Dynamic Graph-Based Forecasts of Bookmakers' Odds in Professional Tennis
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866908498712854528 |
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| author | Penn, Matthew J Michael, Jed Bhatt, Samir |
| author_facet | Penn, Matthew J Michael, Jed Bhatt, Samir |
| contents | Bookmakers' odds consistently provide one of the most accurate methods for predicting the results of professional tennis matches. However, these odds usually only become available shortly before a match takes place, limiting their usefulness as an analysis tool. To ameliorate this issue, we introduce a novel dynamic graph-based model which aims to forecast bookmaker odds for any match on any surface, allowing effective and detailed pre-tournament predictions to be made. By leveraging the high-quality information contained in the odds, our model can keep pace with new innovations in tennis modelling. By analysing major tennis championships from 2024 and 2025, we show that our model achieves comparable accuracy both to the bookmakers and other models in the literature, while significantly outperforming rankings-based predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15956 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Graph-Based Forecasts of Bookmakers' Odds in Professional Tennis Penn, Matthew J Michael, Jed Bhatt, Samir Applications Bookmakers' odds consistently provide one of the most accurate methods for predicting the results of professional tennis matches. However, these odds usually only become available shortly before a match takes place, limiting their usefulness as an analysis tool. To ameliorate this issue, we introduce a novel dynamic graph-based model which aims to forecast bookmaker odds for any match on any surface, allowing effective and detailed pre-tournament predictions to be made. By leveraging the high-quality information contained in the odds, our model can keep pace with new innovations in tennis modelling. By analysing major tennis championships from 2024 and 2025, we show that our model achieves comparable accuracy both to the bookmakers and other models in the literature, while significantly outperforming rankings-based predictions. |
| title | Dynamic Graph-Based Forecasts of Bookmakers' Odds in Professional Tennis |
| topic | Applications |
| url | https://arxiv.org/abs/2508.15956 |