Enhancing Predictive Accuracy in Tennis: Integrating Fuzzy Logic and CV-GRNN for Dynamic Match Outcome and Player Momentum Analysis

2025-03-25

AI Summary

To address the insufficient accuracy in predicting match outcomes and player momentum dynamics in professional tennis, this paper proposes a joint predictive framework integrating a multilayer fuzzy evaluation model with a cross-validated gated recurrent neural network (CV-GRNN). Methodologically, we design a two-tier fuzzy system to explicitly characterize momentum transitions; identify 15 statistically significant momentum indicators—including win-rate consistency and score differential—via Pearson correlation analysis; and embed them end-to-end into the CV-GRNN architecture for the first time. Dimensionality reduction via PCA and fuzzy logic integration further enhance model interpretability. Evaluated on the Wimbledon dataset, our framework achieves an 86.64% prediction accuracy for match outcomes and reduces mean squared error by 49.21% compared to baselines. The approach significantly improves the stability, accuracy, and practical utility of dual prediction—both match results and real-time momentum dynamics—in elite tennis contexts.

Technology Category

• Machine Learning: Evaluation and Analysis
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Models for Web evolution

Abstract

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.

Problem

• Enhancing tennis match prediction accuracy using fuzzy logic and CV-GRNN
• Analyzing player momentum via statistical indicators like win streaks
• Improving predictive models for dynamic athletic performance outcomes

Innovation

• Combining fuzzy logic with CV-GRNN model
• Using Principal Component Analysis for indicators
• Refining CV-GRNN with 15 key indicators