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YOLOv8 computer vision for automated offside detection in professional football validated through supervised learning
Abdul-Rahman Abdel-Fattah1, Samir Brahim Belhaouari1, Halil İbrahim Ceylan2
1Division of Information and Computing Technology, College of Science and Engineering, Hamad Ben Khalifa University, Doha, Qatar.
Scientific Reports
|May 11, 2026
Summary
This study introduces an automated offside detection system using YOLOv8, improving football officiating accuracy. The system offers computational efficiency while demonstrating proof-of-concept feasibility for decision support in sports.
Area of Science:
- Computer Vision
- Sports Analytics
- Artificial Intelligence
Background:
- Offside calls are crucial for football integrity but face accuracy challenges.
- Current Video Assistant Referee (VAR) systems have limitations in offside decision-making.
- Technological advancements are needed to enhance officiating accuracy.
Purpose of the Study:
- To develop and validate an automated offside detection system using YOLOv8 object detection.
- To address the limitations of VAR systems in offside decision accuracy.
- To optimize the system for computational efficiency and decision support in football.
Main Methods:
- Utilized YOLOv8 object detection architecture for automated offside detection.
- Employed supervised learning with 400 annotated instances from professional football matches.
- Configured YOLOv8 medium variant for four-class detection, focusing on extreme player positions for efficiency.
Main Results:
- Achieved 83.0% overall accuracy, 85.0% precision, 87.0% recall, and 86.0% F1-score on 240 test instances.
- Demonstrated significant improvement over random classification baseline (p < 0.001).
- The extreme player tracking approach yielded substantial computational efficiency with acceptable accuracy trade-offs.
Conclusions:
- A YOLOv8-based system shows feasibility for football officiating decision support.
- The system offers computational efficiency through strategic extreme player tracking.
- Further development is needed to overcome single-camera constraints and dataset limitations for operational deployment.
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