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

Seq2Event: learning the language of soccer using transformer-based match event prediction
Seq2Event: learning the language of soccer using transformer-based match event prediction
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.
ACM Press
Simpson, Ian
23ee4863-3b0d-4c7e-9cc2-e8ab2d6128a0
Beal, Ryan J
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Locke, Duncan
b6ae1e9e-b916-4d3d-af25-24c572987149
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d
Simpson, Ian
23ee4863-3b0d-4c7e-9cc2-e8ab2d6128a0
Beal, Ryan J
d62649ff-c1d8-4ce7-a752-4e5d2182d472
Locke, Duncan
b6ae1e9e-b916-4d3d-af25-24c572987149
Norman, Timothy
663e522f-807c-4569-9201-dc141c8eb50d

Simpson, Ian, Beal, Ryan J, Locke, Duncan and Norman, Timothy (2022) Seq2Event: learning the language of soccer using transformer-based match event prediction. In 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM Press. 11 pp .

Record type: Conference or Workshop Item (Paper)

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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More information

Published date: 14 August 2022
Venue - Dates: 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, , East Lansing, MI, United States, 2022-08-14 - 2022-08-18

Identifiers

Local EPrints ID: 458099
URI: http://eprints.soton.ac.uk/id/eprint/458099
PURE UUID: 36bd5130-d88d-4dd5-8afe-790ce84251ca
ORCID for Timothy Norman: ORCID iD orcid.org/0000-0002-6387-4034

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Date deposited: 28 Jun 2022 16:59
Last modified: 23 Jul 2022 02:12

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Contributors

Author: Ian Simpson
Author: Ryan J Beal
Author: Duncan Locke
Author: Timothy Norman ORCID iD

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