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

1 Citation (Scopus)

3 Downloads (Pure)

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
Editors	Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet
Publisher	Springer
Pages	786-789
Number of pages	4
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/Territory	Germany
City	Würzburg
Period	16/09/19 → 20/09/19
Internet address	https://ecmlpkdd2019.org/

Keywords

22/2 OA procedure

Access to Document

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

Coutinho2020_Chapter_UnFOOTUnsupervisedFootballAnalFinal published version, 268 KBLicence: Taverne

Cite this

Coutinho, J. C., Moreira, J., & Rebelo de Sá, C. (2020). UnFOOT - Unsupervised Football Analytics Tool. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), 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

Coutinho, José Carlos ; Moreira, João ; Rebelo de Sá, Claudio. / UnFOOT - Unsupervised Football Analytics Tool. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III. editor / Ulf Brefeld ; Elisa Fromont ; Andreas Hotho ; Arno Knobbe ; Marloes Maathuis ; Céline Robardet. Springer, 2020. pp. 786-789 (Lecture notes in computer science).

@inproceedings{57c4476666b7433bbc8bf897e657dafe,

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.",

keywords = "22/2 OA procedure",

author = "Coutinho, {Jos{\'e} Carlos} and Jo{\~a}o Moreira and {Rebelo de S{\'a}}, Claudio",

year = "2020",

month = apr,

day = "30",

doi = "10.1007/978-3-030-46133-1_52",

language = "English",

isbn = "978-3-030-46132-4",

series = "Lecture notes in computer science",

publisher = "Springer",

pages = "786--789",

editor = "Ulf Brefeld and Elisa Fromont and Andreas Hotho and Arno Knobbe and Marloes Maathuis and C{\'e}line Robardet",

booktitle = "Machine Learning and Knowledge Discovery in Databases",

address = "Netherlands",

note = "European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2019, ECML-PKDD 2019 ; Conference date: 16-09-2019 Through 20-09-2019",

url = "https://ecmlpkdd2019.org/",

}

Coutinho, JC, Moreira, J & Rebelo de Sá, C 2020, UnFOOT - Unsupervised Football Analytics Tool. in U Brefeld, E Fromont, A Hotho, A Knobbe, M Maathuis & C Robardet (eds), 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, Bavaria, 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. ed. / Ulf Brefeld; Elisa Fromont; Andreas Hotho; Arno Knobbe; Marloes Maathuis; Céline Robardet. 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

TY - GEN

T1 - UnFOOT - Unsupervised Football Analytics Tool

AU - Coutinho, José Carlos

AU - Moreira, João

AU - Rebelo de Sá, Claudio

PY - 2020/4/30

Y1 - 2020/4/30

N2 - 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.

AB - 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.

KW - 22/2 OA procedure

U2 - 10.1007/978-3-030-46133-1_52

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

M3 - Conference contribution

SN - 978-3-030-46132-4

T3 - Lecture notes in computer science

SP - 786

EP - 789

BT - Machine Learning and Knowledge Discovery in Databases

A2 - Brefeld, Ulf

A2 - Fromont, Elisa

A2 - Hotho, Andreas

A2 - Knobbe, Arno

A2 - Maathuis, Marloes

A2 - Robardet, Céline

PB - Springer

T2 - European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2019

Y2 - 16 September 2019 through 20 September 2019

ER -

Coutinho JC, Moreira J, Rebelo de Sá C. UnFOOT - Unsupervised Football Analytics Tool. In Brefeld U, Fromont E, Hotho A, Knobbe A, Maathuis M, Robardet C, editors, 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). https://doi.org/10.1007/978-3-030-46133-1_52

UnFOOT - Unsupervised Football Analytics Tool

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this