预测现场网球比赛的胜者
Forecasting the Winner of a Live Tennis Match
- Natick High School（纳蒂克高中）
- University of Oxford（牛津大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出混合模型Trace，整合赛前与现场信息预测网球比赛胜者，在8,222场大满贯比赛中于25%、50%、75%比赛进度时准确率达76.06%、82.15%、88.34%。
AI中文摘要：
随着近年来现场体育博彩的兴起，网球预测已从赛前预测扩展到随着比赛进行而更新获胜概率的模型。创建此类模型的一个核心挑战是模型需要不断适应比分和表现的变化。本研究探讨了如何最有效地将赛前信息和现场信息整合到模型中，以产生准确的获胜概率估计。该分析使用了8,222场大满贯比赛，共包含1,505,355分。使用时间顺序划分对五种模型进行了评估，其中2011-2021年的比赛用于训练，2022年用于验证，2023-2024年用于测试。混合模型Trace在比赛进行到25%、50%和75%时分别达到了76.06%、82.15%和88.34%的准确率，这表明混合建模是现场网球预测的一种实用方法。
英文摘要：
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.
