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Flexible marked spatio-temporal point processes with applications to event sequences from association football

Narayanan, Santhosh; Kosmidis, Ioannis; Dellaportas, Petros; (2022) Flexible marked spatio-temporal point processes with applications to event sequences from association football. Journal of the Royal Statistical Society Series C: Applied Statistics , 72 (5) pp. 1095-1126. 10.1093/jrsssc/qlad085. Green open access

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Abstract

We develop a new family of marked point processes by focusing the characteristic properties of marked Hawkes processes exclusively to the space of marks, providing the freedom to specify a different model for the occurrence times. This is possible through the decomposition of the joint distribution of marks and times that allows to separately specify the conditional distribution of marks given the filtration of the process and the current time. We develop a Bayesian framework for the inference and prediction from this family of marked point processes that can naturally accommodate process and point-specific covariate information to drive cross-excitations, offering wide flexibility and applicability in the modelling of real-world processes. The framework is used here for the modelling of in-game event sequences from association football, resulting not only in inferences about previously unquantified characteristics of game dynamics and extraction of event-specific team abilities, but also in predictions for the occurrence of events of interest, such as goals, corners or fouls in a specified interval of time.

Type: Article
Title: Flexible marked spatio-temporal point processes with applications to event sequences from association football
Event: Royal Statistical Society Statistics in Sport Section online meeting
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/jrsssc/qlad085
Publisher version: https://doi.org/10.1093/jrsssc/qlad085
Language: English
Additional information: © The Royal Statistical Society 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Keywords: Bayesian inference, branching structure, Hamiltonian Monte Carlo, team abilities
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10161552
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