YOLOv8 computer vision for automated offside detection in professional football validated through supervised learning

Offside calls are integral to maintaining fairness and competitive integrity in football, yet current officiating processes are constrained by limitations in accuracy despite technological advancements. This investigation developed and validated an automated offside detection system using YOLOv8 object detection architecture to address documented limitations of Video Assistant Referee (VAR) systems in offside decision accuracy. Supervised learning validation employed 400 annotated instances from professional match footage. The YOLOv8 medium variant was configured for four-class detection, with a focus on extreme player positions, reducing computational complexity by approximately 70% relative to comprehensive player tracking while accepting accuracy trade-offs in marginal scenarios. The system was optimized for computational efficiency through highly accurate tracking of player positions. Data were partitioned into 40% (n = 160) for training and 60% (n = 240) for testing, with 5-fold cross-validation. The automated system achieved robust performance metrics across 240 test instances: overall accuracy of 83.0%, precision of 85.0%, recall of 87.0%, and F1-score of 86.0%. Confusion matrix analysis revealed true positives (n = 130), true negatives (n = 70), false positives (n = 22), and false negatives (n = 18). Individual case analysis demonstrated 80% agreement with ground truth classifications. System performance significantly exceeded the random classification baseline (p < 0.001). The strategic extreme player tracking approach achieved substantial computational efficiency while maintaining decision accuracy across diverse match scenarios. A YOLOv8-based offside detection system demonstrates proof-of-concept feasibility for decision-support applications in football officiating. Performance (83% accuracy, 95% CI: 79-87%) on 240 test instances remains below professional benchmarks (> 93%), with single-camera constraints and limited dataset size restricting generalizability to proof-of-concept validation rather than operational deployment.