Accounting for relative age effect in sports performance assessment: Developing age-adjusted performance corridors using mixed models

This study developed a novel statistical approach to account for relative age effect (RAE) on physical performance in young populations. Data from 1715 elite youth French rugby union players (age: mean <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>=</mml:mo><mml:mn>15.9</mml:mn></mml:math>yrs, sd <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>=</mml:mo><mml:mn>1.2</mml:mn></mml:math>; height: mean <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>=</mml:mo><mml:mn>177</mml:mn></mml:math>cm, sd <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>=</mml:mo><mml:mn>9</mml:mn></mml:math>; mass: mean <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>=</mml:mo><mml:mn>79</mml:mn></mml:math>kg, sd <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>=</mml:mo><mml:mn>16</mml:mn></mml:math>) were analysed using their 50 m sprint times. Linear mixed models were employed to characterise the relationship between age and physical performance, incorporating log transformations to address non-linearity and additional parameters to refine model accuracy. Confidence intervals around predictions established performance corridors for each age, while estimated individual effects enabled the calculation of personalised progression curves and corridors. The findings provided novel insights into accounting for RAE and underscored the utility of advanced statistical methods in performance assessment. Specifically, the approach addressed non-linearity in progression with respect to both age and performance level, introduced a new performance indicator - the corridors - that integrates chronological and relative age, and provided a personalised framework for tracking athletes, progression over time.