Professional Goalkeeper

Research on professional goalkeepers is increasingly leveraging computer vision and machine learning to analyze performance and technique. Current efforts focus on developing models that predict optimal goalkeeper movements, assessing performance metrics like expected possession value and save effectiveness, and creating tools for analyzing and improving decision-making through pose estimation and motion correction algorithms. This work has implications for both sports analytics, providing objective performance evaluations and training aids, and for the broader field of computer vision, pushing the boundaries of human pose estimation in challenging scenarios like those involving complex equipment and occlusions.