The analysis of serve decisions in tennis using Bayesian hierarchical models

Author: 
Date created: 
2021-07-07
Identifier: 
etd21466
Keywords: 
Bayesian analysis
Ball tracking data
Roland Garros
Roger Federer
Abstract: 

Anticipating an opponent’s serve is a salient skill in tennis: a skill that undoubtedly requires hours of deliberate practice 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 project 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 serving towards the Body on second serve and on high pressure points are revealed.

Document type: 
Graduating extended essay / Research project
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
Tim Swartz
Department: 
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.
Statistics: