23 citations · 27 across the 6 of their papers we have counts for
7 papers
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…
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…
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…
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…
Optimising Long-Term Outcomes using Real-World Fluent Objectives: An Application to Football
Ryan Beal, Georgios Chalkiadakis, Timothy J. Norman +1
In this paper, we present a novel approach for optimising long-term tactical and strategic decision-making in football (soccer) by encapsulating events in a league environment acro…
Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model
Ryan Beal, Stuart E. Middleton, Timothy J. Norman +1
In this paper, we present a new application-focused benchmark dataset and results from a set of baseline Natural Language Processing and Machine Learning models for prediction of m…