A point-based Bayesian hierarchical model to predict the outcome of tennis matches

Ingram Martin ()
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Ingram Martin: University of Melbourne, School of BioSciences, Melbourne, Australia

Journal of Quantitative Analysis in Sports, 2019, vol. 15, issue 4, 313-325

Abstract: A well-established assumption in tennis is that point outcomes on each player’s serve in a match are independent and identically distributed (iid). With this assumption, it is enough to specify the serve probabilities for both players to derive a wide variety of event distributions, such as the expected winner and number of sets, and number of games. However, models using this assumption, which we will refer to as “point-based”, have typically performed worse than other models in the literature at predicting the match winner. This paper presents a point-based Bayesian hierarchical model for predicting the outcome of tennis matches. The model predicts the probability of winning a point on serve given surface, tournament and match date. Each player is given a serve and return skill which is assumed to follow a Gaussian random walk over time. In addition, each player’s skill varies by surface, and tournaments are given tournament-specific intercepts. When evaluated on the ATP’s 2014 season, the model outperforms other point-based models, predicting match outcomes with greater accuracy (68.8% vs. 66.3%) and lower log loss (0.592 vs. 0.641). The results are competitive with approaches modelling the match outcome directly, demonstrating the forecasting potential of the point-based modelling approach.

Keywords: Bayesian modelling; random walk; sports forecasting (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/jqas-2018-0008

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