Fitness testing is common practice within youth athletes. However, the interpretation of fitness data often occurs within chronological annual-age categories, resulting in athletes being (dis)advantaged due to age or maturity discrepancies. Instead, evaluating fitness performance against rolling averages may be more appropriate. This article presents a novel method for analyzing fitness testing data in youth athletes using z-scores according to rolling averages for both chronological age and maturity status. This analysis technique allows for the dual ability to interpret youth fitness performance according to age and maturation, enhancing accuracy of data interpretation for talent identification, development, and strength and conditioning programming.
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