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A multidimensional approach to identifying the physical qualities of male English regional academy rugby union players; considerations of position, chronological age, relative age and maturation

Owen, Cameron, Till, Kevin, Phibbs, Padraic, Read, Dale ORCID logoORCID: https://orcid.org/0000-0001-6367-0261, Weakley, Jonathon, Atkinson, Mark, Cross, Matt, Kemp, Simon, Sawczuk, Tom, Stokes, Keith, Williams, Sean and Jones, Ben (2022) A multidimensional approach to identifying the physical qualities of male English regional academy rugby union players; considerations of position, chronological age, relative age and maturation. European Journal of Sport Science. ISSN 1536-7290

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Abstract

Interpreting the physical qualities of youth athletes is complex due to the effects of growth, maturation and development. This study aimed to evaluate the effect of position, chronological age, relative age and maturation on the physical qualities of elite male academy rugby union players. 1,424 participants (n = 2,381 observations) from nine Rugby Football Union Regional Academies prospectively completed a physical testing battery at three time points, across three playing seasons. Anthropometrics, body composition, muscular power, muscular strength, speed, aerobic capacity and running momentum were assessed. Positional differences were identified for all physical qualities. The largest effect sizes were observed for the associations between chronological age (d = 0.65 to 0.73) and maturation (d=-0.77 to -0.69) and body mass related variables (i.e. body mass and running momentum). Relative strength, maximum velocity and aerobic capacity were the only models to include two fixed effects with all other models including at least three fixed effects (i.e. position and a combination of chronological age, relative age and maturation). These findings suggest a multidimensional approach considering position, chronological age, relative age and maturation is required to effectively assess the physical qualities of male age grade rugby union players. Therefore practitioners should use regression equations rather than traditional descriptive statistic tables to provide individualised normative comparisons thus enhancing the application of testing results for talent identification and player development.

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