Football Player Detection and Tactical Analysis with Deep Learning

Abstract

In the realm of football, the ability to analyze and understand player movements and tactics is crucial for coaches and teams. Bangladesh's traditional football analysis approach is based on human observation by coaches and other helping staff. The drawbacks of this method are its time-consuming nature and difficulty in identifying trends in massive amounts of data.This paper proposes a deep learning-based football player detection and tactical analysis system. This thesis introduces a cutting-edge method for deep learning based player recognition and tactical analysis of football players. Which will help the team players and coaches. To achieve accurate player detection and tactical analysis, the YOLO v8 (you only look once) object detection model is trained on a large dataset of football and videos. Currently, YOLOv8 has better object-detecting accuracy. After training, the model has an 89% mAP (Mean Average Precision). The detected players are then tracked and their positions are recorded. The system also generates heat maps from the statistical data of the player’s movements. The generated heatmaps can be used by coaches to make decisions about tactics and player positioning. In summary, this thesis offers a system for detecting football players and analyzing their tactical movements that blend deep learning methods—more specifically, the YOLO V8 model—with the creation of heat maps. To enhance their team's performance, coaches may use the system to collect statistical data, display player distributions, and make wise judgments. Using deep learning and machine vision to their full potential.