Importance of fundamental movement skills to predict technical skills in youth grassroots soccer: A machine learning approach

Abstract

This study determined the contributors to soccer technical skills in grassroots youth soccer players using a machine learning approach. One hundred and sixty-two boys aged 7 to 14 (mean ± SD = 10.5 ± 2.1) years, who were regularly engaged in grassroots soccer undertook assessments of anthropometry and maturity offset (the time from age at peak height velocity (APHV)), fundamental movement skills (FMS), perceived physical competence, and physical fitness and technical soccer skill using the University of Ghent dribbling test. Coaches rated player's overall soccer skills for their age. Statistical analysis was undertaken, using machine learning models to predict technical skills from the other variables. A stepwise recursive feature elimination with a 5-fold cross-validation method was used to eliminate the worst-performing features and both L1 and L2 regularisation were evaluated during the process. Five models (linear, ridge, lasso, random forest, and boosted trees) were then used in a heuristic approach using a small subset of suitable algorithms to achieve a reasonable level of accuracy within a reasonable time frame to make predictions and compare them to a test set to understand the predictive capabilities of the models. Results from the machine learning analysis indicated that the total FMS score (0 to 50) was the most important feature in predicting technical soccer skills followed by coach rating of child skills for their age, years of playing experience and APHV. Using a random forest, technical skills could be predicted with 99% accuracy in boys who play grassroots soccer, with FMS being the most important contributor.