In efforts to improve Canadian performance in the men's Elite UCI Mountain Bike World Cup, researchers from the Canadian Sport Institute Ontario (CSIO) presented to us a specific problem. They had a wealth of race data but were unsure how to best extract insights from the dataset. We responded to their request by building an interactive user interface with R Shiny to obtain rider rankings. Estimation was carried out via maximum likelihood using the Bradley-Terry model. We improved on the existing literature, proposed an exponentially weighted version of the model, and determined an optimal weighting parameter through cross-validation involving performance of future races. Therefore, the proposed methods provide forecasting capability. The tuned Bradley-Terry estimation performed better than the UCI point-based ranking in terms of predictive error. This implementation of the Bradley-Terry model with a user-friendly graphical interface provides broader scientific audiences easy access to Bradley-Terry ranking for prediction in racing sports.
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