Football Match Analysis
Year: 2025
Type: article
Abstract: Football has always been a game of strategy, skill and precision, but analysing matches manually can betime-consuming and often lacks accuracy. Coaches and analysts need quick, reliable insights to makeinformed decisions during and after a game. This research introduces an AI-powered approach to footballmatch analysis, using computer vision and deep learning to track player movements, ball trajectories andteam formations in real-time. By combining YOLOv5 for player and ball detection, K-Means clusteringfor movement analysis and OpenCV for preprocessing, our system provides accurate performance metricslike passes, shots and distance covered. With this automated solution, coaches can gain valuable tacticalinsights without the burden of manual data collection. By making match analysis faster and more precise,our research highlights how AI can revolutionize the way teams prepare, strategize and improve theirgame.
Author: Rahil Bhatia, Himneesh Bhatte, Tejas Bhoirekar, Shubham Lonkar, Prof.Bhagyashree Patil
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893341488
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