Maturity-based correction mechanism for talent identification: When is it needed, does it work, and does it help to better predict who will make it to the pros?

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.

Science or Coaches' Eye? - Both! Beneficial Collaboration of Multidimensional Measurements and Coach Assessments for Efficient Talent Selection in Elite Youth Football.

Due to the tremendous popularity of youth football, practitioners in this domain face the ongoing question of the most effective solutions in early talent selection. Although the scientific community has suggested multidimensional models for some time, coach assessments and motor performance tests remain common. Earlier research has determined the strengths and weaknesses within these different approaches. The current investigation directly compared the effectiveness of each approach in talent selection (coach assessment vs. motor performance tests vs. multidimensional data). A sample of 117 youth football players, their parents, and coaches participated in multidimensional measurements in the U14 age category (coach assessments, motor performance tests, psychological characteristics, familial support, training history, and biological maturation). The area under the curve (AUC [95% CI]) from receiver operating characteristic indicated the prognostic validity of each approach in predicting U19 player status five years after the assessments (professional vs. non-professional). Motor performance tests (0.71 [0.58; 0.84]) showed a lower AUC than the multidimensional data (0.85 [0.76; 0.94], p = 0.02), whilst coach assessments did not differ from the two others (.82 [.74; .90]). Further, combined talent selection approaches, especially the use of coach assessments and multidimensional data together, were significantly better at predicting U19 player status (0.93 [0.87; 0.98], p = 0.02 vs. multidimensional data only). Although certain limitations may impede further insights (summation of data, skipped use of non-linear statistics), scientific claims for using multidimensionality within talent selection were confirmed to be fruitful. In particular, the combination of the subjective coaches' eye with scientific data may buffer the mutual weaknesses of these different approaches. Future research should focus on optimizing the output of promising multidimensional models. Knowledge of detailed values relating to specific dimensions within these models and the implementation of enhanced non-linear statistics may enable further improvements in the field of talent selection.

"The Early Specialised Bird Catches the Worm!" - A Specialised Sampling Model in the Development of Football Talents.

Characteristics of learning activities in early sport participation play a key role in the development of the sporting talent. Therefore, pathways of specialisation or diversification/sampling are as well debated as the implementation of practice- or play-oriented activities. The related issues are currently perceived as a two-dimensional construct of domain specificity and performance orientation. In this context, it has been shown that early specialisation, with experiences in practice and play, has led to Swiss junior national team football players reaching higher success levels as adults. This study aimed to examine whether a similar approach improves chances of even being selected for junior national teams from a broader sample. Hence, 294 youth players answered retrospective questionnaires on their early sport participation when entering the Swiss football talent development programme. Using the person-oriented Linking of Clusters after removal of a Residue (LICUR) method, volumes of in-club practice, free play and activities besides football until 12 years of age were analysed along with age at initial club participation. According to the results, clusters of Football enthusiasts (p = 0.01) with the most free play and above average in-club practice and Club players (p = 0.02) with the most in-club practice and average free play had a greater chance of reaching junior national team level. Thus, high levels of domain-specific activities seem to increase the chances of junior national team participation. Furthermore, the most successful constellation (Football enthusiasts) may illustrate the relevance of domain-specific diversity, induced by several types of practice and play. In line with previous studies, specialising in football and sampling different experiences within this specific domain seems to be the most promising pathway. Therefore, we argue that the optimal model for the development of football talents is a specialised sampling model.