Football Player

Research on football players increasingly leverages machine learning to analyze player performance, predict success, and improve officiating. Current studies employ various models, including deep neural networks, graph convolutional networks, and ensemble methods, to analyze diverse data sources such as video footage, tracking data, and scouting combine results. This work aims to enhance player evaluation, optimize team strategies, and automate tasks like event detection and foul assessment, ultimately improving the accuracy and efficiency of football analytics and broadcasting. The resulting insights have implications for player scouting, coaching strategies, and the overall fan experience.