The sports domain presents a number of significant computational challenges for artificial intelligence (AI) and machine learning (ML). In this paper, we explore the techniques that have been applied to the challenges within team sports thus far. We focus on a number of different areas, namely match outcome prediction, tactical decision making, player investments, fantasy sports, and injury prediction. By assessing the work in these areas, we explore how AI is used to predict match outcomes and to help sports teams improve their strategic and tactical decision making. In particular, we describe the main directions in which research efforts have been focused to date. This highlights not only a number of strengths but also weaknesses of the models and techniques that have been employed. Finally, we discuss the research questions that exist in order to further the use of AI and ML in team sports.
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. CCS CONCEPTS• Applied computing; • Computing methodologies → Machine learning; Model development and analysis;
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. CCS CONCEPTS• Applied computing; • Computing methodologies → Model development and analysis; Neural networks;
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.
In this paper, we critically evaluate the performance of nine machine learning classification techniques when applied to the match outcome prediction problem presented by American Football. Specifically, we implement and test nine techniques using real-world datasets of 1280 games over 5 seasons from the National Football League (NFL). We test the nine different classifier techniques using a total of 42 features for each team and we find that the best performing algorithms are able to improve one previous published works. The algoriothms achieve an accuracy of between 44.64% for a Guassian Process classifier to 67.53% with a NaĂŻve Bayes classifer. We also test each classifier on a year by year basis and compare our results to those of the bookmakers and other leading academic papers.
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