When identifying talent, the confounding influence of maturity status on motor performances is an acknowledged problem. To solve this problem, correction mechanisms have been proposed to transform maturity-biased test scores into maturity-unbiased ones. Whether or not such corrections also improve predictive validity remains unclear. To address this question, we calculated correlations between maturity indicators and motor performance variables among a sample of 121 fifteen-year-old elite youth football players in Switzerland. We corrected motor performance scores identified as maturity-biased, and we assessed correction procedure efficacy. Subsequently, we examined whether corrected scores better predicted levels of performance achievement 6 years after data collection (47 professionals vs. 74 non-professional players) compared with raw scores using point biserial correlations, binary logistic regression models, and DeLong tests. Expectedly, maturity indicators correlated with raw scores (0.16 ≤ | r | ≤ 0.72; ps < 0.05), yet not with corrected scores. Contrary to expectations, corrected scores were not associated with an additional predictive benefit (univariate: no significant r-change; multivariate: 0.02 ≤ ΔAUC ≤ 0.03, ps > 0.05). We do not interpret raw and corrected score equivalent predictions as a sign of correction mechanism futility (more work for the same output); rather we view them as an invitation to take corrected scores seriously into account (same output, one fewer problem) and to revise correction-related expectations according to initial predictive validity of motor variables, validity of maturity indicators, initial maturity-bias, and selection systems. Recommending maturity-based corrections is legitimate, yet currently based on theoretical rather than empirical (predictive) arguments.

• In summative approaches to identify talent, correction mechanisms are needed, and they can be successfully implemented. In this study, however, they could not improve predictions of future performance level (compared with raw scores).

• We do not interpret raw and corrected score equivalent predictions as a sign of correction mechanism futility (more work for the same output), instead we see them as an invitation to take corrected scores seriously into account (same output, one fewer problem).

• Expectations related to corrected scores must be revised according to four factors: initial predictive validity; initial maturity bias of considered variables; validity of the maturity indicator; and current selection system.

• The added value of corrected scores for talent identification and development, such as personality development, environmental support, performance profiling or monitoring, currently resides on rather theoretical grounds.