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Tim Matthews

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3 papers

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AAAI Conference 2025 Conference Paper

Optimising Spatial Teamwork Under Uncertainty

Gregory Everett
Ryan J. Beal
Tim Matthews
Timothy J. Norman
Sarvapali D. Ramchurn

We introduce a novel method for assessing agent teamwork based on their spatial coordination. Our approach models the influence of spatial proximity on team formation and sustained spatial dominance over adversaries using a Multi-agent Markov Decision Process. We develop an algorithm to derive efficient teamwork strategies by combining Monte Carlo Tree Search and linear programming. When applied to team defence in football (soccer) using real-world data, our approach reduces opponent threat by 21%, outperforming optimised individual behaviour by 6%. Additionally, our model enhances the predictive accuracy of future attack locations and provides deeper insights compared to existing teamwork models that do not explicitly consider the spatial dynamics of teamwork.

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AAMAS Conference 2023 Conference Paper

Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations

Gregory Everett
Ryan J. Beal
Tim Matthews
Joseph Early
Timothy J. Norman
Sarvapali D. Ramchurn

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 problems typically use uniform timesteps with observations for all agents. In this work, we analyse the problem of agent location imputation, specifically posed in environments with non-uniform timesteps and limited agent observability (∼95% missing values). Our approach uses Long Short-Term Memory and Graph Neural Network components to learn temporal and inter-agent patterns to predict the location of all agents at every timestep. We apply this to the domain of football (soccer) by imputing the location of all players in a game from sparse event data (e. g. , shots and passes). Our model estimates player locations to within ∼6. 9m; a ∼62% reduction in error from the best performing baseline. This approach facilitates downstream analysis tasks such as player physical metrics, player coverage, and team pitch control. Existing solutions to these tasks often require optical tracking data, which is expensive to obtain and only available to elite clubs. By imputing player locations from easy to obtain event data, we increase the accessibility of downstream tasks.

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AAAI Conference 2012 Conference Paper

Competing with Humans at Fantasy Football: Team Formation in Large Partially-Observable Domains

Tim Matthews
Sarvapali Ramchurn
Georgios Chalkiadakis

We present the first real-world benchmark for sequentiallyoptimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker’s beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers’ performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2. 5M human players.

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