Football Match Analysis With Yolo

Abstract: Football match analysis is an important task in modern sports analytics for evaluating player performance and tactical strategies. Traditional analysis methods depend on manual observation, which is time-consuming and subjective. This paper presents an automated football match analysis system using the YOLO (You Only Look Once) deep learning algorithm for real-time object detection. The proposed system processes football match videos to detect key entities such as players, referees, and the ball. YOLO performs single-stage detection by predicting bounding boxes and class probabilities in a single forward pass, enabling fast and efficient analysis.The model is trained on annotated football match datasets and evaluated using standard metrics including precision, recall, mean Average Precision (mAP), and frames per second (FPS). Experimental results show that the system achieves high detection accuracy with real-time performance, even in dynamic match conditions. The proposed approach reduces computational complexity compared to traditional methods and can assist coaches and analysts in performance evaluation and tactical decision- making.