diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index e79f24b4ad453e7c4cb335dd8bc1e06a1f4a4e68..0000000000000000000000000000000000000000 --- a/.gitattributes +++ /dev/null @@ -1,36 +0,0 @@ -*.7z filter=lfs diff=lfs merge=lfs -text -*.arrow filter=lfs diff=lfs merge=lfs -text -*.bin filter=lfs diff=lfs merge=lfs -text -*.bz2 filter=lfs diff=lfs merge=lfs -text -*.ckpt filter=lfs diff=lfs merge=lfs -text -*.ftz filter=lfs diff=lfs merge=lfs -text -*.gz filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.joblib filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text -*.mlmodel filter=lfs diff=lfs merge=lfs -text -*.model filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.npy filter=lfs diff=lfs merge=lfs -text -*.npz filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text -*.parquet filter=lfs diff=lfs merge=lfs -text -*.pb filter=lfs diff=lfs merge=lfs -text -*.pickle filter=lfs diff=lfs merge=lfs -text -*.pkl filter=lfs diff=lfs merge=lfs -text -*.pt filter=lfs diff=lfs merge=lfs -text -*.pth filter=lfs diff=lfs merge=lfs -text -*.rar filter=lfs diff=lfs merge=lfs -text -*.safetensors filter=lfs diff=lfs merge=lfs -text -saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text -*.tar filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tgz filter=lfs diff=lfs merge=lfs -text -*.wasm filter=lfs diff=lfs merge=lfs -text -*.xz filter=lfs diff=lfs merge=lfs -text -*.zip filter=lfs diff=lfs merge=lfs -text -*.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text -*.csv filter=lfs diff=lfs merge=lfs -text diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index e814c22482922ad48b793d27200a88a38721d268..0000000000000000000000000000000000000000 --- a/Dockerfile +++ /dev/null @@ -1,31 +0,0 @@ - -# Use the official lightweight Python image. -# https://hub.docker.com/_/python -FROM python:3.11-slim - -# Allow statements and log messages to immediately appear in the logs -ENV PYTHONUNBUFFERED True - -# Copy local code to the container image. -ENV APP_HOME /app -WORKDIR $APP_HOME -COPY . ./ - -# Install production dependencies. -RUN pip install -r requirements.txt - -RUN useradd -m -u 1000 user -USER user -ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH - -WORKDIR $APP_HOME - -COPY --chown=user . $HOME/app - -# Run the web service on container startup. Here we use the gunicorn -# webserver, with one worker process and 8 threads. -# For environments with multiple CPU cores, increase the number of workers -# to be equal to the cores available. -# Timeout is set to 0 to disable the timeouts of the workers to allow Cloud Run to handle instance scaling. -CMD exec gunicorn --bind 0.0.0.0:7860 --workers 4 --threads 8 --timeout 0 main:app diff --git a/README.md b/README.md deleted file mode 100644 index b1fb0a3abcb9899c7847c25f64bf83d17791c028..0000000000000000000000000000000000000000 --- a/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: MARCI (NFL Betting) -emoji: 🏈 -colorFrom: red -colorTo: blue -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/Source/Build/__pycache__/build.cpython-310.pyc b/Source/Build/__pycache__/build.cpython-310.pyc deleted file mode 100644 index ecf6de300e8cb9413059cd3c459d6e66283ee231..0000000000000000000000000000000000000000 Binary files a/Source/Build/__pycache__/build.cpython-310.pyc and /dev/null differ diff --git a/Source/Build/__pycache__/build.cpython-311.pyc b/Source/Build/__pycache__/build.cpython-311.pyc deleted file mode 100644 index 5909cbfe4ef98a0ecad897792933c85a8adc66a4..0000000000000000000000000000000000000000 Binary files a/Source/Build/__pycache__/build.cpython-311.pyc and /dev/null differ diff --git a/Source/Build/__pycache__/build.cpython-39.pyc b/Source/Build/__pycache__/build.cpython-39.pyc deleted file mode 100644 index 87f042092e06c92657a6a1dc160dd7f7d8e62bef..0000000000000000000000000000000000000000 Binary files a/Source/Build/__pycache__/build.cpython-39.pyc and /dev/null differ diff --git a/Source/Build/build.py b/Source/Build/build.py deleted file mode 100644 index a20e52af6872ebafd9279c25bc428658ef276b2a..0000000000000000000000000000000000000000 --- a/Source/Build/build.py +++ /dev/null @@ -1,197 +0,0 @@ -from nfl_data_py import nfl_data_py as nfl -from tqdm import tqdm -import numpy as np -import pandas as pd -pd.set_option('chained_assignment',None) -pd.set_option('display.max_columns',None) -import os -import datetime as dt - -current_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.dirname(current_directory) -data_directory = os.path.join(parent_directory, 'Data') - -year = dt.datetime.now().year -month = dt.datetime.now().month -current_season = year if month in [8,9,10,11,12] else year-1 - -def get_pbp_data(get_seasons=[]): - """ - Pull data from nflFastR's Github repo. - - """ - pbp = nfl.import_pbp_data(get_seasons) - #pbp = pd.read_csv(r"C:\Users\brayd\Downloads\play_by_play_2023.csv") - pbp['TOP_seconds'] = pbp['drive_time_of_possession'].apply(lambda x: int(x.split(':')[0]) * 60 + int(x.split(':')[1]) if pd.notnull(x) else 0) - - return pbp - - -def build_gbg_data(get_seasons=[]): - """ - Build a game-by-game dataset to use for prediction models. - - """ - print('Loading play-by-play data.') - pbp = get_pbp_data(get_seasons) - game_date_dict = dict(pbp[['game_id','game_date']].values) - teams = list(set(list(pbp['home_team'].unique()) + list(pbp['away_team'].unique()))) - seasons = pbp['season'].unique() - - print('Building game-by-game data.') - data = pd.DataFrame() - for season in seasons: - print(season) - for team_name in tqdm(teams): - # create features - team = pbp.loc[((pbp['home_team']==team_name) | (pbp['away_team']==team_name)) & (pbp['season']==season)] - team['GP'] = team['week'] - team['W'] = [1 if r>0 and team_name==h else 1 if r<0 and team_name==a else 0 for r,a,h in team[['result','away_team','home_team']].values] - team['L'] = [0 if r>0 and team_name==h else 0 if r<0 and team_name==a else 1 for r,a,h in team[['result','away_team','home_team']].values] - team['W_PCT'] = team['W']/team['GP'] - team['TOP'] = [t if team_name==p else 0 for t,p in team[['TOP_seconds','posteam']].values] - team['FGA'] = [1 if team_name==p and f==1 else 0 for p,f in team[['posteam','field_goal_attempt']].values] - team['FGM'] = [1 if team_name==p and f=='made' else 0 for p,f in team[['posteam','field_goal_result']].values] - team['FG_PCT'] = team['FGM']/team['FGA'] - team['PassTD'] = np.where((team['posteam'] == team_name) & (team['pass_touchdown'] == 1), 1, 0) - team['RushTD'] = np.where((team['posteam'] == team_name) & (team['rush_touchdown'] == 1), 1, 0) - team['PassTD_Allowed'] = np.where((team['defteam'] == team_name) & (team['pass_touchdown'] == 1), 1, 0) - team['RushTD_Allowed'] = np.where((team['defteam'] == team_name) & (team['rush_touchdown'] == 1), 1, 0) - team['PassYds'] = [y if p==team_name else 0 for p,y in team[['posteam','passing_yards']].values] - team['RushYds'] = [y if p==team_name else 0 for p,y in team[['posteam','rushing_yards']].values] - team['PassYds_Allowed'] = [y if d==team_name else 0 for d,y in team[['defteam','passing_yards']].values] - team['RushYds_Allowed'] = [y if d==team_name else 0 for d,y in team[['defteam','rushing_yards']].values] - team['Fum'] = np.where((team['defteam'] == team_name) & (team['fumble_lost'] == 1), 1, 0) - team['Fum_Allowed'] = np.where((team['posteam'] == team_name) & (team['fumble_lost'] == 1), 1, 0) - team['INT'] = np.where((team['defteam'] == team_name) & (team['interception'] == 1), 1, 0) - team['INT_Allowed'] = np.where((team['posteam'] == team_name) & (team['interception'] == 1), 1, 0) - team['Sacks'] = np.where((team['defteam'] == team_name) & (team['sack'] == 1), 1, 0) - team['Sacks_Allowed'] = np.where((team['posteam'] == team_name) & (team['sack'] == 1), 1, 0) - team['Penalties'] = np.where((team['penalty_team'] == team_name), 1, 0) - team['FirstDowns'] = [1 if team_name==p and f==1 else 0 for p,f in team[['posteam','first_down']].values] - team['3rdDownConverted'] = [1 if p==team_name and t==1 else 0 for p,t in team[['posteam','third_down_converted']].values] - team['3rdDownFailed'] = [1 if p==team_name and t==1 else 0 for p,t in team[['posteam','third_down_failed']].values] - team['3rdDownAllowed'] = [1 if d==team_name and t==1 else 0 for d,t in team[['defteam','third_down_converted']].values] - team['3rdDownDefended'] = [1 if d==team_name and t==1 else 0 for d,t in team[['defteam','third_down_failed']].values] - team['PTS'] = [ap if at==team_name else hp if ht==team_name else None for ht,at,hp,ap in team[['home_team','away_team','home_score','away_score']].values] - team['PointDiff'] = [r if team_name==h else -r if team_name==a else 0 for r,a,h in team[['result','away_team','home_team']].values] - - # aggregate from play-by-play to game-by-game - features = { - 'GP':'mean', - 'W':'mean', - 'L':'mean', - 'W_PCT':'mean', - 'TOP':'sum', - 'FGA':'sum', - 'FGM':'sum', - 'FG_PCT':'mean', - 'PassTD':'sum', - 'RushTD':'sum', - 'PassTD_Allowed':'sum', - 'RushTD_Allowed':'sum', - 'PassYds':'sum', - 'RushYds':'sum', - 'PassYds_Allowed':'sum', - 'RushYds_Allowed':'sum', - 'Fum':'sum', - 'Fum_Allowed':'sum', - 'INT':'sum', - 'INT_Allowed':'sum', - 'Sacks':'sum', - 'Sacks_Allowed':'sum', - 'Penalties':'sum', - 'FirstDowns':'sum', - '3rdDownConverted':'sum', - '3rdDownFailed':'sum', - '3rdDownAllowed':'sum', - '3rdDownDefended':'sum', - 'PTS':'mean', - 'PointDiff':'mean' - } - - game = team.groupby('game_id').agg(features).reset_index().sort_values('GP') - game[['W','L']] = game[['W','L']].expanding().sum() - game[game.columns[4:]] = game[game.columns[4:]].expanding().mean() - if season != current_season: - game[game.columns[1:]] = game[game.columns[1:]].shift() - game['TEAM'] = team_name - game['Season'] = season - else: - game['TEAM'] = team_name - game['Season'] = season - - data = pd.concat([data,game]) - - # separate home and away data and merge - data = data.merge(pbp[['game_id','home_team','away_team']].drop_duplicates()) - home = data.loc[data['home_team']==data['TEAM']] - away = data.loc[data['away_team']==data['TEAM']] - away.columns = [f'{i}.Away' for i in away.columns] - gbg = home.merge(away,left_on='game_id',right_on='game_id.Away') - gbg.drop(columns=['TEAM','TEAM.Away','home_team.Away','away_team.Away','Season.Away','game_id.Away'], inplace=True) - gbg['game_date'] = gbg['game_id'].map(game_date_dict) - - # save current data - if current_season in get_seasons: - gbg_this_year = gbg.loc[gbg['Season']==current_season] - file_path = os.path.join(data_directory, 'gbg_this_year.csv') - gbg_this_year.to_csv(file_path, index=False) - - # save historical data - if get_seasons != [current_season]: - gbg = gbg.loc[gbg['Season']!=current_season] - file_path = os.path.join(data_directory, 'gbg.csv') - gbg.to_csv(file_path, index=False) - - -def add_odds_data(): - """ - Get odds from Australian Sports Betting's free online dataset and merge it with game-by-game data. - - """ - - # get team abbreviations - team_descriptions = nfl.import_team_desc() - team_abbreviation_dict = dict(team_descriptions[['team_name','team_abbr']].values) - - # get odds - odds = pd.read_excel('https://www.aussportsbetting.com/historical_data/nfl.xlsx') - odds['Home Team'] = odds['Home Team'].str.replace('Washington Redskins','Washington Commanders').str.replace('Washington Football Team','Washington Commanders') - odds['Away Team'] = odds['Away Team'].str.replace('Washington Redskins','Washington Commanders').str.replace('Washington Football Team','Washington Commanders') - odds['Season'] = [i.year if i.month in [8,9,10,11,12] else i.year-1 for i in odds['Date']] - odds['Home Team Abbrev'] = odds['Home Team'].map(team_abbreviation_dict).str.replace('LAR','LA') - odds['Away Team Abbrev'] = odds['Away Team'].map(team_abbreviation_dict).str.replace('LAR','LA') - odds = odds[['Date','Home Score','Away Score','Home Team Abbrev','Away Team Abbrev','Home Odds Close','Away Odds Close','Total Score Close','Home Line Close']] - odds['Key'] = odds['Date'].astype(str) + odds['Home Team Abbrev'] + odds['Away Team Abbrev'] - odds = odds.drop(columns=['Date','Home Team Abbrev','Away Team Abbrev']).dropna() - odds['Home Odds'] = [round((i-1)*100) if i>= 2 else round(-100/(i-1)) for i in odds['Home Odds Close']] - odds['Away Odds'] = [round((i-1)*100) if i>= 2 else round(-100/(i-1)) for i in odds['Away Odds Close']] - odds['Home Winnings'] = [ho-1 if h>a else -1 if a>h else 0 for ho,h,a in odds[['Home Odds Close','Home Score','Away Score']].values] - odds['Away Winnings'] = [ao-1 if a>h else -1 if h>a else 0 for ao,h,a in odds[['Away Odds Close','Home Score','Away Score']].values] - - # load gbg data - file_path = os.path.join(data_directory, 'gbg.csv') - gbg = pd.read_csv(file_path) - file_path = os.path.join(data_directory, 'gbg_this_year.csv') - gbg_this_year = pd.read_csv(file_path) - - # merge and save - dataframes = [gbg, gbg_this_year] - for idx in range(2): - i = dataframes[idx] - i['Key'] = i['game_date'].astype(str) + i['home_team'] + i['away_team'] - gbg_and_odds = i.merge(odds, left_on='Key', right_on='Key') - gbg_and_odds['Home-Team-Cover'] = [1 if (h-a)>-l else 0 if (h-a)<-l else 2 for h,a,l in gbg_and_odds[['Home Score','Away Score','Home Line Close']].values] - gbg_and_odds['Home-Team-Win'] = (gbg_and_odds['Home Score']>gbg_and_odds['Away Score']).astype(int) - gbg_and_odds['Over'] = ((gbg_and_odds['Home Score'] + gbg_and_odds['Away Score'])>gbg_and_odds['Total Score Close']).astype(int) - - if idx==0: - file_path = os.path.join(data_directory, 'gbg_and_odds.csv') - else: - file_path = os.path.join(data_directory, 'gbg_and_odds_this_year.csv') - - gbg_and_odds.drop_duplicates(subset='game_id').to_csv(file_path, index=False) - - - diff --git a/Source/Build/nfl_data_py b/Source/Build/nfl_data_py deleted file mode 160000 index e4988dc303bc441108dd11f4ae93a8200aab10e1..0000000000000000000000000000000000000000 --- a/Source/Build/nfl_data_py +++ /dev/null @@ -1 +0,0 @@ -Subproject commit e4988dc303bc441108dd11f4ae93a8200aab10e1 diff --git a/Source/Build/update.py b/Source/Build/update.py deleted file mode 100644 index 7022034fb982edbc0060d05155e578891640614a..0000000000000000000000000000000000000000 --- a/Source/Build/update.py +++ /dev/null @@ -1,59 +0,0 @@ -import nfl_data_py.nfl_data_py as nfl -import build -import datetime as dt -import numpy as np -import pandas as pd -pd.set_option('chained_assignment',None) -pd.set_option('display.max_columns',None) -import os -import pickle as pkl - -current_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.dirname(current_directory) -data_directory = os.path.join(parent_directory, 'Data') -pickle_directory = os.path.join(parent_directory, 'Pickles') - -# get team abbreviations -file_path = os.path.join(pickle_directory, 'team_name_to_abbreviation.pkl') -with open(file_path, 'rb') as f: - team_name_to_abbreviation = pkl.load(f) -file_path = os.path.join(pickle_directory, 'team_abbreviation_to_name.pkl') -with open(file_path, 'rb') as f: - team_abbreviation_to_name = pkl.load(f) - -# get current season -year = dt.datetime.now().year -month = dt.datetime.now().month -current_season = year if month in [8,9,10,11,12] else year-1 - -# get schedule -print('Getting schedule.\n') -url = 'https://www.nbcsports.com/nfl/schedule' -df = pd.read_html(url) -file_path = os.path.join(pickle_directory, 'schedule.pkl') -with open(file_path, 'wb') as f: - pkl.dump(df, f) - -# update current season -build.build_gbg_data(get_seasons=[current_season]) -#build.build_gbg_data(get_seasons=range(2014,2024)) -build.add_odds_data() - -# get winners -pbp = build.get_pbp_data([current_season]) -pbp = pbp.drop_duplicates(subset='game_id') -pbp[['season','week','away','home']] = pbp['game_id'].str.split('_', expand=True) -games = pbp[['game_id','away_score','home_score','season','week','away','home']] -games[['away_score','home_score','season','week']] = games[['away_score','home_score','season','week']].astype(int) - -games['away_team'] = games['away'].map(team_abbreviation_to_name) -games['home_team'] = games['home'].map(team_abbreviation_to_name) - -games['total'] = games['away_score'] + games['home_score'] -games['winner'] = [a if a_s>h_s else h if h_s>a_s else 'Tie' for a,h,a_s,h_s in games[['away_team','home_team','away_score','home_score']].values] - -file_path = os.path.join(data_directory, 'results.csv') -games[['game_id','total','winner']].to_csv(file_path, index=False) - - - diff --git a/Source/Data/gbg.csv b/Source/Data/gbg.csv deleted file mode 100644 index 71b4e0bad029082ff2aa1b5857d134344fc4dd83..0000000000000000000000000000000000000000 --- a/Source/Data/gbg.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4b03612517c6505adabfc11be02fbd51be8ab8c104361f645401c58c128fd2d7 -size 1613287 diff --git a/Source/Data/gbg_and_odds.csv b/Source/Data/gbg_and_odds.csv deleted file mode 100644 index 0ec478ab319e1b36896162e968b543ee311894e5..0000000000000000000000000000000000000000 --- a/Source/Data/gbg_and_odds.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d6f1a3ea061ed26a13c2398938efe0a60267d07abfde4baaff82db22ec35e79f -size 1663814 diff --git a/Source/Data/gbg_and_odds_this_year-Thinkpad.csv b/Source/Data/gbg_and_odds_this_year-Thinkpad.csv deleted file mode 100644 index 73d2103f2c0173748f309c4716f84aa5c6dfccfe..0000000000000000000000000000000000000000 --- a/Source/Data/gbg_and_odds_this_year-Thinkpad.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ffb812ba82afc8e124ea3186f8bcc04e66a10b15e9507e6fa8e6eba18463c5f7 -size 276 diff --git a/Source/Data/predictions.csv b/Source/Data/predictions.csv deleted file mode 100644 index bacd0df032e3ec872d38a0169fee4ac5c65b72ae..0000000000000000000000000000000000000000 --- a/Source/Data/predictions.csv +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0c67a5b32fca86661a44a993df5b8001073ab8e99fc0a588d050bfc04bb4f0c3 -size 20000 diff --git a/Source/Data/record.json b/Source/Data/record.json index 9bac304cdd59d0575537c79f1fa0baa255db0ea1..a60b41d3fa27238dc46d99bb69d1c8cfde8f7dd0 100644 --- a/Source/Data/record.json +++ b/Source/Data/record.json @@ -1 +1 @@ -{"winners_correct": "74", "winners_incorrect": "38", "winners_tie": "", "winners_return": "66.1% accuracy, -3.0x return", "over_unders_correct": "68", "over_unders_incorrect": "76", "over_unders_push": "-2", "over_unders_return": "46.6% accuracy, -14.1x return", "latest_game": "Monday, 12/25", "over_unders_binom": "72.0% chance of equal or better performance by flipping a coin.", "winners_binom": "<1% chance of equal or better performance by flipping a coin."} \ No newline at end of file +{"winners_correct": "74", "winners_incorrect": "38", "winners_tie": "", "winners_return": "66.1% accuracy, -3.0x return", "over_unders_correct": "68", 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-warnings.filterwarnings("ignore") -from datetime import datetime - -# set dirs for other files -current_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.dirname(current_directory) -data_directory = os.path.join(parent_directory, 'Data') -model_directory = os.path.join(parent_directory, 'Models') -pickle_directory = os.path.join(parent_directory, 'Pickles') - -file_path = os.path.join(data_directory, 'gbg_this_year.csv') -gbg = pd.read_csv(file_path, low_memory=False) - -file_path = os.path.join(data_directory, 'results.csv') -results = pd.read_csv(file_path, low_memory=False) - -# get team abbreviations -file_path = os.path.join(pickle_directory, 'team_name_to_abbreviation.pkl') -with open(file_path, 'rb') as f: - team_name_to_abbreviation = pkl.load(f) - -file_path = os.path.join(pickle_directory, 'team_abbreviation_to_name.pkl') -with open(file_path, 'rb') as f: - team_abbreviation_to_name = pkl.load(f) - -# get schedule -file_path = os.path.join(pickle_directory, 'schedule.pkl') -with open(file_path, 'rb') as f: - schedule = pkl.load(f) - -# get current week -file_path = os.path.join(pickle_directory, 'the_week.pkl') -with open(file_path, 'rb') as f: - the_week = pkl.load(f) - -# load models -# moneyline -model = 'xgboost_ML_no_odds_71.4%' -file_path = os.path.join(model_directory, f'{model}.json') -xgb_ml = xgb.Booster() -xgb_ml.load_model(file_path) - -# over/under -model = 'xgboost_OU_no_odds_59.8%' -file_path = os.path.join(model_directory, f'{model}.json') -xgb_ou = xgb.Booster() -xgb_ou.load_model(file_path) - - -def get_week(): - week = the_week['week'] - year = the_week['year'] - return int(week), int(year) - - -def get_games(week): - df = schedule[week-1] - df['Away Team'] = [' '.join(i.split('\xa0')[1:]) for i in df['Away TeamAway Team']] - df['Home Team'] = [' '.join(i.split('\xa0')[1:]) for i in df['Home TeamHome Team']] - df['Date'] = pd.to_datetime(df['Game TimeGame Time']) - df['Date'] = df['Date'].dt.strftime('%A %d/%m %I:%M %p') - df['Date'] = df['Date'].apply(lambda x: f"{x.split()[0]} {int(x.split()[1].split('/')[1])}/{int(x.split()[1].split('/')[0])} {x.split()[2]}".capitalize()) - return df[['Away Team','Home Team','Date']] - - -def get_one_week(home,away,season,week): - try: - max_GP_home = gbg.loc[((gbg['home_team'] == home) | (gbg['away_team'] == home)) & (gbg['GP'] < week)]['GP'].max() - max_GP_away = gbg.loc[((gbg['home_team'] == away) | (gbg['away_team'] == away)) & (gbg['GP'] < week)]['GP'].max() - - home_df = gbg.loc[((gbg['away_team']==home) | (gbg['home_team']==home)) & (gbg['Season']==season) & (gbg['GP']==max_GP_home)] - gbg_home_team = home_df['home_team'].item() - home_df.drop(columns=['game_id','home_team','away_team','Season','game_date'], inplace=True) - home_df = home_df[[i for i in home_df.columns if '.Away' not in i] if gbg_home_team==home else [i for i in home_df.columns if '.Away' in i]] - home_df.columns = [i.replace('.Away','') for i in home_df.columns] - - away_df = gbg.loc[((gbg['away_team']==away) | (gbg['home_team']==away)) & (gbg['Season']==season) & (gbg['GP']==max_GP_away)] - gbg_home_team = away_df['home_team'].item() - away_df.drop(columns=['game_id','home_team','away_team','Season','game_date'], inplace=True) - away_df = away_df[[i for i in away_df.columns if '.Away' not in i] if gbg_home_team==away else [i for i in away_df.columns if '.Away' in i]] - away_df.columns = [i.replace('.Away','') + '.Away' for i in away_df.columns] - - df = home_df.reset_index(drop=True).merge(away_df.reset_index(drop=True), left_index=True, right_index=True) - return df - except ValueError: - return pd.DataFrame() - - -def predict(home,away,season,week,total): - global results - - # finish preparing data - if len(home)>4: - home_abbrev = team_name_to_abbreviation[home] - else: - home_abbrev = home - - if len(away)>4: - away_abbrev = team_name_to_abbreviation[away] - else: - away_abbrev = away - - data = get_one_week(home_abbrev,away_abbrev,season,week) - data['Total Score Close'] = total - matrix = xgb.DMatrix(data.astype(float).values) - - # create game id - if week < 10: - game_id = str(season) + '_0' + str(int(week)) + '_' + away_abbrev + '_' + home_abbrev - else: - game_id = str(season) + '_' + str(int(week)) + '_' + away_abbrev + '_' + home_abbrev - - try: - moneyline_result = results.loc[results['game_id']==game_id, 'winner'].item() - except: - moneyline_result = 'N/A' - - try: - ml_predicted_proba = xgb_ml.predict(matrix)[0][1] - winner_proba = max([ml_predicted_proba, 1-ml_predicted_proba]).item() - moneyline = {'Winner': [home if ml_predicted_proba>0.5 else away if ml_predicted_proba<0.5 else 'Toss-Up'], - 'Probabilities':[winner_proba], - 'Result': moneyline_result} - except: - moneyline = {'Winner': 'NA', - 'Probabilities':['N/A'], - 'Result': moneyline_result} - - try: - result = results.loc[results['game_id']==game_id, 'total'].item() - over_under_result = 'Over' if float(result)>float(total) else 'Push' if float(result)==float(total) else 'Under' - - except: - over_under_result = 'N/A' - - try: - ou_predicted_proba = xgb_ou.predict(matrix)[0][1] - ou_proba = max([ou_predicted_proba, 1-ou_predicted_proba]).item() - - over_under = {'Over/Under': ['Over' if ou_predicted_proba>0.5 else 'Under'], - 'Probability': [ou_proba], - 'Result': over_under_result} - except: - over_under = {'Over/Under': 'N/A', - 'Probability': ['N/A'], - 'Result': over_under_result} - - return game_id, moneyline, over_under diff --git a/Source/Test/__init__.py b/Source/Test/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/Source/Test/xgboost_ATS.py b/Source/Test/xgboost_ATS.py deleted file mode 100644 index 2a38426aacf582c6ac1aa8c26cb43e5211375996..0000000000000000000000000000000000000000 --- a/Source/Test/xgboost_ATS.py +++ /dev/null @@ -1,73 +0,0 @@ -from cgi import test -import xgboost as xgb -import pandas as pd -import pickle as pkl -import numpy as np -import os - -model = 'xgboost_ATS_no_odds_57.3%' - -current_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.dirname(current_directory) -data_directory = os.path.join(parent_directory, 'Data') -model_directory = os.path.join(parent_directory, 'Models') -pickle_directory = os.path.join(parent_directory, 'Pickles') - -file_path = os.path.join(model_directory, f'{model}.json') -xgb_ml = xgb.Booster() -xgb_ml.load_model(file_path) - -file_path = os.path.join(pickle_directory, 'test_games_ATS_no_odds.pkl') -with open(file_path,'rb') as f: - test_games = pkl.load(f).tolist() - -file_path = os.path.join(data_directory, 'gbg_and_odds.csv') -gbg_and_odds = pd.read_csv(file_path) -test_data = gbg_and_odds.loc[gbg_and_odds['game_id'].isin(test_games)] -test_data_matrix = xgb.DMatrix(test_data.drop(columns=['game_id','Home-Team-Win','Home-Team-Cover','Over','Season','home_team','away_team','game_date','Key','Home Score','Away Score','Home Odds Close','Away Odds Close','Home Winnings','Away Winnings','Away Odds','Home Odds']).astype(float).values) - -predicted_probas = xgb_ml.predict(test_data_matrix) -predictions = np.argmax(predicted_probas, axis=1) -test_data['predicted_proba'] = [i[1] for i in predicted_probas] -test_data['prediction'] = predictions -test_data['correct'] = test_data['Home-Team-Cover']==test_data['prediction'] -print(test_data['predicted_proba']) -print(test_data['correct'].mean()) - -bets = test_data.loc[(test_data['predicted_proba']>0.5) | (test_data['predicted_proba']<0.5)] -bets['winnings'] = [0.91 if c==1 else -1 for c in bets['correct']] - -print('Actual') -print(bets.loc[bets['Home-Team-Cover']==1].shape) -print(bets.loc[bets['Home-Team-Cover']==0].shape) -print(bets.loc[bets['Home-Team-Cover']==2].shape) - -print('Predicted') -print(bets.loc[bets['prediction']==1].shape) -print(bets.loc[bets['prediction']==0].shape) -print(bets.loc[bets['prediction']==2].shape) - - -import matplotlib.pyplot as plt -fig = plt.figure(facecolor='black') -ax = fig.add_subplot(1, 1, 1, facecolor='black') - -# Plot data with line color as RGB(0, 128, 0) -ax.plot(bets['winnings'].cumsum().values*100, linewidth=3, color=(0/255, 128/255, 0/255)) - -# Set title and labels -ax.set_title('MARCI 3.0 - Against the Spread', color='white') -ax.set_xlabel('Games Bet On', color='white') -ax.set_ylabel('Return (%)', color='white') - -# Change tick colors to white -ax.tick_params(axis='x', colors='white') -ax.tick_params(axis='y', colors='white') - -# Change axis edge colors -ax.spines['bottom'].set_color('white') -ax.spines['top'].set_color('white') -ax.spines['left'].set_color('white') -ax.spines['right'].set_color('white') - -plt.savefig(f'{model}_dark.png', facecolor='black') \ No newline at end of file diff --git a/Source/Test/xgboost_ATS_no_odds_55.3%_dark.png b/Source/Test/xgboost_ATS_no_odds_55.3%_dark.png deleted file mode 100644 index d6af99f75b04cebc36cb86f551120c1beeff9363..0000000000000000000000000000000000000000 Binary files a/Source/Test/xgboost_ATS_no_odds_55.3%_dark.png and /dev/null differ diff --git a/Source/Test/xgboost_ATS_no_odds_57.3%_dark.png b/Source/Test/xgboost_ATS_no_odds_57.3%_dark.png deleted file mode 100644 index 872c53551476c591753f5222d1a0cff646b9d39f..0000000000000000000000000000000000000000 Binary files a/Source/Test/xgboost_ATS_no_odds_57.3%_dark.png and /dev/null differ diff --git a/Source/Test/xgboost_ML.py b/Source/Test/xgboost_ML.py deleted file mode 100644 index 0196886ed85201ec82142e45a7231de19e2f7afd..0000000000000000000000000000000000000000 --- a/Source/Test/xgboost_ML.py +++ /dev/null @@ -1,59 +0,0 @@ -import xgboost as xgb -import pandas as pd -import pickle as pkl -import numpy as np -import os - -model = 'xgboost_ML_no_odds_71.4%' - -current_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.dirname(current_directory) -data_directory = os.path.join(parent_directory, 'Data') -model_directory = os.path.join(parent_directory, 'Models') -pickle_directory = os.path.join(parent_directory, 'Pickles') - -file_path = os.path.join(model_directory, f'{model}.json') -xgb_ml = xgb.Booster() -xgb_ml.load_model(file_path) - -file_path = os.path.join(pickle_directory, 'test_games_ML_no_odds.pkl') -with open(file_path,'rb') as f: - test_games = pkl.load(f).tolist() - -file_path = os.path.join(data_directory, 'gbg_and_odds.csv') -gbg_and_odds = pd.read_csv(file_path) -test_data = gbg_and_odds.loc[gbg_and_odds['game_id'].isin(test_games)] -test_data_matrix = xgb.DMatrix(test_data.drop(columns=['game_id','Over','Home-Team-Win','Season','home_team','away_team','game_date','Key','Home Score','Away Score','Home Odds Close','Away Odds Close','Home Winnings','Away Winnings','Away Odds','Home Odds']).astype(float).values) - -predicted_probas = xgb_ml.predict(test_data_matrix) -predictions = np.argmax(predicted_probas, axis=1) -test_data['predicted_proba'] = [i[1] for i in predicted_probas] -test_data['prediction'] = (test_data['predicted_proba']>0.5).astype(int) -test_data['correct'] = test_data['Home-Team-Win']==test_data['prediction'] - -bets = test_data.loc[(test_data['predicted_proba']>0.6) | (test_data['predicted_proba']<0.4)] -bets['winnings'] = [h if p==1 else a for h,a,p in bets[['Home Winnings','Away Winnings','prediction']].values] - -import matplotlib.pyplot as plt -fig = plt.figure(facecolor='black') -ax = fig.add_subplot(1, 1, 1, facecolor='black') - -# Plot data with line color as RGB(0, 128, 0) -ax.plot(bets['winnings'].cumsum().values*100, linewidth=3, color=(0/255, 128/255, 0/255)) - -# Set title and labels -ax.set_title('MARCI 3.0 - MoneyLine w/ 60% Confidence Threshold', color='white') -ax.set_xlabel('Games Bet On', color='white') -ax.set_ylabel('Return (%)', color='white') - -# Change tick colors to white -ax.tick_params(axis='x', colors='white') -ax.tick_params(axis='y', colors='white') - -# Change axis edge colors -ax.spines['bottom'].set_color('white') -ax.spines['top'].set_color('white') -ax.spines['left'].set_color('white') -ax.spines['right'].set_color('white') - -plt.savefig(f'{model}_dark.png', facecolor='black') \ No newline at end of file diff --git a/Source/Test/xgboost_ML_75.4%.png b/Source/Test/xgboost_ML_75.4%.png deleted file mode 100644 index 109fa3bfe33329a7a80b5a7b198f027d481eeee2..0000000000000000000000000000000000000000 Binary files a/Source/Test/xgboost_ML_75.4%.png and /dev/null differ diff --git a/Source/Test/xgboost_ML_75.4%_dark.png b/Source/Test/xgboost_ML_75.4%_dark.png deleted file mode 100644 index 9612e3dea1ddbbb83c4e947a51d433b3c9687b3e..0000000000000000000000000000000000000000 Binary files a/Source/Test/xgboost_ML_75.4%_dark.png and /dev/null differ diff --git a/Source/Test/xgboost_ML_no_odds_67.8%_dogs_only_dark.png 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e666282db845d5b54fd670371f445386c810550e..0000000000000000000000000000000000000000 --- a/Source/Test/xgboost_OU.py +++ /dev/null @@ -1,59 +0,0 @@ -import xgboost as xgb -import pandas as pd -import pickle as pkl -import numpy as np -import os - -model = 'xgboost_OU_no_odds_60.8%' - -current_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.dirname(current_directory) -data_directory = os.path.join(parent_directory, 'Data') -model_directory = os.path.join(parent_directory, 'Models') -pickle_directory = os.path.join(parent_directory, 'Pickles') - -file_path = os.path.join(model_directory, f'{model}.json') -xgb_ou = xgb.Booster() -xgb_ou.load_model(file_path) - -file_path = os.path.join(pickle_directory, 'test_games_OU_no_odds.pkl') -with open(file_path,'rb') as f: - test_games = pkl.load(f).tolist() - -file_path = os.path.join(data_directory, 'gbg_and_odds.csv') -gbg_and_odds = pd.read_csv(file_path) -test_data = gbg_and_odds.loc[gbg_and_odds['game_id'].isin(test_games)] -test_data_matrix = xgb.DMatrix(test_data.drop(columns=['game_id','Over','Home-Team-Win','Season','home_team','away_team','game_date','Key','Home Score','Away Score','Home Odds Close','Away Odds Close','Home Winnings','Away Winnings','Away Odds','Home Odds']).astype(float).values) - -predicted_probas = xgb_ou.predict(test_data_matrix) -predictions = np.argmax(predicted_probas, axis=1) -test_data['predicted_proba'] = [i[1] for i in predicted_probas] -test_data['prediction'] = (test_data['predicted_proba']>0.5).astype(int) -test_data['correct'] = test_data['Over']==test_data['prediction'] - -bets = test_data.loc[(test_data['predicted_proba']>0.6) | (test_data['predicted_proba']<0.4)] -bets['winnings'] = [0.91 if c else -1 for c in bets[['correct']].values] - -import matplotlib.pyplot as plt -fig = plt.figure(facecolor='black') -ax = fig.add_subplot(1, 1, 1, facecolor='black') - -# Plot data with line color as RGB(0, 128, 0) -ax.plot(bets['winnings'].cumsum().values*100, linewidth=3, color=(0/255, 128/255, 0/255)) - -# Set title and labels -ax.set_title('MARCI 3.0 - Over/Under', color='white') -ax.set_xlabel('Games Bet On', color='white') -ax.set_ylabel('Return (%)', color='white') - -# Change tick colors to white -ax.tick_params(axis='x', colors='white') -ax.tick_params(axis='y', colors='white') - -# Change axis edge colors -ax.spines['bottom'].set_color('white') -ax.spines['top'].set_color('white') -ax.spines['left'].set_color('white') -ax.spines['right'].set_color('white') - -plt.savefig(f'{model}_dark.png', facecolor='black') \ No newline at end of file diff --git a/Source/Test/xgboost_OU_59.3%.png b/Source/Test/xgboost_OU_59.3%.png deleted file mode 100644 index 734cd068df78d636fddc9b6c9c840d3f731a1e7a..0000000000000000000000000000000000000000 Binary files a/Source/Test/xgboost_OU_59.3%.png and /dev/null differ diff --git a/Source/Test/xgboost_OU_59.3%_dark.png b/Source/Test/xgboost_OU_59.3%_dark.png 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caa41ff96de95fa57c036970c9749d7d07af3fd7..0000000000000000000000000000000000000000 Binary files a/Source/Test/xgboost_OU_no_odds_60.8%_dark.png and /dev/null differ diff --git a/Source/Train/xgboost_ATS.py b/Source/Train/xgboost_ATS.py deleted file mode 100644 index 609c9608f76bc06a18817b145259eb875ec593de..0000000000000000000000000000000000000000 --- a/Source/Train/xgboost_ATS.py +++ /dev/null @@ -1,73 +0,0 @@ -import xgboost as xgb -import pandas as pd -import pickle as pkl -import numpy as np -from tqdm import tqdm -from IPython.display import clear_output -from sklearn.metrics import accuracy_score -from sklearn.model_selection import train_test_split -import os - -current_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.dirname(current_directory) -data_directory = os.path.join(parent_directory, 'Data') -model_directory = os.path.join(parent_directory, 'Models') -pickle_directory = os.path.join(parent_directory, 'Pickles') - -file_path = os.path.join(data_directory, 'gbg_and_odds.csv') -data = pd.read_csv(file_path).dropna() - -margin = data['Home-Team-Cover'] - -data.drop(columns=['Home-Team-Win','Home-Team-Cover','Over','Season','home_team','away_team','game_date','Key','Home Score','Away Score','Home Odds Close','Away Odds Close','Home Winnings','Away Winnings', 'Home Odds', 'Away Odds'], inplace=True) -features = [i for i in data.columns if i!='game_id'] -print(features) -acc_results = [] - -for x in tqdm(range(100)): - X_train, X_test, y_train, y_test = train_test_split(data, margin, test_size=.1) - - train_games = X_train['game_id'] - test_games = X_test['game_id'] - - X_train.drop(columns=['game_id'], inplace=True) - X_test.drop(columns=['game_id'], inplace=True) - - train = xgb.DMatrix(X_train.astype(float).values, label=y_train) - test = xgb.DMatrix(X_test.astype(float).values, label=y_test) - - param = { - 'max_depth': 6, - 'eta': 0.01, - 'objective': 'multi:softprob', - 'num_class': 3 - } - epochs = 500 - - model = xgb.train(param, train, epochs) - predictions = model.predict(test) - y = [] - for z in predictions: - y.append(np.argmax(z)) - - acc = round(accuracy_score(y_test, y)*100, 1) - acc_results.append(acc) - clear_output(wait=True) - print(f"Best accuracy: {max(acc_results)}%") - - # only save results if they are the best so far - if acc == max(acc_results): - file_path = os.path.join(pickle_directory, 'train_games_ATS_no_odds.pkl') - with open(file_path,'wb') as f: - pkl.dump(train_games,f) - - file_path = os.path.join(pickle_directory, 'test_games_ATS_no_odds.pkl') - with open(file_path,'wb') as f: - pkl.dump(test_games,f) - - file_path = os.path.join(model_directory, f'xgboost_ATS_no_odds_{acc}%.json') - model.save_model(file_path) - -importances = (model.get_score(importance_type='gain')) -print(pd.DataFrame(zip(features,importances.values())).sort_values(1,ascending=False)) -print('Done') diff --git a/Source/Train/xgboost_ML.py b/Source/Train/xgboost_ML.py deleted file mode 100644 index 71e67e432f5c09ff12af7416c54f70b997656b52..0000000000000000000000000000000000000000 --- a/Source/Train/xgboost_ML.py +++ /dev/null @@ -1,69 +0,0 @@ -import xgboost as xgb -import pandas as pd -import pickle as pkl -import numpy as np -from tqdm import tqdm -from IPython.display import clear_output -from sklearn.metrics import accuracy_score -from sklearn.model_selection import train_test_split -import os - -current_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.dirname(current_directory) -data_directory = os.path.join(parent_directory, 'Data') -model_directory = os.path.join(parent_directory, 'Models') -pickle_directory = os.path.join(parent_directory, 'Pickles') - -file_path = os.path.join(data_directory, 'gbg_and_odds.csv') -data = pd.read_csv(file_path).dropna() - -margin = data['Home-Team-Win'] -data.drop(columns=['Home-Team-Win','Over','Season','home_team','away_team','game_date','Key','Home Score','Away Score','Home Odds Close','Away Odds Close','Home Winnings','Away Winnings', 'Home Odds', 'Away Odds'], inplace=True) - -acc_results = [] - -for x in tqdm(range(100)): - X_train, X_test, y_train, y_test = train_test_split(data, margin, test_size=.1) - - train_games = X_train['game_id'] - test_games = X_test['game_id'] - - X_train.drop(columns=['game_id'], inplace=True) - X_test.drop(columns=['game_id'], inplace=True) - - train = xgb.DMatrix(X_train.astype(float).values, label=y_train) - test = xgb.DMatrix(X_test.astype(float).values, label=y_test) - - param = { - 'max_depth': 2, - 'eta': 0.01, - 'objective': 'multi:softprob', - 'num_class': 2 - } - epochs = 500 - - model = xgb.train(param, train, epochs) - predictions = model.predict(test) - y = [] - for z in predictions: - y.append(np.argmax(z)) - - acc = round(accuracy_score(y_test, y)*100, 1) - acc_results.append(acc) - clear_output(wait=True) - print(f"Best accuracy: {max(acc_results)}%") - - # only save results if they are the best so far - if acc == max(acc_results): - file_path = os.path.join(pickle_directory, 'train_games_ML_no_odds.pkl') - with open(file_path,'wb') as f: - pkl.dump(train_games,f) - - file_path = os.path.join(pickle_directory, 'test_games_ML_no_odds.pkl') - with open(file_path,'wb') as f: - pkl.dump(test_games,f) - - file_path = os.path.join(model_directory, f'xgboost_ML_no_odds_{acc}%.json') - model.save_model(file_path) - -print('Done') \ No newline at end of file diff --git a/Source/Train/xgboost_OU.py b/Source/Train/xgboost_OU.py deleted file mode 100644 index 33b263ccd9d9b0476a28bbb94825cdb8d24efb80..0000000000000000000000000000000000000000 --- a/Source/Train/xgboost_OU.py +++ /dev/null @@ -1,70 +0,0 @@ -import xgboost as xgb -import pandas as pd -import pickle as pkl -import numpy as np -from tqdm import tqdm -from IPython.display import clear_output -from sklearn.metrics import accuracy_score -from sklearn.model_selection import train_test_split -import os - -current_directory = os.path.dirname(os.path.abspath(__file__)) -parent_directory = os.path.dirname(current_directory) -data_directory = os.path.join(parent_directory, 'Data') -model_directory = os.path.join(parent_directory, 'Models') -pickle_directory = os.path.join(parent_directory, 'Pickles') - -file_path = os.path.join(data_directory, 'gbg_and_odds.csv') -data = pd.read_csv(file_path).dropna() - -OU = data['Over'] -data.drop(columns=['Home-Team-Win','Over','Season','home_team','away_team','game_date','Key','Home Score','Away Score','Home Odds Close','Away Odds Close','Home Winnings','Away Winnings','Away Odds','Home Odds'], inplace=True) - -acc_results = [] - -for x in tqdm(range(100)): - X_train, X_test, y_train, y_test = train_test_split(data, OU, test_size=.1) - - train_games = X_train['game_id'] - test_games = X_test['game_id'] - - X_train.drop(columns=['game_id'], inplace=True) - X_test.drop(columns=['game_id'], inplace=True) - - train = xgb.DMatrix(X_train.astype(float).values, label=y_train) - test = xgb.DMatrix(X_test.astype(float).values, label=y_test) - - param = { - 'max_depth': 6, - 'eta': 0.05, - 'objective': 'multi:softprob', - 'num_class': 3 - } - epochs = 300 - - model = xgb.train(param, train, epochs) - predictions = model.predict(test) - y = [] - - for z in predictions: - y.append(np.argmax(z)) - - acc = round(accuracy_score(y_test, y)*100, 1) - acc_results.append(acc) - clear_output(wait=True) - print(f"Best accuracy: {max(acc_results)}%") - - # only save results if they are the best so far - if acc == max(acc_results): - file_path = os.path.join(pickle_directory, 'train_games_OU_no_odds.pkl') - with open(file_path,'wb') as f: - pkl.dump(train_games,f) - - file_path = os.path.join(pickle_directory, 'test_games_OU_no_odds.pkl') - with open(file_path,'wb') as f: - pkl.dump(test_games,f) - - file_path = os.path.join(model_directory, f'xgboost_OU_no_odds_{acc}%.json') - model.save_model(file_path) - -print('Done') \ No newline at end of file diff --git a/get_record.py b/get_record.py deleted file mode 100644 index 90dea6ac72f44280505dcb3c720db945a75adbb9..0000000000000000000000000000000000000000 --- a/get_record.py +++ /dev/null @@ -1,177 +0,0 @@ -from datetime import date, datetime -import numpy as np -import pandas as pd -pd.set_option('chained_assignment',None) -pd.set_option('display.max_columns',None) -import os -import pickle as pkl -from Source.Predict.predict import predict - -# get team abbreviations -with open('Source/Pickles/team_abbreviation_to_name.pkl', 'rb') as f: - team_abbreviation_to_name = pkl.load(f) - -# get this year's odds and results -gbg_and_odds_this_year = pd.read_csv('Source/Data/gbg_and_odds_this_year.csv') -results = pd.read_csv('Source/Data/results.csv') - -# make predictions -from tqdm import tqdm -print("Predicting games and getting record") -predictions = {} -for game_id,home,away,season,week,total in tqdm(gbg_and_odds_this_year[['game_id','home_team','away_team','Season','GP','Total Score Close']].values): - if week!=1: - predictions[game_id] = predict(home,away,season,week,total) - -# merge data -predictions_df = pd.DataFrame(predictions).T -predictions_df['predicted_winner'] = [i['Winner'][0] if type(i['Winner'])==list else None for i in predictions_df[1]] -predictions_df['predicted_winner'] = predictions_df['predicted_winner'].map(team_abbreviation_to_name) -predictions_df['predicted_winner_probability'] = [i['Probabilities'][0] if type(i['Probabilities'])==list else None for i in predictions_df[1]] -predictions_df['predicted_over_under'] = [i['Over/Under'][0] if type(i['Over/Under'])==list else None for i in predictions_df[2]] -predictions_df['predicted_over_under_probability'] = [i['Probability'][0] if type(i['Probability'])==list else None for i in predictions_df[2]] -predictions_df = predictions_df.merge(results, left_index=True, right_on='game_id').merge(gbg_and_odds_this_year[['game_id','Total Score Close','home_team','away_team','game_date','Home Odds Close','Away Odds Close']]).dropna(subset=['predicted_winner']) -predictions_df['over_under'] = ['Over' if t>tsc else 'Under' if tdatetime(year=2023,month=9,day=19)] - -# Save -predictions_df.to_csv('Source/Data/predictions.csv') -bins = np.arange(0.5, 1.05, 0.05) -bin_midpoints = [(bins[i] + bins[i+1]) / 2 for i in range(len(bins) - 1)] - -predictions_df['winner_probability_bin'] = pd.cut(predictions_df['predicted_winner_probability'], bins=bins, labels=bin_midpoints) -predictions_df['over_under_probability_bin'] = pd.cut(predictions_df['predicted_over_under_probability'], bins=bins, labels=bin_midpoints) -winner_binned = predictions_df.groupby('winner_probability_bin')['winner_correct'].mean().reset_index() -over_under_binned = predictions_df.groupby('over_under_probability_bin')['over_under_correct'].mean().reset_index() - -## plot - -import matplotlib.pyplot as plt -import numpy as np - -def style_plot(ax, title): - ax.set_facecolor('black') - ax.set_title(title, color='white') - ax.set_xlabel('MARCI Predicted Probability', color='white') - ax.set_ylabel('Actual Probability', color='white') - ax.tick_params(axis='x', colors='white') - ax.tick_params(axis='y', colors='white') - ax.spines['bottom'].set_color('white') - ax.spines['top'].set_color('white') - ax.spines['left'].set_color('white') - ax.spines['right'].set_color('white') - #ax.grid(True, linestyle='--', linewidth=0.5, color='grey') - ax.set_ylim((0,1.1)) - -def add_identity_line(ax, max_x): - x = np.linspace(0.5, max_x, 100) - ax.plot(x, x, linestyle='--', color='grey') - -def add_best_fit_line(ax, x_values, y_values): - x_values = x_values.astype('float64') - y_values = y_values.astype('float64') - mask = ~np.isnan(x_values) & ~np.isnan(y_values) - x_values = x_values[mask] - y_values = y_values[mask] - coef = np.polyfit(x_values, y_values, 1) - poly1d_fn = np.poly1d(coef) - ax.plot(x_values, poly1d_fn(x_values), color='green') - corr = np.corrcoef(x_values, y_values)[0,1] - max_x = np.max(x_values) - max_y = poly1d_fn(max_x) - #ax.text(max_x, max_y, f'Corr: {corr:.2f}', color='green') - -# Create the Winner scatter plot -x_values_winner = winner_binned['winner_probability_bin'] -y_values_winner = winner_binned['winner_correct'] -fig1 = plt.figure(facecolor='black') -ax1 = fig1.add_subplot(1, 1, 1) -ax1.scatter(x_values_winner, - y_values_winner, - color=(0/255, 128/255, 0/255), s=100, marker='o') -add_identity_line(ax1, predictions_df['predicted_winner_probability'].max()) -add_best_fit_line(ax1, predictions_df['predicted_winner_probability'], predictions_df['winner_correct']) -line, = ax1.plot([], [], linestyle='--', color='grey') -marci_line, = ax1.plot([], [], color='green') -ax1.legend([line, marci_line], ['Perfect Model', 'MARCI'], loc='upper left', facecolor='black', edgecolor='white', labelcolor='white') -style_plot(ax1, 'Winner Predictions') -plt.savefig('Static/Winner_Predictions_dark.png', facecolor='black') -plt.close(fig1) - -# Create the Over/Under scatter plot -x_values_over_under = over_under_binned['over_under_probability_bin'] -y_values_over_under = over_under_binned['over_under_correct'] -fig2 = plt.figure(facecolor='black') -ax2 = fig2.add_subplot(1, 1, 1) -ax2.scatter(x_values_over_under, - y_values_over_under, - color=(0/255, 128/255, 0/255), s=100, marker='o') -add_identity_line(ax2, predictions_df['predicted_over_under_probability'].max()) -add_best_fit_line(ax2, predictions_df['predicted_over_under_probability'], predictions_df['over_under_correct']) -line, = ax2.plot([], [], linestyle='--', color='grey') -marci_line, = ax2.plot([], [], color='green') -ax2.legend([line, marci_line], ['Perfect Model', 'MARCI'], loc='upper left', facecolor='black', edgecolor='white', labelcolor='white') -style_plot(ax2, 'Over/Under Predictions') -plt.savefig('Static/Over_Under_Predictions_dark.png', facecolor='black') -plt.close(fig2) - - -## get record -threshold = 0.6 - -winners_correct = predictions_df.loc[predictions_df['predicted_winner_probability']>threshold, 'winner_correct'].sum() -winners_accuracy = predictions_df.loc[predictions_df['predicted_winner_probability']>threshold, 'winner_correct'].mean() -winners_incorrect = predictions_df.loc[predictions_df['predicted_winner_probability']>threshold,'winner_incorrect'].sum() -winners_tie = predictions_df.loc[predictions_df['predicted_winner_probability']>threshold,'winner_tie'].sum() -winners_return = predictions_df.loc[predictions_df['predicted_winner_probability']>threshold, 'winner_return'].sum() - -over_unders_correct = predictions_df.loc[predictions_df['predicted_over_under_probability']>threshold,'over_under_correct'].sum() -over_unders_accuracy = predictions_df.loc[predictions_df['predicted_over_under_probability']>threshold,'over_under_correct'].mean() -over_unders_incorrect = predictions_df.loc[predictions_df['predicted_over_under_probability']>threshold,'over_under_incorrect'].sum() -over_unders_push = predictions_df.loc[predictions_df['predicted_over_under_probability']>threshold,'over_under_push'].sum() -over_unders_return = predictions_df.loc[predictions_df['predicted_over_under_probability']>threshold,'over_under_return'].sum() - -max_date = predictions_df['game_date'].max() -latest_game = pd.Timestamp(max_date).strftime("%A, %m/%d") - -## get binom prob -from scipy.stats import binom - -def compare_to_coinflip(c,n): - prob_fewer = binom.cdf(c, n, 0.5) - prob_more = 1 - prob_fewer - return f"{round(prob_more*100,1)}% chance of equal or better performance by flipping a coin." - -record = {"winners_correct":str(winners_correct), - "winners_incorrect":str(winners_incorrect), - "winners_tie":("-"+str(winners_tie) if winners_tie>0 else ''), - "winners_return": str(round(winners_accuracy*100,1))+"% accuracy, " + str(round(winners_return,1))+"x return", - "over_unders_correct":str(over_unders_correct), - "over_unders_incorrect":str(over_unders_incorrect), - "over_unders_push":("-"+str(over_unders_push) if over_unders_push>0 else ''), - "over_unders_return": str(round(over_unders_accuracy*100,1))+"% accuracy, " + str(round(over_unders_return,1))+"x return", - "latest_game":latest_game, - "over_unders_binom":compare_to_coinflip(over_unders_correct, (over_unders_incorrect+over_unders_correct)), - "winners_binom":compare_to_coinflip(winners_correct, (winners_incorrect+winners_correct))} - -import json -with open('Source/Data/record.json', 'w') as f: - json.dump(record,f) - diff --git a/main.py b/main.py deleted file mode 100644 index 06b41013af33440513fafa88d4261756a7971d3a..0000000000000000000000000000000000000000 --- a/main.py +++ /dev/null @@ -1,102 +0,0 @@ -from Source.Predict import predict -from flask import Flask, render_template, jsonify, request, session -import requests -import pickle as pkl -import pandas as pd -import numpy as np -pd.set_option('display.max_columns', None) -pd.set_option('display.expand_frame_repr', False) - -import json -with open('Source/Data/record.json','r') as f: - record = json.load(f) -with open('Source/Data/lines.json','r') as f: - lines = json.load(f) - -app = Flask(__name__, template_folder="Templates", static_folder="Static", static_url_path="/Static") -app.config.update( - SESSION_COOKIE_SECURE=True, - SESSION_COOKIE_SAMESITE='None', -) -app.secret_key = 'green-flounder' - -# get week, season -current_week, season = predict.get_week() -current_games = predict.get_games(current_week)[['Date','Away Team','Home Team']] -available_weeks = list(range(current_week+1))[3:] -available_weeks.reverse() - -# load current data by default -@app.route('/') -def index(): - print('Current Week', current_week) - session['selected_week'] = current_week - - for week in available_weeks: - session[f'games_week_{week}'] = None - - session[f'games_week_{current_week}'] = current_games.to_json() - return render_template('index.html', **record) - -# send week list to front end -@app.route('/get_weeks') -def get_weeks(): - return jsonify(available_weeks) - -# send lines to front end -@app.route('/get_lines') -def get_lines(): - try: - return jsonify(lines[str(session.get('selected_week'))]) - except: - return jsonify(lines[str(current_week)]) - -# send games of selected week to front end -@app.route('/get_games') -def get_games(): - requested_week = int(request.args.get('week')) - session['selected_week'] = requested_week - - # If select a new week - if requested_week and requested_week != current_week: - print("Requested Week:", requested_week) - # Check if that week's games are cached - if session.get(f'games_week_{requested_week}'): - print("Using cached games") - print(session.get(f'games_week_{requested_week}')) - games = session.get(f'games_week_{requested_week}') - games = json.loads(games) - return jsonify(games) - else: - games = predict.get_games(requested_week)[['Date','Away Team','Home Team']] - session[f'games_week_{requested_week}'] = games.to_json(orient='records') - return jsonify(games.to_dict(orient='records')) - else: - games = current_games - return jsonify(games.to_dict(orient='records')) - -# make predictions -@app.route('/submit_games', methods=['POST']) -def submit_games(): - data = request.json - data = pd.DataFrame(data).replace('', np.nan).dropna() - home_teams = data['HomeTeam'].values - away_teams = data['AwayTeam'].values - ou_lines = data['OverUnderLine'].values - row_indices = data['rowIndex'].values - - moneylines = [] - over_unders = [] - for row_index,home,away,total in zip(row_indices,home_teams,away_teams,ou_lines): - selected_week = session.get('selected_week') - game_id, moneyline, over_under = predict.predict(home,away,season,selected_week,total) - moneyline['rowIndex'] = int(row_index) - over_under['rowIndex'] = int(row_index) - moneylines.append(moneyline) - over_unders.append(over_under) - - return jsonify({'moneylines': moneylines, - 'over_unders': over_unders}) - -if __name__ == '__main__': - app.run(host='0.0.0.0', port='7860', debug=True) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 87042469007bc493083893b12163ddb7a31eca2a..0000000000000000000000000000000000000000 --- a/requirements.txt +++ /dev/null @@ -1,10 +0,0 @@ -Flask==2.2.5 -requests==2.31.0 -gunicorn==20.1.0 -xgboost==1.7.6 -beautifulsoup4==4.12.2 -pandas==1.5.3 -numpy==1.25.1 -matplotlib==3.7.1 -tqdm==4.65.0 -lxml==4.9.2 \ No newline at end of file diff --git a/update_data.bat b/update_data.bat deleted file mode 100644 index 24aca3d0228ef0704d307196f3bb7a7b1bc43701..0000000000000000000000000000000000000000 --- a/update_data.bat +++ /dev/null @@ -1,11 +0,0 @@ -python "C:\Users\Brayden\OneDrive - stern.nyu.edu\Brayden Moore LLC\Python\Projects\MARCI 3.0\MARCI-NFL-Betting\Source\Build\update.py" -python "C:\Users\Brayden\OneDrive - stern.nyu.edu\Brayden Moore LLC\Python\Projects\MARCI 3.0\MARCI-NFL-Betting\get_record.py" -cd "C:\Users\Brayden\OneDrive - stern.nyu.edu\Brayden Moore LLC\Python\Projects\MARCI 3.0\MARCI-NFL-Betting" -git add "Source\Data\gbg_and_odds_this_year.csv" -git add "Source\Data\gbg_this_year.csv" -git add "Source\Data\results.csv" -git add "Source\Data\record.json" -git add "Source\Data\lines.json" -git commit -m "Update with up to date data" -git push -pause \ No newline at end of file diff --git a/update_data.sh b/update_data.sh deleted file mode 100644 index 0f1cd0435b9a650f03b05e34ad16ce583ebb614b..0000000000000000000000000000000000000000 --- a/update_data.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash -export PATH=/opt/homebrew/bin/:$PATH -echo "Updating MARCI!" -cd "/Users/braydenmoore/Library/CloudStorage/OneDrive-stern.nyu.edu/Brayden Moore LLC/Python/Projects/MARCI 3.0/MARCI-NFL-Betting" -python3 "Source/Build/update.py" -python3 "get_record.py" -echo "Committing to repo!" -git add "Source/Data/gbg_and_odds_this_year.csv" -git add "Source/Data/gbg_this_year.csv" -git add "Source/Data/results.csv" -git add "Source/Data/record.json" -git add "Source/Data/lines.json" -git add "Source/Pickles" -git add "Static" -git add "Templates" -git commit -m "Update with up to date data" -git push all main \ No newline at end of file