Artificial intelligence in team sports

Abstract

The Sports Analytics Market is growing rapidly, in 2020 it was valued at over $1 billion and is expected to reach over $5 billion by 2026. However, even with this level of growth the use of Artificial Intelligence (AI) techniques have yet to fully be explored. The sports analytics domain presents a number of significant computational challenges for AI and Machine Learning. In this thesis, we propose a number novel methods for analysing team sports data to help sports teams utilise AI to improve their strategic and tactical decision making. By doing so, we present a number of contributions to the AI and sports analytics communities. In particular, we present a model for the tactical decisions that are made in football and show how game theoretic techniques can be used to optimise these. We focus on both the short-term decisions made for individual games, as well as longer term decisions to maximise performance over a season. We show that we can increase a teams chances of winning individual games by 16.1% and can increase a teams mean expected finishing position by up to 35.6%. We also, introduce a new model for valuing the teamwork between players in sports teams by assessing the outcomes of chains of interactions between the players in a team. We then present a novel model for forming teams based on this value and maximise teamwork by assessing the overlapping pairs in a team. 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. Finally, we show how we can use natural language processing techniques to improve the traditional statistical methods for prediction sports match outcomes. We use domain expert written articles from the media to train our models and we show that by incorporating the features learned from the text, we can boost the accuracy of the traditional statistical methods by 6.9%.

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Identifiers

ORCID for Sarvapali Ramchurn: orcid.org/0000-0001-9686-4302

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