Differentiating movement styles in professional tennis: A machine learning and hierarchical clustering approach
(Differenzierung von Bewegungsstilen im professionellen Tennis: Ein Ansatz für maschinelles Lernen und hierarchisches Clustering)
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. Taylor & Francis. Alle Rechte vorbehalten.
Schlagworte: | Tennis Leistungssport Bewegung männlich weiblich Analyse Untersuchungsmethode Technologie Richtungswechsel Bewegungshandlung |
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Notationen: | Spielsportarten |
Tagging: | maschinelles Lernen |
DOI: | 10.1080/17461391.2021.2006800 |
Veröffentlicht in: | European Journal of Sport Science |
Veröffentlicht: |
2023
|
Jahrgang: | 23 |
Heft: | 1 |
Seiten: | 44-53 |
Dokumentenarten: | Artikel |
Sprache: | Englisch |
Level: | hoch |