Using PYMC3 to model NBA matches
- get_daily_player_gamelogs.py: data by player/game req:api (train)
- get_seasonal_games.py: scheduled games for the season req:api
- manually download odds from https://sportsbookreviewsonline.com/scoresoddsarchives/nba/nbaoddsarchives.htm
- parse_daily_player_gamelogs: all player/games in one clean table (train)
- parse_seasonal_games.py: all games from the season
- parse_odds.py: write clean csv with odds
- shared_reg: feature engineering + bayesian model
- odds: calculate return on test data using odds.
- get_daily_lineup.py: expected lineup per game
- parse_game_lineup.py: write clean csv with lineups
- predict_one: predict one particular game
- predict_batch: predict all matches for selected dates
- benchmark: results from a model with one intercept (dummy) per player (not bayesian)
- out_of_training.ipynb: notebook showing what happens if you include a player that wasn't seen in training