Abstract Individual tennis points evolve over time and space, as each of the two opposing players are constantly reacting and positioning themselves in response to strikes of the ball. However, these reactions are diminished into simple tally statistics such as the amount of winners or unforced errors a player has. In this paper, a new way is proposed to evaluate how an individual tennis point is evolving, by measuring how many points a player can expect from each shot, given who struck the shot and where both players are located. This measurement, named “Expected Shot Value” (ESV), derives from stochastically modeling each shot of individual tennis points. The modeling will take place on multiple resolutions, differentiating between the continuous player movement and discrete events such as strikes occurring and duration of shots ending. Multi-resolution stochastic modeling allows for the incorporation of information-rich spatiotemporal player-tracking data, while allowing for computational tractability on large amounts of data. In addition to estimating ESV, this methodology will be able to identify the strengths and weaknesses of specific players, which will have the ability to guide a player’s in-match strategy.

中文翻译：

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单个网球点的逐次随机建模

摘要网球的各个点随着时间和空间的变化而发展，因为两个对立的球员中的每一个都不断对球的打击做出反应和定位。但是，这些反应被简化为简单的统计数据，例如获胜者的数量或玩家所犯的错误。在本文中，提出了一种新的方式来评估单个网球点的变化情况，方法是：根据击球的人和两名球员的位置，测量运动员每次击球可期望获得的得分。这种测量称为“预期击球值”（ESV），是通过随机模拟单个网球点的每次击球而得出的。建模将在多种分辨率下进行，以区分玩家的连续运动和离散事件，例如罢工发生和投篮持续时间。多分辨率随机建模允许合并信息丰富的时空播放器跟踪数据，同时允许对大量数据进行计算可处理性。除了估算ESV之外，这种方法还可以识别特定玩家的优势和劣势，从而可以指导玩家的比赛策略。