In this study, we investigated the relationship between age and performance in professional road cycling. We considered 1864 male riders present in the yearly top 500 ranking of ProCyclingStats (PCS) since 1993 until 2021 with more than 700 PCS Points. We applied a data-driven approach for finding natural clusters of the rider's speciality (General Classification, One Day, Sprinter or All-Rounder). For each cluster, we divided the riders into the top 50% and bottom 50% based on their total number of PCS points. The athlete's yearly performance was defined as the average number of points collected per race. Age-performance models were constructed using polynomial regression and we obtained that the top 50% of the riders in each cluster have a statistically significant (p < 0.05) higher peak performance age. Considering the best 50% of the riders, general classification riders peak at an older age than the other rider types (p < 0.05). For those top riders, we found ages of peak performance of 26.3, 26.5, 26.2 and 27.5 years for sprinters, all-rounders, one day specialists and general classification riders, respectively. Our findings can be used for scouting purposes, assisting coaches in designing long-term training programmes and benchmarking the athletes' performance development.
Keywords: Performance optimization; age performance modelling; machine learning; road cycling.