Research on factors influencing tennis winning probability using a LSTM model

Chunxi Su; Yuxuan Wang

doi:10.1117/12.3054346

3 January 2025 Research on factors influencing tennis winning probability using a LSTM model

Chunxi Su, Yuxuan Wang

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 134422F (2025) https://doi.org/10.1117/12.3054346
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

In our paper, we developed a Long Short-Term Memory Model to explore the role of momentum in tennis performance, particularly within the context of the 2023 Wimbledon Championships. The Long Short-Term Memory Model describes changes in on-court situations through the difference in performance between the two sides, focusing on predicting changes in tennis matches. The model begins with a correlation analysis to identify effective indicators, then introduces a new dimension called "state" and employs a state-corrected LSTM network. It enhances the understanding of match dynamics and aids in more accurate player performance predictions. Based on this, the model further predicts the scoring rate for both sides and offers player suggestions. Our model accuracy up to 69.654%, which makes a more accurate predictions about the winning rate of the game than previous model (The Support Vector Machine model). Through the model, our thesis presents a multifaceted view of momentum’s role in tennis, offering strategies for improving player performance and decision-making. The evaluation of these models demonstrates their practical applicability and effectiveness in a competitive sports environment.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chunxi Su and Yuxuan Wang "Research on factors influencing tennis winning probability using a LSTM model", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 134422F (3 January 2025); https://doi.org/10.1117/12.3054346

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