Prediction of talent selection in elite male youth soccer across 7 seasons: A machine-learning approach.

Abstract

This study aimed to investigate the relative importance of parameters from several domains associated to both selecting or de-selecting players with regards to the next age group within a professional German youth soccer academy across a 7-year period. Following a mixed-longitudinal approach, physical, physiological, psychological, skill-, health-, age-, and position-related parameters were collected from 409 male players (980 datapoints) from the U12 to U19 age groups. Supervised machine learning classifiers were used to predict the selection status regarding the next age group. The XGBoost models (ROC-AUC: 0.69, F1-Score: 0.84) revealed that physical and physiological (linear sprint, change-of-direction sprint, countermovement jump, aerobic speed reserve) as well as skill-related parameters (soccer-specific skill) were most important for being selected or de-selected regarding the next age group across the entire sample and all age groups. The majority of psychological parameters (motive structure, motive attention, motive competition, cognitive flexibility) were of medium importance. No clear pattern was observed for the health-, age-, and position-related parameters. Our study provides insights into key parameters for talent selection thereby contributing to an overall talent management strategy in highly trained youth soccer players. In particular, coaches and key stakeholders might focus on physical, physiological, and skill-related parameters.