Many data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as Functional Principal Component Analysis (FPCA) or functional clustering algorithms are presented and compared on simulated data. Finally, the problem of the detection of promising young swimmers is addressed through a curve clustering procedure on a real data set of performance progression curves. This study reveals that the fastest improvement of young swimmers generally appears before 16 years old. Moreover, several patterns of improvement are identified and the functional clustering procedure provides a useful detection tool.

Maximal physical performances are powerful and accurate biomarkers in the understanding of age-related changes during the aging process. Previous studies have characterized age-related changes from Caenorhabditis elegans to Homo sapiens. We characterized changes in this pattern for H. sapiens, decade by decade, from 1970 to 2017. Using 286,916 performances related to age from the world's best performances in each age group, we measured the relative change of 10 different running and jumping events for both women and men. We compared the change in sexual dimorphism with age and showed that the gender gap in maximal performance regarding age increases gradually, especially after the age of 50. Between 1970 and 2017, the performances for all age groups in all events have slightly progressed. However, during the last decades, the relative progression of the best performances for all age groups has decreased in both range and frequency, suggesting that age-related maximal physical performances for H. sapiens are reaching their physiological limits.