The early and accurate identification of playing position-specific skills in young footballers is of critical importance for both performance development and long-term player planning. In this context, the evaluation of quantitative data obtained from technical tests using analytical methods provides a more objective approach that supports the coach’s intuition. This study aims to predict the playing positions of young footballers by using data obtained from anthropometric and technical performance tests with machine learning (ML) algorithms. This study involved 200 male footballers aged 15–17 who played in different positions (defence = 66, midfield = 67, forward = 67) and were recorded according to the primary tactical role assigned to them by the coach. The participants’ football-specific technical skills (ball control, shooting, dribbling) and anthropometric characteristics (height, weight, age, BMI) were recorded. Their technical and anthropometric characteristics were compared according to their playing positions using an ANOVA test, and the Bonferroni post-hoc test was performed to test for differences between groups. After data pre-processing and standardisation, the model created with the obtained technical and anthropometric parameters was analysed using Support Vector Machines (SVM, RBF kernel), K-Nearest Neighbour (KNN), Logistic Regression (LR) and Gaussian Naive Bayes algorithms. Model performances were compared based on accuracy, precision, sensitivity, and macro F1 scores. ROC curves and confusion matrices were analysed for the model with the highest performance. Furthermore, the technical and anthropometric parameters affecting the highest performance were analysed using the permutation importance method. The results of the one-way ANOVA showed significant differences between playing positions in terms of age, height, BMI, heading, and dribbling performance ( p < 0.05). Post-hoc analyses revealed that midfielders were older than defenders. Forwards, on the other hand, were both taller and had lower BMI values. Furthermore, forwards demonstrated higher heading performance and achieved better results in dribbling skills compared to defenders. Among the ML models, the highest classification success was achieved with the SVM (RBF kernel) model (accuracy = 86%); the model correctly classified forwards at a rate of 100%, midfielders at 85%, and defenders at 75%. ROC analysis revealed high discriminative power for all playing positions, with AUC values of 1.00 for Forwards, 0.96 for Defenders, and 0.94 for Midfielders. Feature importance analysis revealed that the most influential variables in playing position classification were 20 m dribbling, shooting, body weight, and dribbling; while the head juggling and mixed juggling variables contributed the least to the model. These findings demonstrate that playing position-specific physical and technical characteristics in footballers can be reliably distinguished using both statistical methods and machine learning models, and that performance variables based on speed, finishing ability and physical capacity are particularly decisive in playing position classification.
Izhanov et al. (Fri,) studied this question.