Peter Tea
Title: The Analysis of Serve Decisions in Tennis using Bayesian Hierarchical Models
Date: July 7, 2021
Time: 3:00 pm (PDT)
Location: Remote delivery
Abstract
Anticipating an opponent鈥檚 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 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鈥檚 and women鈥檚 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.