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.

Evaluating individual contributions from team members is a critical challenge across many domains, such as security and team sports. While progress has been made in valuing contributions, such as target defence in security or on-ball performance in football (soccer), many aspects of performance, such as off-ball football actions, remain difficult to quantify. We introduce GAPP, a Graph Attention Network model that predicts football pass reception probabilities and provides interpretable insights into off-ball defending. Using attention mechanisms, GAPP captures player interactions and introduces two new metrics to quantify defender contributions. We tested GAPP on 306 English Premier League matches, and showed it reduces binary cross-entropy loss by 6.4 percent compared to multiple baselines for pass reception prediction, while offering unique insights for off-ball defender evaluation for coaches, scouts and teams. This work shows the potential of graph attention networks for analysing complex multi-agent systems like football.

Soccer is a sport characterised by open and dynamic play, with player actions and roles aligned according to team strategies simultaneously and at multiple temporal scales with high spatial freedom. This complexity presents an analytics challenge, which to date has largely been solved by decomposing the game according to specific criteria to analyse specific problems. We propose a more holistic approach, utilising Transformer or RNN components in the novel Seq2Event model, in which the next match event is predicted given prior match events and context. We show metric creation using a general purpose context-aware model as a deployable practical application, and demonstrate development of the poss-util metric using a Seq2Event model. Summarising the expectation of key attacking events (shot, cross) during each possession, our metric is shown to correlate over matches (r=0.91, n=190) with the popular xG metric. Example practical application of poss-util to analyse behaviour over possessions and matches is made. Potential in sports with stronger sequentiality, such as rugby union, is discussed.

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 match outcomes for games of football (soccer). By doing so we give a baseline for the prediction accuracy that can be achieved exploiting both statistical match data and contextual articles from human sports journalists. Our dataset is focuses on a representative time-period over 6 seasons of the English Premier League, and includes newspaper match previews from The Guardian. The models presented in this paper achieve an accuracy of 63.18% showing a 6.9% boost on the traditional statistical methods.

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 of the game, while there has been less work on event-driven metrics for valuing defensive actions (e.g., tackles and interceptions). Therefore in this paper, we use deep learning techniques to define a novel metric that values such defensive actions by studying the threat of passages of play that preceded them. By doing so, we are able to value defensive actions based on what they prevented from happening in the game. Our Defensive Action Expected Threat (DAxT) model has been validated using real-world event-data from the 2017/2018 and 2018/2019 English Premier League seasons, and we combine our model outputs with additional features to derive an overall rating of defensive ability for players. Overall, we find that our model is able to predict the impact of defensive actions allowing us to better value defenders using event-data.

In this paper we describe a novel approach to team formation based on the value of inter-agent interactions. Specifically, we propose a model of teamwork that considers outcomes from chains of interactions between agents. Based on our model, we devise a number of network metrics to capture the contribution of interactions between agents. This is then used to learn the value of teamwork from historical team performance data. We apply our model to predict team performance and validate our approach using real-world team performance data from the 2018 FIFA World Cup. Our model is shown to better predict the real-world performance of teams by up to 46% compared to models that ignore inter-agent interactions.