Simulating a basketball match with a homogeneous Markov model and forecasting the outcome

We used a possession‐based Markov model to model the progression of a basketball match. The model’s transition matrix was estimated directly from NBA play‐by‐play data and indirectly from the teams’ summary statistics. We evaluated both this approach and other commonly used forecasting approaches: logit regression of the outcome, a latent strength rating method, and bookmaker odds. We found that the Markov model approach is appropriate for modelling a basketball match and produces forecasts of a quality comparable to that of other statistical approaches, while giving more insight into basketball. Consistent with previous studies, bookmaker odds were the best probabilistic forecasts.