大满贯赛事网球比赛的建模和预测

Pub Date : 2024-02-29 DOI:10.3233/jsa-240670
N. Buhamra, A. Groll, S. Brunner
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引用次数: 0

摘要

本手稿提出了对大满贯赛事中的网球比赛进行建模和预测的不同方法。本文使用的数据包含 2011-2022 年男子大满贯赛事中 5013 场比赛的信息。所有考虑到的方法都基于回归模型,对第一名选手获胜的概率进行建模。我们考虑了几个潜在的协变量,包括球员年龄、ATP 排名和积分、赔率、elo 评分以及两个额外的年龄变量,其中考虑到网球运动员的最佳年龄在 28 岁至 32 岁之间。我们针对 2011 年至 2021 年的比赛,采用 43 倍交叉验证的方法,比较了不同回归模型方法的三个性能指标,即分类率、预测伯努利可能性和布赖尔得分。然后选出平均排名最高的前五个最优模型。为了预测 2022 年的比赛结果并将其与实际结果进行比较,采用了 "滚动窗口 "策略,对不断更新的数据集进行比较。同时,再次计算之前提到的性能指标。此外,我们还研究了非线性效应或其他特定球场和球员能力的假设是否合理。
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Modeling and prediction of tennis matches at Grand Slam tournaments
In this manuscript, different approaches for modeling and prediction of tennis matches in Grand Slam tournaments are proposed. The data used here contain information on 5,013 matches in men’s Grand Slam tournaments from the years 2011–2022. All regarded approaches are based on regression models, modeling the probability of the first-named player winning. Several potential covariates are considered including the players’ age, the ATP ranking and points, odds, elo rating as well as two additional age variables, which take into account that the optimal age of a tennis player is between 28 and 32 years. We compare the different regression model approaches with respect to three performance measures, namely classification rate, predictive Bernoulli likelihood, and Brier score in a 43-fold cross-validation-type approach for the matches of the years 2011 to 2021. The top five optimal models with highest average ranks are then selected. In order to predict and compare the results of the tournaments in 2022 with the actual results, a comparison over a continuously updating data set via a “rolling window” strategy is used. Also, again the previously mentioned performance measures are calculated. Additionally, we examine whether the assumption of non-linear effects or additional court- and player-specific abilities is reasonable.
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