UnFOOT - Unsupervised Football Analytics Tool

José Carlos Coutinho, João Moreira, Claudio Rebelo de Sá

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

Abstract

Labelled football (soccer) data is hard to acquire and it usually needs humans to annotate the match events. This process makes it more expensive to be obtained by smaller clubs. UnFOOT (Unsupervised Football Analytics Tool) combines data mining techniques and basic statistics to measure the performance of players and teams from positional data. The capabilities of the tool involve preprocessing the match data, extraction of features, visualization of player and team performance. It also has built-in data mining techniques, such as association rule mining and subgroup discovery.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III
PublisherSpringer
Pages786-789
ISBN (Electronic)978-3-030-46133-1
ISBN (Print)978-3-030-46132-4
DOIs
Publication statusPublished - 30 Apr 2020
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2019 - Hubland campus University of Würzburg, Würzburg, Germany
Duration: 16 Sep 201920 Sep 2019
https://ecmlpkdd2019.org/

Publication series

NameLecture notes in computer science
Volume11908

Conference

ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2019
Abbreviated titleECML-PKDD 2019
CountryGermany
CityWürzburg
Period16/09/1920/09/19
Internet address

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  • Cite this

    Coutinho, J. C., Moreira, J., & Rebelo de Sá, C. (2020). UnFOOT - Unsupervised Football Analytics Tool. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III (pp. 786-789). (Lecture notes in computer science; Vol. 11908). Springer. https://doi.org/10.1007/978-3-030-46133-1_52