“All that glitters is not gold (& vice versa)?” - How Percentile Comparison Methods (PCMs) improve youth performance evaluation

Inter-individual developmental differences compromise the capability to equitably evaluate youth sport performance. Percentile Comparison Methods (PCMs) aim to account for developmental differences, generating relative age and maturity status-specific performance percentile ranks alongside annual-age cohort ranks. This study aimed to improve PCM estimation and examined the consistency of PCM profile distributions when applied to 50-m and 200-m Front-Crawl (FC) events. Participants were <i>N</i> = 930 (50-m) and <i>N</i> = 733 (200-m FC) male swimmers, aged 11-16 years, respectively. At events, performance, background and anthropometric measures were obtained, the latter identifying maturity status. For both events, quadratic regression trendlines summarising relative age- and maturity status-performance relationships were generated, identifying trendlines and distributional performance percentile ranks. Trendline confidence intervals and maturity status estimation error were then factored into the establishment of threshold criteria to identify swimmer PCM profiles. Results identified 94% of swimmers significantly changed performance ranks when relative age and maturational differences were considered relative to normative age-group ranking. Individual-cohort PCM profiles identified five patterns of rank change, with similar prevalence across events examined. Findings highlight PCMs can help better contextualise current youth performance; inform evaluation and (de-)selection processes; address relative age and maturity-biases and provide insight toward short-term developmental trajectories.