AI Football

Abstract

The system uses modern machine learning and computer vision technology to develop AI analytics that increases performance evaluation capabilities for football analysis. The system provides performance data about teams and addresses the need of tracking both balls in real-time and monitoring player activities. Key objectives include: • YOLOv11 serves as the first function by both recognizing human body positions and identifying the precise loca- tions of these significant points. • The field application of Homography enables the platform to deliver exact spatial positioning results. • RF-DETR executes gameplay detection for both ball carriers and football players in their field zone. • The EasyOCR software program effectively retrieves text information from printed numbers present on sportswear. • K-means clustering provides the mechanism to analyze teams into separate groups. • Calculating player speed and ball possession. • Speed statistics and ball possession data measurement are part of the platform functionality that tracks players whileassessing their strategies. Orthographic calculations thatanalyze tracking data alongside multiple other sets of data provide essential insights to coaches as well as analytical team members. This project employs automated process handling along with football dynamics expertise enhancement to deal with sports analytics requirements while building a computing-based solution.