Data-Driven Tennis Strategy Evaluation through Hierarchical Markov Models
Abstract
1 Introduction
2 Related Works
3 Experimental Preparation
Symbols | Definition |
---|---|
Sk | Player k's score in set |
gk | Player k's score in game |
pk | Player k's score in points |
Fij | Probability that player i serves to win |
Pm | Probability of winning a match |
4 Tracking the scoring process of a game
4.1 Markov Chain Model
4.2 Hierarchical Markov Model
4.2.1 Modeling games
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4.2.2 Modeling sets
4.2.3 Modeling a tiebreak game
4.2.4 Modeling a best-of-three match
4.2.5 The probability equation for winning
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4.3 Identification of Momentum-Related Factors
4.4 Predicting Momentum Transitions Using LSTM
4.5 Strategic Implications for Coaches
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5 RESULTS and DISCUSSIONs
5.1 Model Evaluation and Generalization
Player | R2 | RMSE | Player | R2 | RMSE |
---|---|---|---|---|---|
HC | 0.9506 | 0.5642 | EF1 | 0.5236 | 2.1265 |
CC | 0.9511 | 0.5267 | EF2 | 0.5019 | 2.5611 |
GC | 0.9456 | 0.5549 | EF3 | 0.4925 | 3.1021 |
FT1 | 0.9219 | 0.6306 | TT1 | 0.4769 | 2.4263 |
FT2 | 0.9286 | 0.5872 | TT2 | 0.4872 | 2.5106 |
FT3 | 0.9301 | 0.6103 | TT3 | 0.4536 | 2.7126 |
5.2 Model Limitations
6 Conclusions
6.1 Strengthen
6.2 Weaknesses
6.3 Extensions
6.4 LSTM Capabilities
Acknowledgments
Footnotes
References
Index Terms
- Data-Driven Tennis Strategy Evaluation through Hierarchical Markov Models
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