<ul><li>A new approach to game prediction in professional tennis is introduced, combining a multi-level fuzzy evaluation model with a CV-GRNN model.</li><li>Critical statistical indicators are identified using Principal Component Analysis and a two-tier fuzzy model is developed based on Wimbledon data.</li><li>The study reveals strong correlations among momentum indicators, such as Player Win Streak and Score Difference, providing insights into players transitioning between losing and winning streaks.</li><li>By incorporating 15 statistically significant indicators in the CV-GRNN model, the accuracy increases to 86.64% and the mean squared error decreases by 49.21%.</li></ul>

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

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