A data-driven multidimensional performance evaluation framework for university soccer midfielders

Abstract

Traditional methods for evaluating soccer midfielders often rely on subjective judgment, which fails to comprehensively reflect players’ multidimensional performance. This study constructs a systematic, data-driven evaluation model for midfield players using Principal Component Analysis (PCA) and Analytic Hierarchy Process (AHP), providing a scientific and quantitative approach to player selection and training. The model integrates six primary dimensions: performance indicators (weight 0.4269), psychological factors (weight 0.2050), physical fitness (weight 0.1738), technical skills (weight 0.1001), body morphology (weight 0.0596), and physiological function (weight 0.0346). it shows that "penetrative pass" (weight 0.2521) and "target focus" (weight 0.1185) are the most influential secondary indicators, highlighting the midfielder’s essential role in offensive organization, team coordination, and pressure management. While technical skills, body morphology, and physiological function carry lower weights, they provide critical supplementary insights for comprehensive evaluations. Through expert scoring and robust validation, the study demonstrates the model’s applicability for optimizing midfielder selection, training design, and performance enhancement. This model offers a systematic evaluation tool for Chinese university soccer, establishing a scientific foundation for improving the selection and development pathways for midfield players.