Action rate models for predicting actions in soccer

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Authors

We present a data-driven approach to predict the next action in soccer. We focus on passing actions of the ball possessing player and aim to forecast the pass itself and when, in time, the pass will be played. At the same time, our model estimates the probability that the player loses possession of the ball before she can perform the action. Our approach consists of parameterized exponential rate models for all possible actions that are adapted to historic data with graph recurrent neural networks to account for inter-dependencies of the output space (i.e., the possible actions). We report on empirical results.

OriginalspracheEnglisch
ZeitschriftAStA Advances in Statistical Analysis
Jahrgang107
Ausgabenummer1-2
Seiten (von - bis)29-49
Anzahl der Seiten21
ISSN1863-8171
DOIs
PublikationsstatusErschienen - 02.03.2022

Bibliographische Notiz

Funding Information:
We would like to thank Hendrik Weber as well as DFL and Sportec Solutions for providing the data for this study.

Publisher Copyright:
© 2022, The Author(s).

DOI

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