Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage
Matthew Wear, Ryan Beal, Tim Matthews, Tim Norman, Sarvapali, Ramchurn

TL;DR
This paper develops a computer vision and machine learning framework to analyze professional goalkeeper techniques from broadcast footage, aiming to help amateur goalkeepers learn effective skills by identifying optimal techniques in various match situations.
Contribution
It introduces an unsupervised learning approach using 3D body pose data to evaluate and extract professional goalkeeper techniques from broadcast footage.
Findings
Successful extraction of professional goalkeeper techniques
Development of an 'expected saves' model for optimal technique identification
Framework applicable to different match contexts
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSports Analytics and Performance · Sports Performance and Training · Anomaly Detection Techniques and Applications
