融合赛前与实时数据，提升网球比赛胜率预测精度。
Forecasting the Winner of a Live Tennis Match
- 采用混合模型整合赛前与实时比赛数据。
- 在比赛进行25%、50%、75%时准确率分别达76.06%、82.15%、88.34%。
- 适合体育博彩、赛事分析及实时决策系统使用。
近年来，随着实时体育博彩的兴起，网球比赛预测已从赛前预测扩展到动态更新比赛胜率。本文研究如何有效整合赛前与实时信息以生成精准的胜率估计。基于包含1,505,355个得分的8,222场大满贯比赛数据，采用时间顺序划分：2011-2021年训练，2022年验证，2023-2024年测试。五种模型被评估，其中混合模型Trace在比赛进行25%、50%、75%时的准确率分别为76.06%、82.15%和88.34%，表明混合建模是实现实时网球预测的有效方法。
原文摘要 · Abstract (English)
With the rise of live sports betting in recent years, tennis forecasting has expanded from pre-match prediction to models that update win probabilities as a match unfolds. A central challenge in creating such a model is the constant need for models to adapt to score and performance changes. This study examines how pre-match and live information can be most effectively integrated into a model to produce accurate win-probability estimates. The analysis uses 8,222 Grand Slam matches containing a total of 1,505,355 points. Five models were evaluated using a chronological split, with matches from 2011-2021 used for training, 2022 for validation, and 2023-2024 for testing. Trace, a hybrid model, achieved accuracies of 76.06%, 82.15%, and 88.34% at 25%, 50%, and 75% match progress, suggesting that hybrid modeling is a practical approach to live tennis forecasting.
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