A gamma process based in-play prediction model for National Basketball Association games

This paper presents an in-play prediction model based on the gamma process for the scoring processes of the National Basketball Association matches. The model is team-specific, i.e., it takes account of the relative strengths of the two teams playing in a match. The dependence between the home and away scoring processes is characterized by a common latent variable. A Bayesian dynamic forecasting procedure for future games is developed, which utilizes the in-match information to update the scale parameter of the model as the match progresses. An evaluation against baseline models is provided in an empirical study. Our proposed model can predict the final score and total points, while the baseline models are unable to make such predictions. Furthermore, our model can produce positive returns on the point spread betting market and the over-under betting market.