Differentiating movement styles in professional tennis: A machine learning and hierarchical clustering approach

Purpose: Recent explorations of tennis-specific movements have developed contemporary methods for identifying and classifying changes of direction (COD) during match-play. The aim of this research was to employ these new analysis techniques to objectively explore individual nuance and style factors in the execution of COD movements in professional tennis. Methods: Player tracking data from 62 male and 77 female players at the Australian Open Grand Slam were analysed for COD movements using a model algorithm, with a sample of 150,000 direction changes identified. Hierarchical clustering methods were employed on the time-motion and degree characteristics of these direction changes to identify groups of different COD performers. Results: Five unique clusters, labelled Cutters, Gear Changers, Lateral Changers, Balanced Changers and Passive Changers were identified in accordance with their varying speed, acceleration, degree and directionality of change features. Conclusions: Player COD clustering challenge previously held assumptions regarding on-court movement style, highlighting the complexity and variation in the sport's locomotion demands. In practice, the speed, acceleration, directionality and degree of change characteristics of each COD style can facilitate athlete profiling and the specificity of training interventions.

Automated summary

This research used new analysis techniques to objectively explore the execution of COD movements in professional tennis and identified five unique clusters of COD performers. These findings challenge previous assumptions about on-court movement style and highlight the complexity and variation in the sport's locomotion demands. The characteristics of each COD style can facilitate athlete profiling and specific training interventions.