Automated Detection of Complex Tactical Patterns in Football—Using Machine Learning Techniques to Identify Tactical Behavior

DSpace Repository

Show simple item record

dc.contributor.advisor Höner, Oliver (Prof. Dr.)
dc.contributor.author Bauer, Pascal
dc.date.accessioned 2022-02-17T17:03:03Z
dc.date.available 2022-02-17T17:03:03Z
dc.date.issued 2022-02-17
dc.identifier.uri http://hdl.handle.net/10900/124679
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1246795 de_DE
dc.identifier.uri http://dx.doi.org/10.15496/publikation-66042
dc.description.abstract Football tactics is a topic of public interest, where decisions are predominantly made based on gut instincts from domain-experts. Sport science literature often highlights the need for evidence-based research on football tactics, however the limited capabilities in modeling the dynamics of football has prevented researchers from gaining usable insights. Recent technological advances have made high quality football data more available and affordable. Particularly, positional data providing player and ball coordinates at every instance of a match can be combined with event data containing spatio-temporal information on any event taking place on the pitch (e.g. passes, shots, fouls). On the other hand, the application of machine learning methods to domain-specific problems yields a paradigm shift in many industries including sports. The need for more informed decisions as well as automating time consuming processes—accelerated by the availability of data—has motivated many scientific investigations in football analytics. This thesis is part of a research program combining methodologies from sports and data science to address the following problems: the synchronization of positional and event data, objectively quantifying offensive actions, as well as the detection of tactical patterns. Although various basic insights from the overall research program are integrated, this thesis focuses primarily on the latter one. Specifically, positional and event data are used to apply machine learning techniques to identify eight established tactical patterns in football: namely high-/mid-/low-block defending, build-up/attacking play in the offense, counterpressing and counterattacks during transitions, and patterns when defending corner-kicks, e.g. player-/zonal- or post-marking. For each pattern, we consolidate definitions with football experts and label large amounts of data manually using video recordings. The inter-labeler reliability is used to ensure that each pattern is well-defined. Unsupervised techniques are used for the purpose of exploration, and supervised machine learning methods based on expert-labeled data for the final detection. As an outlook, semi-supervised methods were used to reduce the labeling effort. This thesis proves that the detection of tactical patterns can optimize everyday processes in professional clubs, and leverage the domain of tactical analysis in sport science by gaining unseen insights. Additionally, we add value to the machine learning domain by evaluating recent methods in supervised and semi supervised machine learning on challenging, real-world problems. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.rights ubt-podok de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=de de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=en en
dc.subject.ddc 004 de_DE
dc.subject.ddc 500 de_DE
dc.subject.ddc 510 de_DE
dc.subject.ddc 796 de_DE
dc.subject.other Sports Analytics en
dc.subject.other Football (Soccer) Analytics en
dc.subject.other Machine Learning in Sports en
dc.subject.other Tactical Patterns en
dc.subject.other Phases of play en
dc.subject.other Football (Soccer) en
dc.subject.other Tracking Data en
dc.subject.other Event Data en
dc.title Automated Detection of Complex Tactical Patterns in Football—Using Machine Learning Techniques to Identify Tactical Behavior en
dc.type PhDThesis de_DE
dcterms.dateAccepted 2022-01-13
utue.publikation.fachbereich Sportwissenschaft de_DE
utue.publikation.fakultaet 6 Wirtschafts- und Sozialwissenschaftliche Fakultät de_DE
utue.publikation.noppn yes de_DE

Dateien:

This item appears in the following Collection(s)

Show simple item record