UnFOOT - Unsupervised Football Analytics Tool

José Carlos Coutinho; João Moreira; Claudio Rebelo de Sá

doi:10.1007/978-3-030-46133-1_52

UnFOOT - Unsupervised Football Analytics Tool

José Carlos Coutinho, João Moreira, Claudio Rebelo de Sá

Datamanagement & Biometrics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases
Subtitle of host publication	European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III
Publisher	Springer
Pages	786-789
ISBN (Electronic)	978-3-030-46133-1
ISBN (Print)	978-3-030-46132-4
DOIs	https://doi.org/10.1007/978-3-030-46133-1_52
Publication status	Published - 30 Apr 2020
Event	European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2019 - Hubland campus University of Würzburg, Würzburg, Germany Duration: 16 Sep 2019 → 20 Sep 2019 https://ecmlpkdd2019.org/

Publication series

Name	Lecture notes in computer science
Volume	11908

Conference

Conference	European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2019
Abbreviated title	ECML-PKDD 2019
Country	Germany
City	Würzburg
Period	16/09/19 → 20/09/19
Internet address	https://ecmlpkdd2019.org/

Access to Document

10.1007/978-3-030-46133-1_52

https://link.springer.com/chapter/10.1007/978-3-030-46133-1_52

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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

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title = "UnFOOT - Unsupervised Football Analytics Tool",

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.",

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year = "2020",

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Coutinho, JC, 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. Lecture notes in computer science, vol. 11908, Springer, pp. 786-789, European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2019, Würzburg, Germany, 16/09/19. https://doi.org/10.1007/978-3-030-46133-1_52

UnFOOT - Unsupervised Football Analytics Tool. / Coutinho, José Carlos; Moreira, João; Rebelo de Sá, Claudio.

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III. Springer, 2020. p. 786-789 (Lecture notes in computer science; Vol. 11908).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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