Predicting the in-game status in soccer with machine learning using spatiotemporal player tracking data
Crossref DOI link: https://doi.org/10.1038/s41598-022-19948-1
Published Online: 2022-09-29
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Lang, Steffen http://orcid.org/0000-0002-9313-8142
Wild, Raphael
Isenko, Alexander
Link, Daniel
Funding for this research was provided by:
Technische Universität München
Text and Data Mining valid from 2022-09-29
Version of Record valid from 2022-09-29
Article History
Received: 1 July 2022
Accepted: 7 September 2022
First Online: 29 September 2022
Competing interests
: The authors declare no competing interests.