Pitch Vision: AI-Powered Football Analytics Platform

Abstract

The integration of artificial intelligence and computer vision into sports analysis has created opportunities for enhanced understanding of player performance, tactical strategies, and match dynamics. This thesis presents PitchVision, an AI-powered football analytics platform that leverages YOLO object detection and ByteTrack algorithms to identify and track players, the ball, and key events from standard video footage without specialized hardware. The system solves key challenges in modern football analysis: high costs of advanced tools, inaccuracies in manual tracking, lack of real-time feedback, and integration complexities. Using React.js for frontend and Django for backend, PitchVision offers an interactive web interface for match tracking, performance analysis, tactical visualization, and reporting. The system efficiently classifies players by team, tracks movements, generates heat maps, calculates metrics such as distance and speed, and identifies key events. The evaluation shows over 90% accuracy in player detection and team classification across various conditions, with capability to process HD video at 20+ frames per second on consumer hardware. The significance extends beyond technical innovation to practical applications in coaching, player development, and fan engagement, democratizing sophisticated analytics for smaller clubs, academies, and individual analysts. Keywords: Computer Vision, YOLO, Football Analytics, Object Detection, Sports Technology, Performance Analysis, ByteTrack Tracking, Web Application