Machine Learning for Sports Betting

Abstract

With sports betting becoming more widely legal, the use of machine learning algorithms for improving an individual’s odds of placing successful sport bets have increased. In general, applying machine learning algorithms comes with challenges such as data selection, feature engineering, and dealing with time series data. In the context of gambling, it also comes with ethical considerations such as the use of such models to gamble, the accuracy of the model, and transparency of the model. This research focuses specifically on predicting the total combined score of NBA games. This is directly applicable to the over/under bet – over if you believe the combined total score will be above the number set by the sports book and under if you believe the combined score will be less than the number set by the sports book. The goal of this research is to create a machine learning model that can accurately predict the total combined score of NBA games and consider the ethical use of the model.