Problem
Research questions and friction points this paper is trying to address.
tennis forecasting
live information
win-probability estimates
hybrid model
Forecasting the Winner of a Live Tennis Match
🤖 AI Summary
该研究旨在通过结合赛前和实时信息，使用包含8,222场大满贯比赛数据的五种模型来提高网球比赛中实时胜率预测的准确性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
hybrid model
live tennis forecasting
win-probability estimates
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