Large Scale Analysis of Offensive Performance in Football - Using Synchronized Positional and Event Data to Quantify Offensive Actions, Tactics, and Strategies

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dc.contributor.advisor Höner, Oliver (Prof. Dr.)
dc.contributor.author Anzer, Gabriel
dc.date.accessioned 2022-02-17T16:46:39Z
dc.date.available 2022-02-17T16:46:39Z
dc.date.issued 2022-02-17
dc.identifier.uri http://hdl.handle.net/10900/124678
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1246780 de_DE
dc.identifier.uri http://dx.doi.org/10.15496/publikation-66041
dc.description.abstract Offensive performances in football have always been of great focus for fans and clubs alike as evidenced by the fact that nearly all Ballon d’Or winners have been forwards or midfielders. With the increase in availability of granular data, evaluating these performances on a deeper level than just goals scored or gut instinct has become possible. The domain of sports analytics has recently emerged, exploring how applying data science techniques or other statistical methods to sports data can improve decision making within sporting organizations. This thesis follows the footsteps of other sports like baseball or basketball where, at first, offensive performances were analyzed. It consists of four studies exploring various levels of offensive performance, ranging from basic actions to team-level strategy. For that, it uses a dataset part of larger research program that also explores the automatic detection of tactical patterns. This dataset mainly consists of positional and event data from eight seasons of the German Bundesliga and German Bundesliga 2 between the seasons 2013/2014 and 2020/2021. In total this amounts to 4, 896 matches, with highly accurate player and ball positions for every moment of the match and detailed logs of every action that occurred, thus making it one of the largest football datasets to be analyzed at this level of granularity. In a first step, this thesis shows how the two different data sources can be synchronized. With this synchronized data it is possible to better quantify individual basic actions like shots or passes. For both actions new metrics (Expected Goals and Expected Passes) were developed, that use the contextual information to quantify the chance quality and passing difficulty. Using this improved quantification of individual actions, the subsequent studies evaluate offensive performance on a tactical pattern level (how goals are scored) and on a strategy level (what team formations are particular effective offensively). Besides their usage on the performance side, these metrics have also been adapted from broadcasters to enhance their data story telling: Expected goals and expected passes are shown during every Bundesliga match to a worldwide audience, thus bringing the field of sports analytics to millions of fans. 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 Analytics en
dc.subject.other  Machine Learning in Sports en
dc.subject.other Offensive Performance en
dc.subject.other Football (soccer) en
dc.subject.other Tracking Data en
dc.subject.other Event Data en
dc.title Large Scale Analysis of Offensive Performance in Football - Using Synchronized Positional and Event Data to Quantify Offensive Actions, Tactics, and Strategies 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

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