Dynamic Financial Valuation of Football Players: A Machine Learning Approach Across Career Stages

The financial valuation of professional football players is influenced by multiple factors that evolve throughout a player’s career. This study examines these determinants using Gradient Boosting Machine Learning models, segmented by three age categories and three playing positions to capture the dynamic nature of player valuation. K-fold cross-validation is applied to measure accuracy, with results indicating that incorporating a player’s projected future potential improves model precision from an average of 74% to 84%. The findings reveal that the relevance of valuation factors diminishes with age, and the most influential features vary by position—shooting for attackers, passing for midfielders, and defensive skills for defenders. The study adopts a dynamic segmentation approach, providing financial insights relevant to club managers, investors, and stakeholders in sports finance. The results contribute to sports analytics and financial modeling in sports, with applications in contract negotiations, talent scouting, and transfer market decisions.