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
ISSN1863-8171
DOIs
PublikationsstatusAngenommen/Im Druck - 2022

DOI