Football AI Tutorial: From Basics to Advanced Stats with Python

Dive into a comprehensive tutorial on building a Football AI system to enhance match statistics analysis. Learn to leverage computer vision and machine learning techniques for player tracking, team identification, and advanced metrics calculation like ball possession and speed. Explore the architectural blueprint for Football AI, fine-tune YOLOv8 for object detection, implement robust multi-object tracking with ByteTrack, and perform player clustering using embedding analysis. Master perspective transformation, pitch landmark detection, and homography applications for creating virtual overlays and tactical radar views. Gain insights into spatial analysis for ball territory mapping and discuss implementation challenges. Discover potential future applications in sports analytics while getting hands-on experience with Python-based tools and datasets provided by Roboflow.