Talent
Background to the Research Field
Youth academies aim to facilitate the transition of the most promising players into professional teams. This process involves three interconnected areas: talent identification, which focuses on discovering players with the potential to be developed; talent selection, where decisions are made within high-performance academies about which players progress into the next age group; and talent development, where systematic training and support, help athletes improve continuously all aspects related to the sport. This structured approach is designed to ensure that young talents can successfully transition within the academy setting and eventually into professional careers.
To address the complexity of talent identification, selection and development within high-performance academies, a multidisciplinary and longitudinal approach is essential. This enables practitioners and researchers to examine factors that influence the development across multiple domains. It is important to consider a wide range of physical, physiological, psychological, sport-specific, health-related, as well as growth and maturation characteristics to ensure a holistic perspective. Advanced statistical methods, including machine learning, facilitate the identification to patterns within complex datasets and develop models that provide valuable insights. By generating this scientific evidence, research provides clues in making more informed decisions throughout the talent pathway, from initial identification to selection and into professional football.
Projects within the Research Field
Within this research area, retrospective longitudinal data that are routinely collected within youth academies serve as the foundation for scientific investigations. These data are gathered from multiple domains: physical qualities such as linear sprinting and change-of-direction performance, sport-specific skills, psychological characteristics including executive functions and stress profiles, growth and maturation measures such as standing height, body mass, and biological maturation, as well as health-related aspects like injury and illness.
One central project within this research area investigated which factors contribute to talent selection in elite youth football. Using machine learning algorithms, it is possible to identify which parameters are most relevant for selection decisions across different age groups. Findings indicate that physical and physiological qualities, such as linear sprint, as well as sport-specific skills show the highest importance for selection processes. Psychological characteristics, including motivational profiles and cognitive flexibility, contribute to a substantial extent as well. This approach offers a promising complement to subjective evaluation in decision-making processes and provides key stakeholders with insights on which parameters might be worthwhile monitoring and emphasising as part of the overall talent selection processes. Ultimately, this line of research aims to understand which combination of factors facilitates the transition from youth to professional football.
While these findings provide valuable insights into selection processes, understanding how talent unfolds over time requires a different approach. To disentangle the most relevant aspects of talent development within youth sports, longitudinal prospective studies investigating the developmental pathway of youth athletes are essential. Such studies allow for tracking how characteristics develop and interact over time, capturing the dynamic nature of talent selection and development. By following young athletes across critical developmental phases, it becomes possible to identify not only which factors matter, but also when they matter most. This approach enables a broader understanding of the factors that shape young athletes' pathways to elite performance across different sports, ultimately informing evidence-based talent identification, selection and development strategies.
