Browsing by Author "Michael Jed"

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    Dynamic graph-based forecasts of bookmakers’ odds in professional tennis
    (2026-5-27) Penn Matthew J.; Michael Jed; Bhatt Samir
    Abstract 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 bookmakers’ odds for any match on any surface, allowing effective and detailed pre-tournament predictions to be made. By leveraging the high-quality information embedded in these odds, our model incorporates the most up-to-date market assessments of player ability. By analysing major tennis championships from 2024 and 2025, we show that our model achieves comparable accuracy to the bookmakers while significantly outperforming benchmark models on a range of metrics.