Estimating the potential of alpine skiers is an unresolved question, especially because of the complexity of sports performance. We developed a potential estimation model based solely on the evolution of performance as a function of age. A bayesian mixed model allowed to estimate the potential curve and the age at peak performance for the population (24.81 ± 0.2) and for each individual as the uncertainty around this curve. With Gaussian mixtures, we identified among all the estimates four types of curves, clustered according to the performance level and the progression per age. Relying on the uncertainty calculated on the progression curve the model created also allow to estimate a score and an uncertainty associated with each cluster for all individuals. The results allows to: i) describe and explain the relationship between age and performance in alpine skiing from a species point of view (at 0.87%) and ii) to provide to sport staffs the estimation of the potential of each individual and her/his typology of progression to better detect sports potential. The entire methodology is based on age and performance data, but the progression identified may depend on parameters specific to alpine skiing.

Background This study aimed to identify a Relative Age Effect (RAE) among French young swimmers and apply corrective adjustment procedures to rebalance performances according to categories and events. Methods 5,339,351 performances of French swimmers aged 10 to 18 were collected between 2000 and 2019. Birth quarters distribution was examined according to competitiveness level (‘All’, ‘Top50%’, ‘Top25%’ and ‘Top10%’), event and age category. A linear relationship between the distribution of performances and calendar days provides a calibration coefficient allowing to rebalance performances by considering the effect of RAE for each event. Then, adjusted performances are recalculated using this coefficient, the initial performance and the relative age. Results Proportion of swimmers born in the first quarter was higher than the proportion of those born in the last quarter for all events and strokes (p < 0.01). RAE increases with the competitiveness level for all events. Indeed, among ’All’ 12 years old 50m freestyle swimmers, the proportion born in the first quarter is 30.9% vs 19.2% in the fourth quarter, while among the “Top10%”, 47.5% were born in the first quarter vs 10.3% in the last one. (p-value < 0.01). In average, each day represents a gap of 0.008 second, resulting in a difference of almost 3 seconds over a year. This tool is validated by comparing swimmers who have performed at least twice in a season. It provides a day by day rebalancing method for all swimming events and age categories. Conclusions Relative age effect is present among French young male and female swimmers, and is strengthened by competitiveness level. A new corrective adjustment procedure to rebalance performances considering categories and events is proposed and validated. By applying such a tool, we are able to reveal the full potential of swimmers and make it possible to compare them at the same relative age.