Seq2Event: learning the language of soccer using transformer-based match event prediction

Abstract

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 (푟 = 0.91, 푛 = 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.

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ORCID for Timothy Norman: orcid.org/0000-0002-6387-4034

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