职业网球发球接发球冲击模式的统计模型
A Statistical Model of Serve Return Impact Patterns in Professional Tennis
摘要：
跟踪系统在体育运动中的广泛使用使得细粒度时空分析成为新兴体育分析行业的主要焦点。最近公布的男子职业网球的跟踪数据允许第一个详细的空间分析返回的影响。混合模型是一个有吸引力的基于模型的体育空间分析框架，其中潜变量发现往往是主要的兴趣。虽然有限混合模型具有可解释性和可扩展性的优点，但大多数实现都假设以潜变量为条件的结果的标准参数分布。在本文中，我们提出了一个更灵活的替代方案，允许潜在的条件分布是有限高斯混合的混合成员。我们的模型的动机是我们的努力，以描述常见的风格返回的影响位置的职业网球运动员，是我们命名的方法是一个“潜在的风格分配”模型的原因。在一个完全贝叶斯实现中，我们将该模型应用于2018年至2020年期间网球职业协会赛事中141名顶级球员的142，803个回球点，并表明潜在风格分配提高了有限高斯混合模型的预测性能，并确定了第一次和第二次发球回球的六种独特影响风格。
英文摘要：
The spread in the use of tracking systems in sport has made fine-grained spatiotemporal analysis a primary focus of an emerging sports analytics industry. Recently publicized tracking data for men's professional tennis allows for the first detailed spatial analysis of return impact. Mixture models are an appealing model-based framework for spatial analysis in sport, where latent variable discovery is often of primary interest. Although finite mixture models have the advantages of interpretability and scalability, most implementations assume standard parametric distributions for outcomes conditioned on latent variables. In this paper, we present a more flexible alternative that allows the latent conditional distribution to be a mixed member of finite Gaussian mixtures. Our model was motivated by our efforts to describe common styles of return impact location of professional tennis players and is the reason we name the approach a 'latent style allocation' model. In a fully Bayesian implementation, we apply the model to 142,803 return points played by 141 top players at Association of Tennis Professional events between 2018 and 2020 and show that the latent style allocation improves predictive performance over a finite Gaussian mixture model and identifies six unique impact styles on the first and second serve return.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
