International Journal of Scientific Research and Engineering Development

Football analytics has become an indispensable component of modern sports performance evaluation, driven by the increasing availability of match video data and advancements in computer vision and deep learning technologies. Traditional football analysis techniques rely heavily on manual video inspection and subjective judgment, which limits scalability and introduces human bias. To overcome these limitations, this paper presents a comprehensive automated football analytics framework capable of extracting tactical and physical insights directly from broadcast match videos. The proposed system integrates deep learning-based object detection, multi-object player tracking, team identification using colour-based clustering, and holography-based field projection to enable metric analysis. Using this unified pipeline, the system computes detailed performance indicators such as player speed, total distance covered, heatmaps, positional distribution, and basic tactical structures. The framework is designed to operate efficiently under real-world match conditions, including dynamic camera motion, occlusions, and varying illumination. Experimental evaluation on multiple broadcast football matches demonstrates that the proposed approach achieves reliable detection accuracy, stable tracking performance, and near real-time processing capability. The results indicate that the system is suitable for post-match analysis, coaching support, scouting, and broadcast enhancement applications.

Jayashree M, Tanmay Raj, Sushant Dhakal, Sameer Khan, "Video Processing and Data Analysis for Football Matches using Deep Learning" International Journal of Scientific Research and Engineering Development, V8(6): Page(2780-2786) Nov-Dec 2025. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.