Detection of individual ball possession in soccer

Martin Hoernig, Daniel Link, Michael Herrmann, Bernd Radig, Martin Lames

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


While ball possession usually is considered on team level, a model on player level brings several advantages. We calculate ball possession and control statistics for all players as well as new ball control heat maps to evaluate the players’ performances. Furthermore, a basis for detecting events and tactical structure becomes available. To derive individual ball possession from spatio-temporal data, we present an automatic approach, based both on physical knowledge and machine learning techniques. Moreover, we introduce different ball possession definitions and algorithms to model various grades of ball control. When applied to flawless raw data, the algorithms show precision and recall ratios between 80 and 92%. With approximately four percentage points less in uncorrected data, the presented algorithms are also reliable in real-world scenarios.

Original languageEnglish
Title of host publicationProceedings of the 10th International Symposium on Computer Science in Sports, ISCSS
EditorsPaul Chung, Andrea Soltoggio, Christian W. Dawson, Qinggang Meng, Matthew Pain
PublisherSpringer Verlag
Number of pages5
ISBN (Print)9783319245584
StatePublished - 2016
Event10th International Symposium of Computer Science in Sport, IACSS/ISCSS 2015 - Loughborough, United Kingdom
Duration: 9 Sep 201511 Sep 2015

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


Conference10th International Symposium of Computer Science in Sport, IACSS/ISCSS 2015
Country/TerritoryUnited Kingdom


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