A differencing approach to opponent-relative features for intelligent match prediction

This study introduces and validates a differencing-based feature construction method to improve match outcome prediction. Using English Premier League data (2021–2024), we apply first-order differencing to technical performance metrics, creating opponent-relative features that capture tactical advantages against specific opponent. We develop and compare three model architectures: (1) baseline models using raw metrics, (2) differenced models with opponent-relative metrics, and (3) hybrid models combining both. Formulated in logistic regression frameworks, these models estimate winning probabilities. Results show that opponent-relative differencing significantly enhances predictive accuracy, with relative metrics proving more informative than absolute ones. Moreover, differenced features alone perform on par with hybrid models, suggesting that raw metrics may be redundant. This highlights the sufficiency of opponent-relative features for reliable forecasting and points to a methodological shift in sports analytic. We recommend that both practitioners and researchers prioritize opponent-relative over absolute technical metrics to achieve superior predictive performance. In addition, from a computer enhancement perspective, sports information databases should not only store raw technical data per team per match but also automatically compute opponent-relative differenced metrics to enable more intelligent analytic.