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

Kechen Li, Jiaming Liu, Zhenyu Wu, Tianbo Ji

TL;DR

A novel approach to game prediction by combining a multi-level fuzzy evaluation model with a CV-GRNN model, which strengthens the methodological framework for predicting tennis match outcomes, emphasizing its practical utility and potential for adaptation in various athletic contexts.

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.

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

TL;DR

A novel approach to game prediction by combining a multi-level fuzzy evaluation model with a CV-GRNN model, which strengthens the methodological framework for predicting tennis match outcomes, emphasizing its practical utility and potential for adaptation in various athletic contexts.

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.

Paper Structure

This paper contains 30 sections, 12 equations, 10 figures, 9 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Method
  4. The Establishment of The FAHP Model and Solution
  5. Evaluation Index Establishment
  6. The Evaluation Model Sloving:
  7. Correlation Verification and CV-GRNN
  8. Correlation Verification
  9. Establishment and Solution of CV-GRNN Model
  10. Generalized Regression Neural Network GRNN
  11. EXPERIMENTS AND ANALYSES
  12. Data Sources
  13. Data Preprocessing
  14. Testing and Handling Missing Values
  15. Importance of Addressing Missing Values
  16. ...and 15 more sections

Figures (10)

  • Figure 1: The Overall Flow Chart of Techniques and Methods in this Paper
  • Figure 2: Schematic Diagram of Second-Level Fuzzy Comprehensive Evaluation System Model
  • Figure 3: The Membership Function Graph Plotted According to Membership Function
  • Figure 4: The Momentum of The Two Players Varies at Different Times
  • Figure 5: Generalized Regression Neural Network Model Structure
  • ...and 5 more figures