Predicting the in-game status in soccer with machine learning using spatiotemporal player tracking data.

ABSTRACT: An important structuring feature of a soccer match is the in-game status, whether a match is interrupted or in play. This is necessary to calculate performance indicators relative to the effective playing time or to find standard situations, ball actions, and other tactical structures in spatiotemporal data. Our study explores the extent to which the in-game status can be determined using time-continuous player positions. Therefore, to determine the in-game status we tested four established machine learning methods: logistic regression, decision trees, random forests, and AdaBoost. The models were trained and evaluated using spatiotemporal data and manually annotated in-game status of 102 matches in the German Bundesliga. Results show up to 92% accuracy in predicting the in-game status in

SUBMITTER: Lang S 

PROVIDER: S-EPMC9522646 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature