Ranking and prediction for Cycling Canada

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.