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What happened next? Using deep learning to value defensive actions in football event-data

What happened next? Using deep learning to value defensive actions in football event-data
What happened next? Using deep learning to value defensive actions in football event-data
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.
Merhej, Charbel
e2845c15-521a-40bb-81b4-6cef4705e54d
Beal, Ryan, James
d9874cb0-bd92-4a16-8576-78d769b41ff7
Matthews, Tim
f41aa009-4f12-4887-8427-50d344d5d9b3
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Merhej, Charbel
e2845c15-521a-40bb-81b4-6cef4705e54d
Beal, Ryan, James
d9874cb0-bd92-4a16-8576-78d769b41ff7
Matthews, Tim
f41aa009-4f12-4887-8427-50d344d5d9b3
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3

Merhej, Charbel, Beal, Ryan, James, Matthews, Tim and Ramchurn, Sarvapali (2021) What happened next? Using deep learning to value defensive actions in football event-data. KDD 2021, Virtual. 14 - 18 Aug 2021. 10 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

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.

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DAxT_KDD_2021 - Author's Original
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More information

Published date: 2021
Venue - Dates: KDD 2021, Virtual, 2021-08-14 - 2021-08-18

Identifiers

Local EPrints ID: 449656
URI: http://eprints.soton.ac.uk/id/eprint/449656
PURE UUID: 1fdab91a-a491-43b3-bc29-d464dee64b5b
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 10 Jun 2021 16:31
Last modified: 11 Jun 2021 01:39

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