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Background <p>This study aims to define and cluster tennis match types based on how they are played.</p> Methods <p>The 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.</p> Results <p>Calculation 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.</p> Conclusion <p>This 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.</p>

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Deep learning-based tennis match type clustering

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月28日

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  • Hyo-Jun Yun,
  • Nara Jang,
  • Minsoo Jeon

摘要

Background

This study aims to define and cluster tennis match types based on how they are played.

Methods

The 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.

Results

Calculation 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.

Conclusion

This 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.