In modern tennis, data analytics and artificial intelligence are key for predicting crucial match turning points and forming strategies. This study enhances the understanding of tennis momentum by focusing on strategic turning points and players' diverse skills. The study uses the TOPSIS method to quantify player attributes and integrate these with key momentum indicators using Principal Component Analysis and Factor Analysis. This results in nine principal components, with three particularly influential ones—emphasizing the importance of service quality, physical fitness, and mental stability in match outcomes. The model is highly effective, demonstrated by a Kendall coordination coefficient of 0.933. The study also suggests potential improvements by including factors like environmental conditions to increase model generalizability. Our findings improve insights into player performance dynamics and enhance the predictive accuracy of match results, supporting the development of sophisticated strategies for players and coaches. The research highlights the significance of advanced analytical methods in leveraging the subtle yet impactful elements of momentum in professional tennis.

Dishen Yang, Qingyang Zhang, Baihe Luan. Study on Dynamic Prediction Model for Tennis Matches Based on Multi-Dimensional Indicator Evaluation. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 8: 27-32. https://doi.org/10.25236/AJCIS.2024.070805.

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