Forecasting the Winner of a Live Tennis Match

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.

Machine Learning (cs.LG)

arXiv:2609.07617 [cs.LG]

(or arXiv:2609.07617v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.07617 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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