have dropped to Division 2, so they won't have games \n",
    "    # a certain year. In this case, we don't want to divide by 0, so we'll just set the\n",
    "    # averages to 0 instead\n",
    "    if numGames == 0:\n",
    "        avgPointsScored = 0\n",
    "        avgPointsAllowed = 0\n",
    "        avg3sMade = 0\n",
    "        avgTurnovers = 0\n",
    "        avgAssists = 0\n",
    "        avgRebounds = 0\n",
    "        avgSteals = 0\n",
    "    else:\n",
    "        avgPointsScored = totalPointsScored/numGames\n",
    "        avgPointsAllowed = totalPointsAllowed/numGames\n",
    "        avg3sMade = total3sMade/numGames\n",
    "        avgTurnovers = totalTurnovers/numGames\n",
    "        avgAssists = totalAssists/numGames\n",
    "        avgRebounds = totalRebounds/numGames\n",
    "        avgSteals = totalSteals/numGames\n",
    "    #return [numWins, sos, srs]\n",
    "    #return [numWins, avgPointsScored, avgPointsAllowed, checkPower6Conference(team_id), avg3sMade, avg3sAllowed, avgTurnovers,\n",
    "    #        tournamentSeed, getStrengthOfSchedule(team_id, year), getTourneyAppearances(team_id)]\n",
    "    return [numWins, avgPointsScored, avgPointsAllowed, checkPower6Conference(team_id), avg3sMade, avgAssists, avgTurnovers,\n",
    "           checkConferenceChamp(team_id, year), checkConferenceTourneyChamp(team_id, year), tournamentSeed,\n",
    "            sos, srs, avgRebounds, avgSteals, getTourneyAppearances(team_id), getNumChampionships(team_id)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End of https://github.com/adeshpande3/March-Madness-2017 stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### This is adashpande3's as well, but heavily modified\n",
    "def createSeasonDict(year):\n",
    "    seasonDictionary = collections.defaultdict(list)\n",
    "    for team in teamList:\n",
    "        team_id = teams_data[teams_data['Team_Name'] == team].values[0][0]\n",
    "        team_vector = getSeasonData(team_id, year)\n",
    "        \n",
    "        vector_headers = [\"num_wins\", \"avg_points_scored\", \"avg_points_allowed\", \"power6\", \"avg3s_made\", \"avg_assists\",\n",
    "                         \"avg_turnovers\", \"conference_chap_this_year\", \"conference_tourney_champ_this_year\", \"tournament_seed\",\n",
    "                         \"sos\", \"srs\", \"avg_rebounds\", \"avg_steals\", \"num_tourney_appearances\", \"num_championships\"]\n",
    "        seasonDictionary[team_id] = dict(zip(vector_headers, team_vector))\n",
    "        \n",
    "        del seasonDictionary[team_id][\"power6\"] #we dont' think this is useful\n",
    "        \n",
    "        #seasonDictionary[team_id] = team_vector\n",
    "    return seasonDictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s2017_results = createSeasonDict(2017)\n",
    "df_2017_results = pd.DataFrame(s2016_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "avg3s_made                             8.78125\n",
       "avg_assists                           15.65625\n",
       "avg_points_allowed                    66.75000\n",
       "avg_points_scored                     79.50000\n",
       "avg_rebounds                          44.56250\n",
       "avg_steals                             5.75000\n",
       "avg_turnovers                         12.96875\n",
       "conference_chap_this_year              0.00000\n",
       "conference_tourney_champ_this_year     0.00000\n",
       "num_championships                      0.00000\n",
       "num_tourney_appearances               20.00000\n",
       "num_wins                              25.00000\n",
       "sos                                    4.23000\n",
       "srs                                   16.92000\n",
       "tournament_seed                       11.00000\n",
       "Name: 1211, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2017_results[1211] #Stats for Gonzaga, for example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Season', 'Daynum', 'Wteam', 'Wscore', 'Lteam', 'Lscore', 'Wloc',\n",
       "       'Numot', 'Wfgm', 'Wfga', 'Wfgm3', 'Wfga3', 'Wftm', 'Wfta', 'Wor', 'Wdr',\n",
       "       'Wast', 'Wto', 'Wstl', 'Wblk', 'Wpf', 'Lfgm', 'Lfga', 'Lfgm3', 'Lfga3',\n",
       "       'Lftm', 'Lfta', 'Lor', 'Ldr', 'Last', 'Lto', 'Lstl', 'Lblk', 'Lpf'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tourney_detail.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Headers of winning and losing team information\n",
    "winHeaders = ['Wteam', 'Wscore', 'Wfgm', 'Wfga', \n",
    "              'Wfgm3', 'Wfga3', 'Wftm', 'Wfta', 'Wor', \n",
    "             'Wdr', 'Wast', 'Wto', 'Wstl', 'Wblk', 'Wpf']\n",
    "\n",
    "lossHeaders = ['Lteam', 'Lscore', 'Lfgm', 'Lfga', \n",
    "               'Lfgm3', 'Lfga3','Lftm', 'Lfta', 'Lor', \n",
    "               'Ldr', 'Last', 'Lto', 'Lstl', 'Lblk', 'Lpf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Initialize the new dataframe\n",
    "df =  pd.DataFrame(data=None, index=range(0, tourney_detail.shape[0]), \n",
    "            columns=['Ateam', 'Ascore', 'Afgm', 'Afga', \n",
    "              'Afgm3', 'Afga3', 'Aftm', 'Afta', 'Aor', \n",
    "             'Adr', 'Aast', 'Ato', 'Astl', 'Ablk', 'Apf', 'Bteam', 'Bscore', 'Bfgm', 'Bfga', \n",
    "              'Bfgm3', 'Bfga3', 'Bftm', 'Bfta', 'Bor', \n",
    "             'Bdr', 'Bast', 'Bto', 'Bstl', 'Bblk', 'Bpf', 'Awin'], dtype=None, copy=False)\n",
    "\n",
    "#Initialize Headers\n",
    "aHeaders = ['Ateam', 'Ascore', 'Afgm', 'Afga', 'Afgm3', \n",
    "            'Afga3', 'Aftm', 'Afta', 'Aor', 'Adr', 'Aast', \n",
    "            'Ato', 'Astl', 'Ablk', 'Apf']\n",
    "\n",
    "bHeaders = ['Bteam', 'Bscore', 'Bfgm', 'Bfga', \n",
    "              'Bfgm3', 'Bfga3', 'Bftm', 'Bfta', 'Bor', \n",
    "             'Bdr', 'Bast', 'Bto', 'Bstl', 'Bblk', 'Bpf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "for i in range(0, tourney_detail.shape[0]):\n",
    "    # Random Int of 1 or 0 decides whether the winning team is team A or team B\n",
    "    if (np.random.randint(2)):\n",
    "        aInsertHeaders = winHeaders\n",
    "        bInsertHeaders = lossHeaders\n",
    "        df['Awin'][i] = 1\n",
    "    else:\n",
    "        aInsertHeaders = lossHeaders\n",
    "        bInsertHeaders = winHeaders\n",
    "        df['Awin'][i] = 0\n",
    "    \n",
    "    # Puts the team data in the appropriate location in the df\n",
    "    for j in range(0, len(aHeaders)):\n",
    "        df[aHeaders[j]][i] = tourney_detail[aInsertHeaders[j]][i]\n",
    "        df[bHeaders[j]][i] = tourney_detail[bInsertHeaders[j]][i]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "columns= ['Ateam', 'Ascore', 'Afgm', 'Afga', \n",
    "              'Afgm3', 'Afga3', 'Aftm', 'Afta', 'Aor', \n",
    "             'Adr', 'Aast', 'Ato', 'Astl', 'Ablk', 'Apf', 'Bteam', 'Bscore', 'Bfgm', 'Bfga', \n",
    "              'Bfgm3', 'Bfga3', 'Bftm', 'Bfta', 'Bor', \n",
    "             'Bdr', 'Bast', 'Bto', 'Bstl', 'Bblk', 'Bpf']\n",
    "\n",
    "clf = AdaBoostClassifier(n_estimators=100)\n",
    "\n",
    "Y = list(df[\"Awin\"].values)\n",
    "X = df[list(columns)].values\n",
    "clf = clf.fit(X, Y)\n",
    "scores = cross_val_score(clf, X, Y)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in range(0, len(columns)):\n",
    "    print(columns[i], \":\", clf.feature_importances_[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Submission = pd.DataFrame(SampleSubmission['Id'].str.split(\"_\", 3, expand=True))\n",
    "Submission.rename(columns={0:\"Year\", 1:\"Team1\", 2:\"Team2\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [

    "# For row in Submission\n",
    "# Randomly choose team1 or team2 to be A, other B\n",
    "# New row in new_pd with cols TeamA TeamB TeamA... TeamB...\n",
    "# How do we have a measure of which team won? - like what is the \"Y\" for this?\n",
    "# Maybe add season aggregated data to the previous model\n",
    "\n",
    "season_matches_season_stats_pd =  pd.DataFrame(data=None, index=range(0, tourney_detail.shape[0]), \n",
    "            columns=['A_team', 'A_avg3s_made', 'A_avg_assists', 'A_avg_points_allowed', \n",
    "                     'A_avg_points_scored', 'A_avg_rebounds', 'A_avg_steals', 'A_avg_turnovers', \n",
    "                     'A_conference_chap_this_year', 'A_conference_tourney_champ_this_year', \n",
    "                     'A_num_championships', 'A_num_tourney_appearances', 'A_num_wins', 'A_sos',\n",
    "                     'A_srs', 'A_tournament_seed',\n",
    "                     'B_team', 'B_avg3s_made', 'B_avg_assists', 'B_avg_points_allowed', \n",
    "                     'B_avg_points_scored', 'B_avg_rebounds', 'B_avg_steals', 'B_avg_turnovers', \n",
    "                     'B_conference_chap_this_year', 'B_conference_tourney_champ_this_year', \n",
    "                     'B_num_championships', 'B_num_tourney_appearances', 'B_num_wins', 'B_sos',\n",
    "                     'B_srs', 'B_tournament_seed',\n",
    "                     'A_win'], dtype=None, copy=False)\n",
    "\n",
    "season_stats_headers = ['avg3s_made', 'avg_assists', 'avg_points_allowed', 'avg_points_scored',\n",
    "                        'avg_rebounds', 'avg_steals', 'avg_turnovers',\n",
    "                        'conference_chap_this_year', 'conference_tourney_champ_this_year',\n",
    "                        'num_championships', 'num_tourney_appearances', 'num_wins', 'sos',\n",
    "                        'srs', 'tournament_seed']\n",
    "\n",
    "a_season_Headers = ['A_avg3s_made', 'A_avg_assists', 'A_avg_points_allowed', \n",
    "                     'A_avg_points_scored', 'A_avg_rebounds', 'A_avg_steals', 'A_avg_turnovers', \n",
    "                     'A_conference_chap_this_year', 'A_conference_tourney_champ_this_year', \n",
    "                     'A_num_championships', 'A_num_tourney_appearances', 'A_num_wins', 'A_sos',\n",
    "                     'A_srs', 'A_tournament_seed']\n",
    "\n",
    "b_season_Headers = ['B_avg3s_made', 'B_avg_assists', 'B_avg_points_allowed', \n",
    "                     'B_avg_points_scored', 'B_avg_rebounds', 'B_avg_steals', 'B_avg_turnovers', \n",
    "                     'B_conference_chap_this_year', 'B_conference_tourney_champ_this_year', \n",
    "                     'B_num_championships', 'B_num_tourney_appearances', 'B_num_wins', 'B_sos',\n",
    "                     'B_srs', 'B_tournament_seed']\n",
    "\n",
    "a_headers_mapping = dict(zip(season_stats_headers, a_season_Headers))\n",
    "b_headers_mapping = dict(zip(season_stats_headers, a_season_Headers))\n",
    "\n",
    "reg_season_game_outcomes = pd.DataFrame.copy(reg_season_detail)\n",
    "reg_season_game_outcomes = reg_season_game_outcomes.drop(['Wscore', 'Daynum','Lscore','Wloc',\n",
    "                                                       'Numot', 'Wfgm', 'Wfga', 'Wfgm3', 'Wfga3', 'Wftm', \n",
    "                                                        'Wfta', 'Wor', 'Wdr','Wast', 'Wto', 'Wstl', 'Wblk',\n",
    "                                                        'Wpf', 'Lfgm', 'Lfga', 'Lfgm3', 'Lfga3', 'Lftm', \n",
    "                                                        'Lfta', 'Lor', 'Ldr', 'Last', 'Lto', 'Lstl', 'Lblk', 'Lpf'], axis=1)\n",
    "reg_season_game_outcomes = reg_season_game_outcomes[reg_season_game_outcomes.Season == 2017]\n",
    "reg_season_game_outcomes = reg_season_game_outcomes.drop(\"Season\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['avg3s_made', 'avg_assists', 'avg_points_allowed', 'avg_points_scored',\n",
      "       'avg_rebounds', 'avg_steals', 'avg_turnovers',\n",
      "       'conference_chap_this_year', 'conference_tourney_champ_this_year',\n",
      "       'num_championships', 'num_tourney_appearances', 'num_wins', 'sos',\n",
      "       'srs', 'tournament_seed', 'A_team'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nfor index, row in reg_season_game_outcomes.iterrows():\\n    w_team_season_stats = df_2017_results[row[\"Wteam\"]]\\n    l_team_season_stats = df_2017_results[row[\"Lteam\"]]\\n'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(reg_season_game_outcomes.columns)\n",
    "\n",
    "df_2017_results[1211]\n",
    "w_team_season_stats = df_2017_results[1211]\n",
    "print(w_team_season_stats.index)\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "for index, row in reg_season_game_outcomes.iterrows():\n",
    "    w_team_season_stats = df_2017_results[row[\"Wteam\"]]\n",
    "    l_team_season_stats = df_2017_results[row[\"Lteam\"]]\n",
    "\"\"\" \n",
    "    # print(df_2016_results[row[\"Wteam\"]]\n",
    "    # a_team, b_team, season_stats for a_team, season_stats b_team, a_win\n",
    "    # a_team from reg_season_game_outcomes\n",
    "    \n",
    "#TODO:\n",
    "#randomly make Wteam A or B\n",
    "#create A_win var appropriately\n",
    "# new row in df comprised of TeamA Team B TeamA_stats TeamB_stats A_win"

   ]
  }
 ],
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