Publications (5)
Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage
Matthew Wear, Ryan Beal, Tim Matthews +2
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 model…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
The Strain of Success: A Predictive Model for Injury Risk Mitigation and Team Success in Soccer
Gregory Everett, Ryan Beal, Tim Matthews +2
In this paper, we present a novel sequential team selection model in soccer. Specifically, we model the stochastic process of player injury and unavailability using player-specific…
What Happened Next? Using Deep Learning to Value Defensive Actions in Football Event-Data
Charbel Merhej, Ryan Beal, Sarvapali Ramchurn +1
Objectively quantifying the value of player actions in football (soccer) is a challenging problem. To date, studies in football analytics have mainly focused on the attacking side…
Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations
Gregory Everett, Ryan J. Beal, Tim Matthews +3
Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS prob…