用模糊逻辑与神经网络结合，提升网球比赛结果与选手状态预测准确率。
Enhancing Predictive Accuracy in Tennis: Integrating Fuzzy Logic and CV-GRNN for Dynamic Match Outcome and Player Momentum Analysis
- 融合主成分分析与两级模糊模型，识别关键比赛指标。
- 引入15个显著指标后，预测准确率达86.64%，均方误差降低49.21%。
- 适合体育数据分析、智能裁判系统及竞技策略研究者参考。
网球比赛结果与选手状态的预测长期存在挑战。本文提出一种新方法，将多层级模糊评估模型与基于交叉验证的广义回归神经网络（CV-GRNN）结合。通过主成分分析识别关键统计指标，并基于温布尔登数据构建两层模糊模型。皮尔逊相关系数分析显示，如选手连胜场次与比分差等状态指标间存在强相关性，揭示了选手在输赢交替中的动态趋势。进一步将15个统计显著指标融入CV-GRNN模型，使预测准确率提升至86.64%，均方误差下降49.21%。该方法强化了网球比赛预测的理论框架，具备实际应用价值，且可推广至其他体育领域。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。