Automated classification of passing in football

Michael Horton; Joachim Gudmundsson; Sanjay Chawla; Joël Estephan

doi:10.1007/978-3-319-18032-8_25

Automated classification of passing in football

Michael Horton, Joachim Gudmundsson, Sanjay Chawla, Joël Estephan

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

16 Scopus citations

Abstract

A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game. In this paper we consider the problem of producing an automated system to make the same evaluation of passes. We present a model that constructs numerical predictor variables from spatiotemporal match data using feature functions based on methods from computational geometry, and then learns a classification function from labelled examples of the predictor variables. In addition, we show that the predictor variables computed using methods from computational geometry are among the most important to the learned classifiers.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
Editors	Tru Cao, Ee-Peng Lim, Tu-Bao Ho, Zhi-Hua Zhou, Hiroshi Motoda, David Cheung
Publisher	Springer Verlag
Pages	319-330
Number of pages	12
ISBN (Print)	9783319180311
DOIs	https://doi.org/10.1007/978-3-319-18032-8_25
State	Published - 2015
Externally published	Yes
Event	19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 - Ho Chi Minh City, Viet Nam Duration: May 19 2015 → May 22 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9078
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
Country/Territory	Viet Nam
City	Ho Chi Minh City
Period	05/19/15 → 05/22/15

Access to Document

10.1007/978-3-319-18032-8_25

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Horton, M., Gudmundsson, J., Chawla, S., & Estephan, J. (2015). Automated classification of passing in football. In T. Cao, E-P. Lim, T-B. Ho, Z-H. Zhou, H. Motoda, & D. Cheung (Eds.), Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings (pp. 319-330). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9078). Springer Verlag. https://doi.org/10.1007/978-3-319-18032-8_25

Horton, Michael ; Gudmundsson, Joachim ; Chawla, Sanjay et al. / Automated classification of passing in football. Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings. editor / Tru Cao ; Ee-Peng Lim ; Tu-Bao Ho ; Zhi-Hua Zhou ; Hiroshi Motoda ; David Cheung. Springer Verlag, 2015. pp. 319-330 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Automated classification of passing in football",

abstract = "A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game. In this paper we consider the problem of producing an automated system to make the same evaluation of passes. We present a model that constructs numerical predictor variables from spatiotemporal match data using feature functions based on methods from computational geometry, and then learns a classification function from labelled examples of the predictor variables. In addition, we show that the predictor variables computed using methods from computational geometry are among the most important to the learned classifiers.",

author = "Michael Horton and Joachim Gudmundsson and Sanjay Chawla and Jo{\"e}l Estephan",

note = "Funding Information: S. Chawla—Sanjay Chawla{\textquoteright}s research was supported by ARC Discovery and Linkage grants. Funding Information: J. Gudmundsson—Joachim Gudmundsson was supported by the Australian Research Council (project numbers DP150101134 and FT100100755). Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 ; Conference date: 19-05-2015 Through 22-05-2015",

year = "2015",

doi = "10.1007/978-3-319-18032-8_25",

language = "English",

isbn = "9783319180311",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "319--330",

editor = "Tru Cao and Ee-Peng Lim and Tu-Bao Ho and Zhi-Hua Zhou and Hiroshi Motoda and David Cheung",

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}

Horton, M, Gudmundsson, J, Chawla, S & Estephan, J 2015, Automated classification of passing in football. in T Cao, E-P Lim, T-B Ho, Z-H Zhou, H Motoda & D Cheung (eds), Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9078, Springer Verlag, pp. 319-330, 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015, Ho Chi Minh City, Viet Nam, 05/19/15. https://doi.org/10.1007/978-3-319-18032-8_25

Automated classification of passing in football. / Horton, Michael; Gudmundsson, Joachim; Chawla, Sanjay et al.
Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings. ed. / Tru Cao; Ee-Peng Lim; Tu-Bao Ho; Zhi-Hua Zhou; Hiroshi Motoda; David Cheung. Springer Verlag, 2015. p. 319-330 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9078).

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

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AU - Horton, Michael

AU - Gudmundsson, Joachim

AU - Chawla, Sanjay

AU - Estephan, Joël

N1 - Funding Information: S. Chawla—Sanjay Chawla’s research was supported by ARC Discovery and Linkage grants. Funding Information: J. Gudmundsson—Joachim Gudmundsson was supported by the Australian Research Council (project numbers DP150101134 and FT100100755). Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game. In this paper we consider the problem of producing an automated system to make the same evaluation of passes. We present a model that constructs numerical predictor variables from spatiotemporal match data using feature functions based on methods from computational geometry, and then learns a classification function from labelled examples of the predictor variables. In addition, we show that the predictor variables computed using methods from computational geometry are among the most important to the learned classifiers.

AB - A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game. In this paper we consider the problem of producing an automated system to make the same evaluation of passes. We present a model that constructs numerical predictor variables from spatiotemporal match data using feature functions based on methods from computational geometry, and then learns a classification function from labelled examples of the predictor variables. In addition, we show that the predictor variables computed using methods from computational geometry are among the most important to the learned classifiers.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 319

EP - 330

BT - Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings

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ER -

Horton M, Gudmundsson J, Chawla S, Estephan J. Automated classification of passing in football. In Cao T, Lim E-P, Ho T-B, Zhou Z-H, Motoda H, Cheung D, editors, Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings. Springer Verlag. 2015. p. 319-330. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-18032-8_25

Automated classification of passing in football

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