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.

中文翻译：

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基于点的贝叶斯层次模型来预测网球比赛的结果

摘要网球中一个公认的假设是，一场比赛中每位球员发球的得分结果是独立的并且分布均匀（iid）。以此假设为例，为两个玩家指定发球概率就足以得出各种各样的事件分布，例如预期的获胜者和比赛次数以及比赛次数。但是，使用这种假设的模型（我们将其称为“基于点数”）在预测比赛获胜者方面的表现通常比文献中的其他模型差。本文提出了一种基于点的贝叶斯分层模型来预测网球比赛的结果。该模型预测在给定的水面，比赛和比赛日期上赢得积分的概率。每位球员都有发球和接发球技能，并假设他们随时间跟随高斯随机漫步。此外，每个球员的技能会因表面而异，并且会为比赛提供特定于比赛的拦截。当在ATP 2014赛季进行评估时，该模型的表现优于其他基于点的模型，预测比赛结果的准确性更高（分别为68.8％和66.3％）和更低的对数损失（分别为0.592和0.641）。该结果与直接对比赛结果进行建模的方法相比具有竞争力，证明了基于点的建模方法的预测潜力。