Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage
This work addresses the need for amateur goalkeepers to learn from professionals, but it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of analyzing professional goalkeeper technique from broadcast footage using computer vision and machine learning, resulting in an 'expected saves' model that identifies optimal techniques in different match contexts.
As an amateur goalkeeper playing grassroots soccer, who better to learn from than top professional goalkeepers? In this paper, we harness computer vision and machine learning models to appraise the save technique of professionals in a way those at lower levels can learn from. We train an unsupervised machine learning model using 3D body pose data extracted from broadcast footage to learn professional goalkeeper technique. Then, an "expected saves" model is developed, from which we can identify the optimal goalkeeper technique in different match contexts.