Deep learning-based tennis match type clustering
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View abstract on PubMed
Summary
This summary is machine-generated.This study identified four distinct tennis match types: Net Rusher Defensive, All Courter Defensive, Stroke Placement Offensive, and Serve Placement Offensive. These classifications aid in developing targeted game strategies for improved player performance.
Area Of Science
- Sports Science
- Performance Analysis
- Tennis Analytics
Background
- Understanding diverse tennis match dynamics is crucial for strategic development.
- Previous research has lacked a systematic classification of playing styles.
Purpose Of The Study
- To define and cluster distinct tennis match types based on playing characteristics.
- To provide a data-driven framework for categorizing playing styles in professional tennis.
Main Methods
- Analysis of 32 matches from the 2023 International Tennis Open Tournament finals.
- Inclusion of 27 variables across seven domains, informed by expert knowledge.
- Application of three clustering models, with silhouette coefficient used for optimal cluster identification.
Main Results
- Model 3 demonstrated the highest performance with a silhouette coefficient of 0.406.
- Four distinct tennis match types were identified: NEt Rusher Defensive, ALl Courter Defensive, STroke Placement Offensive, and SErve Placement Offensive.
- Significant differences were observed in game record variables across the identified clusters.
Conclusions
- The study provides foundational data for classifying tennis match types.
- Findings can inform the establishment of tailored game strategies for each identified type.
- This classification has the potential to enhance player performance through strategic optimization.
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