Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage

• URL: http://arxiv.org/abs/2202.12259v1
• Date: Tue, 22 Feb 2022 18:17:30 GMT
• Title: Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage
• Authors: Matthew Wear, Ryan Beal, Tim Matthews, Tim Norman and Sarvapali Ramchurn
• Abstract summary: 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.
• Score: 3.4386226615580107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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