A Tennis Player’s Momentum Assessment Based on Game Theory and Projection Pursuit Evaluation Model

DOI

10.2991/978-94-6463-632-1_17How to use a DOI?

Keywords

Tennis Player; Momentum; Portfolio Assessment Model; Game Theory; Projection Pursuit Evaluation Model

Abstract

This paper comprehensively evaluates the athletic motivation of tennis players through the game theory combination weighting model and the simulated annealing optimization projection pursuit evaluation model, aiming to uncover the key factors affecting athletes’ performance. The research results indicate that the main factors influencing athletes’ motivation are “Type of return” (weight 24.12%). A specific analysis reveals that Carlos Alcaraz outperformed Novak Djokovic in the 30–34 games, especially showcasing his best form in the 3rd game. The study shows that the smaller the momentum difference of the serving side, the higher the likelihood of winning the match. Although an athlete may dominate the match, the adaptability and endurance of the opponent are equally crucial, emphasizing the importance of match strategy. These findings provide theoretical support and practical guidance for the optimization of training and match strategies for tennis players.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Xuening Kong
AU  - Xiyu Liu
AU  - Yongqiang Yin
PY  - 2024
DA  - 2024/12/26
TI  - A Tennis Player’s Momentum Assessment Based on Game Theory and Projection Pursuit Evaluation Model
BT  - Proceedings of the 2024 4th International Conference on Business Administration and Data Science (BADS 2024)
PB  - Atlantis Press
SP  - 166
EP  - 172
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-632-1_17
DO  - 10.2991/978-94-6463-632-1_17
ID  - Kong2024
ER  -