"YOLO-Based Object Detection and Player Tracking for Automated Football Match Analysis"

Performance analysis is essential to improving coaching, player development, and strategy in contemporary football. The need for automated solutions stems from the time-consuming and error-prone nature of traditional manual annotation. In order to extract important match statistics from unprocessed broadcast video, this work proposes a computer vision-based football analysis pipeline that combines OpenCV tracking, YOLO-based object detection, and machine learning techniques. The pipeline uses motion estimation to account for camera movement while detecting and tracking players, referees, and the ball. By examining player jersey colors, a clustering technique based on KMeans determines team identities. The closest player is assigned the ball in space to determine ball possession, and geometric transformations are used to calculate player dynamics like speed, distance traveled, and field positioning. By integrating deep learning, computer vision, and statistical analysis in a fully automated workflow, this study demonstrates an effective framework for real-time or post-match football analytics, with potential applications in coaching, sports broadcasting, and fan engagement. The system generates annotated video outputs that highlight team control, player trajectories, and match events.