2025/03/25 by K. Li, Li, Kechen, Jiaming Liu +5
Economics, Econometrics and Finance · Mathematics · Medicine · #68T07 #Applications (stat.AP) #FOS: Computer and information sciences #I.2.6 #Machine Learning (cs.LG) #Sports Analytics and Performance #Sports Performance and Training #Statistical Methods in Epidemiology
paper · pdf · doi:10.48550/arxiv.2503.21809
openalex publication_date 2025/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The predictive analysis of match outcomes and player momentum in professional tennis has long been a subject of scholarly debate. In this paper, we introduce a novel approach to game prediction by combining a multi-level fuzzy evaluation model with a CV-GRNN model. We first identify critical statistical indicators via Principal Component Analysis and then develop a two-tier fuzzy model based on the Wimbledon data. In addition, the results of Pearson Correlation Coefficient indicate that the momentum indicators, such as Player Win Streak and Score Difference, have a strong correlation among them, revealing insightful trends among players transitioning between losing and winning streaks. Subsequently, we refine the CV-GRNN model by incorporating 15 statistically significant indicators, resulting in an increase in accuracy to 86.64% and a decrease in MSE by 49.21%. This consequently strengthens the methodological framework for predicting tennis match outcomes, emphasizing its practical utility and potential for adaptation in various athletic contexts.