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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

(In press).


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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
    Publisher version: https://rss.onlinelibrary.wiley.com/journal/146798…
    Language: English
    Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
    Keywords: Bayesian inference; Hamiltonian Monte Carlo; team abilities; branching structure
    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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