The analysis of serve decisions in tennis using Bayesian hierarchical models

Peter Tea and Tim B. Swartz ()
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Peter Tea: Simon Fraser University
Tim B. Swartz: Simon Fraser University

Annals of Operations Research, 2023, vol. 325, issue 1, No 27, 633-648

Abstract: Abstract Anticipating an opponent’s serve is a salient skill in tennis: a skill that undoubtedly requires hours of deliberate study to properly hone. Awareness of one’s own serve tendencies is equally as important, and helps maintain unpredictable serve patterns that keep the returner unbalanced. This paper investigates intended serve direction with Bayesian hierarchical models applied on an extensive, and now publicly available data source of professional tennis players at Roland Garros. We find discernible differences between men’s and women’s tennis, and between individual players. General serve tendencies such as the preference of serving towards the Body on second serve and on high pressure points are revealed.

Keywords: Bayesian multinomial logistic regression; Ball tracking data; Roland Garros; Roger Federer (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-021-04481-7

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