Absolute and relative performance indicators associated with match success across six European professional football leagues
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Abstract
Football performance analysis has traditionally focused on identifying which match-performance indicators are associated with competitive success, while comparatively little attention has been paid to how performance itself should be represented analytically. This study examined whether match success in professional football is more informatively characterised by absolute indicators, team–opponent differences, or their combined representation. All 2,058 matches played during the 2023–2024 season across six European professional domestic leagues were analysed, yielding 4,116 team-match observations. Descriptive analyses and one-way analyses of variance compared wins, draws and losses. Victory versus non-victory discrimination was evaluated using L2-penalised logistic regression with nested five-fold cross-validation grouped by match. Full models included goal-derived conversion rates, whereas a sensitivity analysis excluded all variables containing goals. In the full analysis, the relative model outperformed the absolute model (AUC = 0.979 vs. 0.864; difference = 0.115, 95% CI [0.105, 0.125]), and the combined model provided only a small additional gain (AUC = 0.980). After removing goal-derived rates, discrimination decreased, but the relative advantage persisted (AUC = 0.798 vs. 0.740; difference = 0.058, 95% CI [0.048, 0.069]), while the combined process-only model did not improve on the relative model. A secondary three-indicator explanatory model remained materially stable after adjustment for team fixed effects. These findings distinguish production from superiority: absolute indicators describe what a team produced, whereas opponent-referenced differences describe how that production compared within the same competitive interaction. Relative representation therefore retains meaningful discriminatory information beyond isolated production, although very high model-performance estimates are substantially influenced by variables mathematically proximal to goals. The results support purpose-aligned use of absolute indicators for description, relative indicators for competitive comparison, and internally validated process-only models for forward-looking applications.
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