ArticleBMC sports science, medicine & rehabilitation2025
Deep learning-based tennis match type clustering.
Article in BMC sports science, medicine & rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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3 authors.
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Abstract
backgroundThis study aims to define and cluster tennis match types based on how they are played.
methodsThe research data selected for this study were from the 100th round of 32 matches of the five finals of the 2023 International Tennis Open Tournament. Based on expert knowledge and sports expertise, 27 variables were included across seven areas. Three models were applied and the silhouette coefficient was calculated to identify the optimal number of clusters. A difference test was conducted on the game record variables based on the cluster results.
resultsCalculation of the silhouette coefficients for the three models showed that Model 3 (silhouette coefficient: 0.406) had the highest performance. The clustering results for the tennis match types are as follows. First, the NEt Rusher Defensive type, which is defensive and induces net play. Second, the ALl Courter Defensive type, which is either defensive or all-round. Third, the STroke Placement Offensive type, which is aggressive and has strengths in stroke. Fourth, the SErve Placement Offensive type, which is aggressive and has strengths in sub courses.
conclusionThis study's findings are not only provide basic data to cluster game types in tennis matches but also to contribute to establishing game strategies for each game type, thereby further improving performance.
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