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.
© Copyright 2023 European Journal of Sport Science. Wiley. All rights reserved.
| Subjects: | |
|---|---|
| Notations: | sport games |
| Tagging: | maschinelles Lernen |
| Published in: | European Journal of Sport Science |
| Language: | English |
| Published: |
2023
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| Online Access: | https://doi.org/10.1080/17461391.2021.2006800 |
| Volume: | 23 |
| Issue: | 1 |
| Pages: | 44-53 |
| Document types: | article |
| Level: | advanced |