Identifying Undervalued Football Players Using Performance Metrics and Salary Data

This study investigates whether publicly available football performance statistics can be used to identify undervalued players across major professional leagues. Using performance data from the 2024–2025 season and salary data from 2025–2026, a linear regression model was trained on UEFA Champions League player data to estimate expected salaries based on measurable on-field contributions. The model was then applied to domestic leagues—including the Premier League, La Liga, Bundesliga, Serie A, Ligue 1, and Major League Soccer (MLS)—to compute predicted salaries and performance-adjusted valuation ratios. The results reveal systematic discrepancies between actual wages and performance-based salary predictions across competitions. While predicted salaries are not intended as literal wage prescriptions, they serve as benchmark indicators of relative valuation. Cross-league comparisons show that wage alignment varies according to institutional and economic structures, with larger gaps observed in leagues characterized by salary constraints or developmental dynamics. Overall, the findings demonstrate that transparent statistical modeling can identify players whose measurable output exceeds their compensation. This framework supports budget-conscious recruitment strategies by emphasizing performance efficiency rather than absolute spending levels, illustrating how publicly available data can inform data-driven decision-making in professional football.