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main.py
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main.py
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import argparse
from datetime import datetime, timedelta
import pandas as pd
import tensorflow as tf
from colorama import Fore, Style
from src.DataProviders.SbrOddsProvider import SbrOddsProvider
from src.Predict import NN_Runner, XGBoost_Runner
from src.Utils.Dictionaries import team_index_current
from src.Utils.tools import create_todays_games_from_odds, get_json_data, to_data_frame, get_todays_games_json, create_todays_games
todays_games_url = 'https://data.nba.com/data/10s/v2015/json/mobile_teams/nba/2022/scores/00_todays_scores.json'
data_url = 'https://stats.nba.com/stats/leaguedashteamstats?' \
'Conference=&DateFrom=&DateTo=&Division=&GameScope=&' \
'GameSegment=&LastNGames=0&LeagueID=00&Location=&' \
'MeasureType=Base&Month=0&OpponentTeamID=0&Outcome=&' \
'PORound=0&PaceAdjust=N&PerMode=PerGame&Period=0&' \
'PlayerExperience=&PlayerPosition=&PlusMinus=N&Rank=N&' \
'Season=2022-23&SeasonSegment=&SeasonType=Regular+Season&ShotClockRange=&' \
'StarterBench=&TeamID=0&TwoWay=0&VsConference=&VsDivision='
def createTodaysGames(games, df, odds):
match_data = []
todays_games_uo = []
home_team_odds = []
away_team_odds = []
home_team_days_rest = []
away_team_days_rest = []
for game in games:
home_team = game[0]
away_team = game[1]
if home_team not in team_index_current or away_team not in team_index_current:
continue
if odds is not None:
game_odds = odds[home_team + ':' + away_team]
todays_games_uo.append(game_odds['under_over_odds'])
home_team_odds.append(game_odds[home_team]['money_line_odds'])
away_team_odds.append(game_odds[away_team]['money_line_odds'])
else:
todays_games_uo.append(input(home_team + ' vs ' + away_team + ': '))
home_team_odds.append(input(home_team + ' odds: '))
away_team_odds.append(input(away_team + ' odds: '))
# calculate days rest for both teams
dateparse = lambda x: datetime.strptime(x, '%d/%m/%Y %H:%M')
schedule_df = pd.read_csv('Data/nba-2023-UTC.csv', parse_dates=['Date'], date_parser=dateparse)
home_games = schedule_df[(schedule_df['Home Team'] == home_team) | (schedule_df['Away Team'] == home_team)]
away_games = schedule_df[(schedule_df['Home Team'] == away_team) | (schedule_df['Away Team'] == away_team)]
previous_home_games = home_games.loc[schedule_df['Date'] <= datetime.today()].sort_values('Date',ascending=False).head(1)['Date']
previous_away_games = away_games.loc[schedule_df['Date'] <= datetime.today()].sort_values('Date',ascending=False).head(1)['Date']
if len(previous_home_games) > 0:
last_home_date = previous_home_games.iloc[0]
home_days_off = timedelta(days=1) + datetime.today() - last_home_date
else:
home_days_off = timedelta(days=7)
if len(previous_away_games) > 0:
last_away_date = previous_away_games.iloc[0]
away_days_off = timedelta(days=1) + datetime.today() - last_away_date
else:
away_days_off = timedelta(days=7)
# print(f"{away_team} days off: {away_days_off.days} @ {home_team} days off: {home_days_off.days}")
home_team_days_rest.append(home_days_off.days)
away_team_days_rest.append(away_days_off.days)
home_team_series = df.iloc[team_index_current.get(home_team)]
away_team_series = df.iloc[team_index_current.get(away_team)]
stats = pd.concat([home_team_series, away_team_series])
stats['Days-Rest-Home'] = home_days_off.days
stats['Days-Rest-Away'] = away_days_off.days
match_data.append(stats)
games_data_frame = pd.concat(match_data, ignore_index=True, axis=1)
games_data_frame = games_data_frame.T
frame_ml = games_data_frame.drop(columns=['TEAM_ID', 'TEAM_NAME'])
data = frame_ml.values
data = data.astype(float)
return data, todays_games_uo, frame_ml, home_team_odds, away_team_odds
def main():
odds = None
if args.odds:
odds = SbrOddsProvider(sportsbook=args.odds).get_odds()
games = create_todays_games_from_odds(odds)
if len(games) == 0:
print("No games found.")
return
if (games[0][0] + ':' + games[0][1]) not in list(odds.keys()):
print(games[0][0] + ':' + games[0][1])
print(Fore.RED,"--------------Games list not up to date for todays games!!! Scraping disabled until list is updated.--------------")
print(Style.RESET_ALL)
odds = None
else:
print(f"------------------{args.odds} odds data------------------")
for g in odds.keys():
home_team, away_team = g.split(":")
print(f"{away_team} ({odds[g][away_team]['money_line_odds']}) @ {home_team} ({odds[g][home_team]['money_line_odds']})")
else:
data = get_todays_games_json(todays_games_url)
games = create_todays_games(data)
data = get_json_data(data_url)
df = to_data_frame(data)
data, todays_games_uo, frame_ml, home_team_odds, away_team_odds = createTodaysGames(games, df, odds)
if args.nn:
print("------------Neural Network Model Predictions-----------")
data = tf.keras.utils.normalize(data, axis=1)
NN_Runner.nn_runner(data, todays_games_uo, frame_ml, games, home_team_odds, away_team_odds, args.kc)
print("-------------------------------------------------------")
if args.xgb:
print("---------------XGBoost Model Predictions---------------")
XGBoost_Runner.xgb_runner(data, todays_games_uo, frame_ml, games, home_team_odds, away_team_odds, args.kc)
print("-------------------------------------------------------")
if args.A:
print("---------------XGBoost Model Predictions---------------")
XGBoost_Runner.xgb_runner(data, todays_games_uo, frame_ml, games, home_team_odds, away_team_odds, args.kc)
print("-------------------------------------------------------")
data = tf.keras.utils.normalize(data, axis=1)
print("------------Neural Network Model Predictions-----------")
NN_Runner.nn_runner(data, todays_games_uo, frame_ml, games, home_team_odds, away_team_odds, args.kc)
print("-------------------------------------------------------")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Model to Run')
parser.add_argument('-xgb', action='store_true', help='Run with XGBoost Model')
parser.add_argument('-nn', action='store_true', help='Run with Neural Network Model')
parser.add_argument('-A', action='store_true', help='Run all Models')
parser.add_argument('-odds', help='Sportsbook to fetch from. (fanduel, draftkings, betmgm, pointsbet, caesars, wynn, bet_rivers_ny')
parser.add_argument('-kc', action='store_true', help='Calculates percentage of bankroll to bet based on model edge')
args = parser.parse_args()
main()