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Inferring player location in sports matches: multi-agent spatial imputation from limited observations

Inferring player location in sports matches: multi-agent spatial imputation from limited observations
Inferring player location in sports matches: multi-agent spatial imputation from limited observations
Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS problems typically use uniform timesteps with observations for all agents. In this work, we analyse the problem of agent location imputation, specifically posed in environments with non-uniform timesteps and limited agent observability (~95% missing values). Our approach uses Long Short-Term Memory and Graph Neural Network components to learn temporal and inter-agent patterns to predict the location of all agents at every timestep. We apply this to the domain of football (soccer) by imputing the location of all players in a game from sparse event data (e.g., shots and passes). Our model estimates player locations to within ~6.9m; a ~62% reduction in error from the best performing baseline. This approach facilitates downstream analysis tasks such as player physical metrics, player coverage, and team pitch control. Existing solutions to these tasks often require optical tracking data, which is expensive to obtain and only available to elite clubs. By imputing player locations from easy to obtain event data, we increase the accessibility of downstream tasks.
cs.LG, cs.MA
Everett, Gregory
bd166375-38dd-4790-8a1e-8474e6f32c19
Beal, Ryan J.
d62649ff-c1d8-4ce7-a752-4e5d2182d472
Matthews, Tim
f41aa009-4f12-4887-8427-50d344d5d9b3
Early, Joseph
fd4e9e4c-9251-474d-a9cf-12157a9f2f73
Norman, Timothy J.
663e522f-807c-4569-9201-dc141c8eb50d
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Everett, Gregory
bd166375-38dd-4790-8a1e-8474e6f32c19
Beal, Ryan J.
d62649ff-c1d8-4ce7-a752-4e5d2182d472
Matthews, Tim
f41aa009-4f12-4887-8427-50d344d5d9b3
Early, Joseph
fd4e9e4c-9251-474d-a9cf-12157a9f2f73
Norman, Timothy J.
663e522f-807c-4569-9201-dc141c8eb50d
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS problems typically use uniform timesteps with observations for all agents. In this work, we analyse the problem of agent location imputation, specifically posed in environments with non-uniform timesteps and limited agent observability (~95% missing values). Our approach uses Long Short-Term Memory and Graph Neural Network components to learn temporal and inter-agent patterns to predict the location of all agents at every timestep. We apply this to the domain of football (soccer) by imputing the location of all players in a game from sparse event data (e.g., shots and passes). Our model estimates player locations to within ~6.9m; a ~62% reduction in error from the best performing baseline. This approach facilitates downstream analysis tasks such as player physical metrics, player coverage, and team pitch control. Existing solutions to these tasks often require optical tracking data, which is expensive to obtain and only available to elite clubs. By imputing player locations from easy to obtain event data, we increase the accessibility of downstream tasks.

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2302.06569v1 - Author's Original
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Published date: 13 February 2023
Keywords: cs.LG, cs.MA

Identifiers

Local EPrints ID: 477020
URI: http://eprints.soton.ac.uk/id/eprint/477020
PURE UUID: 7f1acd3a-c4a2-4534-9e36-1efda7d816b9
ORCID for Timothy J. Norman: ORCID iD orcid.org/0000-0002-6387-4034
ORCID for Sarvapali D. Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 23 May 2023 16:50
Last modified: 17 Mar 2024 03:41

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Contributors

Author: Gregory Everett
Author: Ryan J. Beal
Author: Tim Matthews
Author: Joseph Early
Author: Sarvapali D. Ramchurn ORCID iD

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