Differentiating movement styles in professional tennis: A machine learning and hierarchical clustering approach - Giles, B.; Peeling, P.; Kovalchik, S.; Reid, M. (European Journal of Sport Science, 2023)
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.