PerfoRank: Cluster-Based Performance Ranking for Improved Performance Evaluation and Estimation in Professional Cycling

Abstract

Current cycling analytics solutions do not account for the race course profile or the level of the competition. Therefore, this paper develops a unique two-stage clustering-based ranking approach for rider evaluation. Initially, races are segmented into coherent clusters based upon elevation and road surface type. Subsequently, underlying skill levels are determined per cluster through the observed race results using the TrueSkill algorithm which allows to model multi-entrant competitions. The results indicate that our approach uncovers clusters which match the commonly known specializations in road cycling. The ranking methodology generates skill ratings which enable the identification of specialization and can be used in downstream tasks. Our results show that the proposed rankings drastically improve race outcome estimation when adding these rankings as features to the current prediction models.