A Quantitative Analysis of the Effect of Pitch Sports on Players' Potential Energy

Weiyu Tang; Xuanzeng Yi; Huimin Zhao

doi:10.62051/p2075132

Authors

Weiyu Tang
Xuanzeng Yi
Huimin Zhao

DOI:

https://doi.org/10.62051/p2075132

Keywords:

Momentum; Prediction Model; XGBOOST; Dragonfly algorithm; Genetic algorithms.

Abstract

This paper introduces a research framework for sports science, particularly in competitive tennis, focusing on momentum as a crucial factor impacting match outcomes. It addresses controversies surrounding momentum's definition, quantification methods, and its influence on results, highlighting the lack of research on momentum strategies. The study proposes a paradigm for calculating potential energy and employs the XGBOOST model optimized with DA to predict players' potential energy values with high accuracy (R2=0.9836). Through SHAP analysis, it delves into potential energy fluctuations, identifying factors such as continuous court running, winning shot execution frequency, stroke distance, intensity, and ball speed as significant influencers, varying among players. Furthermore, the paper utilizes GA for potential energy optimization, offering tailored training strategies. It concludes by discussing the model's universality, emphasizing its research significance. This framework aids in pinpointing match shortcomings in individual players and devising personalized training plans accordingly.

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References

[1] Ramsay, A. (2023, July 16). Alcaraz Ends the Djokovic Run. Wimbledon. https://www.wimbledon.com/en_GB/news/articles/2023-07-16/alcaraz_ends_the_djokovic_run.html.

[2] Dietl, H. M., Nesseler, C. (2017). Momentum in Tennis: Controlling the Match. International Journal of Sport Psychology, 48 (4), 459 – 471. https://doi.org/10.7352/IJSP.2017.48.459.

[3] Ji, S. W., Wang, X. J., et al. (2019). An Application of a Three-Stage XGBoost-Based Model to Sales Forecasting of a Cross-Border E-Commerce Enterprise. Mathematical Problems in Engineering, 2019. https: //doi.org/10.1155/2019/8503252.

[4] Gu, Z. Y., Cao, M. C. (2022). Research on Mining Maximum Subsidence Prediction Based on Genetic Algorithm Combined with XGBoost Model. Sustainability, 14 (16). https://doi.org/10.3390/su141610421.

[5] Mirjalili, S. (2016). Dragonfly Algorithm: A New Meta-Heuristic Optimization Technique for Solving Single-Objective, Discrete, and Multi-Objective Problems. Neural Computing & Applications, 27 (4), 1053 – 1073. https: //doi.org/10.1007/s00521 - 015 - 1920 - 1.

[6] Wang, J. Y., Li, J. R., Li, Z. W. (2022). Prediction of Air Pollution Interval Based on Data Preprocessing and Multi-Objective Dragonfly Optimization Algorithm. Frontiers in Ecology and Evolution, 10. https://doi.org/10.3389/fevo.2022.855606.

[7] Nordin, N., Zainol, Z., et al. (2022). An Explainable Predictive Model for Suicide Attempt Risk Using an Ensemble Learning and Shapley Additive Explanations (SHAP) Approach. Asian Journal of Psychiatry, 79. https://doi.org/10.1016/j.ajp.2022.103316.

[8] Ziakopoulos, A., Kontaxi, A., & Yannis, G. (2023). Analysis of Mobile Phone Use Engagement during Naturalistic Driving through Explainable Imbalanced Machine Learning. Accident Analysis and Prevention, 181. https://doi.org/10.1016/j.aap.2022.106936.

[9] Chen, C. M., Zhang, S. G., et al. (2021). Personalized Travel Route Recommendation Algorithm Based on Improved Genetic Algorithm. Journal of Intelligent & Fuzzy Systems, 40(3), 4407 – 4423. https://doi.org/10.3233/JIFS-201218.

[10] Ren, M. L., & Zhang, Q., et al. (2022). Research and Application of Reservoir Flood Control Optimal Operation Based on Improved Genetic Algorithm. Water, 14 (8). https://doi.org/10.3390/w14081272.

[11] Li, H., Shi, N. Y. (2022). Application of Genetic Optimization Algorithm in Financial Portfolio Problem. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2022/5246309.

[12] Problem Title: Momentum in Tennis. (2024, February 1). Mathmodels. https://www.mathmodels.org/Problems/2024/MCM-C/index.html.