Forecasting the Winner of a Live Tennis Match

• URL: http://arxiv.org/abs/2609.07617v1
• Date: Mon, 07 Sep 2026 15:21:58 GMT
• Title: Forecasting the Winner of a Live Tennis Match
• Abstract summary: This study examines how pre-match and live information can be most effectively integrated into a model to produce accurate win-probability estimates.<n>The analysis uses 8,222 Grand Slam matches containing a total of 1,505,355 points.
• Score: 2.9832083520093353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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