Clustering English Premier League Referees Using Unsupervised Machine Learning Techniques

Mustafa İspa; Ufuk Yarışan; Tolga Kaya

doi:10.1007/978-3-030-85577-2_27

Clustering English Premier League Referees Using Unsupervised Machine Learning Techniques

Mustafa İspa^*, Ufuk Yarışan, Tolga Kaya

^*Corresponding author for this work

Department of Management Engineering

Istanbul Technical University

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

Abstract

Technological developments have affected the decision-making phase in football matches. The viewing pleasure of fans and the result of the match is a matter of debate determined by the referees. Throughout time, the objectiveness of referees was questioned by the technological tools. Video Assistant Referee (VAR) is an example approach to ensure whether the referees react to similar positions of different matches in the same manner or not. One of the problems of this topic is the controversial decisions of some referees leading to unexpected results. In this research, as a different approach to the referees’ objectivity problem, referees are tried to be classified based on the statistical outcome of the matches using unsupervised machine learning techniques. Meaningful clusters should not be found to be able to state the referees are objective. This study is conducted on 10-years English Premier League between 2009–2018 data. Principal Component Analysis is going to be used for grouping the variables to perform exploratory data analysis. K-Means, hierarchical clustering, and Fuzzy C-Means are going to be used for dividing the referees into various subgroups. R programming language is used for examining data. In conclusion of the analysis, four different referee groups are defined.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
Editors	Cengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	230-237
Number of pages	8
ISBN (Print)	9783030855765
DOIs	https://doi.org/10.1007/978-3-030-85577-2_27
Publication status	Published - 2022
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey Duration: 24 Aug 2021 → 26 Aug 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	308
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2021
Country/Territory	Turkey
City	Istanbul
Period	24/08/21 → 26/08/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Clustering
English Premier League
Football
Machine learning
Referee
Unsupervised learning

Access to Document

10.1007/978-3-030-85577-2_27

Cite this

İspa, M., Yarışan, U., & Kaya, T. (2022). Clustering English Premier League Referees Using Unsupervised Machine Learning Techniques. In C. Kahraman, S. Cebi, S. Cevik Onar, B. Oztaysi, A. C. Tolga, & I. U. Sari (Eds.), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference (pp. 230-237). (Lecture Notes in Networks and Systems; Vol. 308). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85577-2_27

İspa, Mustafa ; Yarışan, Ufuk ; Kaya, Tolga. / Clustering English Premier League Referees Using Unsupervised Machine Learning Techniques. Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. editor / Cengiz Kahraman ; Selcuk Cebi ; Sezi Cevik Onar ; Basar Oztaysi ; A. Cagri Tolga ; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. pp. 230-237 (Lecture Notes in Networks and Systems).

@inproceedings{43f49f1a45584bca9245d03935cf8484,

title = "Clustering English Premier League Referees Using Unsupervised Machine Learning Techniques",

abstract = "Technological developments have affected the decision-making phase in football matches. The viewing pleasure of fans and the result of the match is a matter of debate determined by the referees. Throughout time, the objectiveness of referees was questioned by the technological tools. Video Assistant Referee (VAR) is an example approach to ensure whether the referees react to similar positions of different matches in the same manner or not. One of the problems of this topic is the controversial decisions of some referees leading to unexpected results. In this research, as a different approach to the referees{\textquoteright} objectivity problem, referees are tried to be classified based on the statistical outcome of the matches using unsupervised machine learning techniques. Meaningful clusters should not be found to be able to state the referees are objective. This study is conducted on 10-years English Premier League between 2009–2018 data. Principal Component Analysis is going to be used for grouping the variables to perform exploratory data analysis. K-Means, hierarchical clustering, and Fuzzy C-Means are going to be used for dividing the referees into various subgroups. R programming language is used for examining data. In conclusion of the analysis, four different referee groups are defined.",

keywords = "Clustering, English Premier League, Football, Machine learning, Referee, Unsupervised learning",

author = "Mustafa İspa and Ufuk Yarı{\c s}an and Tolga Kaya",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2021 ; Conference date: 24-08-2021 Through 26-08-2021",

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doi = "10.1007/978-3-030-85577-2_27",

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editor = "Cengiz Kahraman and Selcuk Cebi and {Cevik Onar}, Sezi and Basar Oztaysi and Tolga, {A. Cagri} and Sari, {Irem Ucal}",

booktitle = "Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference",

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İspa, M, Yarışan, U & Kaya, T 2022, Clustering English Premier League Referees Using Unsupervised Machine Learning Techniques. in C Kahraman, S Cebi, S Cevik Onar, B Oztaysi, AC Tolga & IU Sari (eds), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Lecture Notes in Networks and Systems, vol. 308, Springer Science and Business Media Deutschland GmbH, pp. 230-237, International Conference on Intelligent and Fuzzy Systems, INFUS 2021, Istanbul, Turkey, 24/08/21. https://doi.org/10.1007/978-3-030-85577-2_27

Clustering English Premier League Referees Using Unsupervised Machine Learning Techniques. / İspa, Mustafa; Yarışan, Ufuk; Kaya, Tolga.
Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. ed. / Cengiz Kahraman; Selcuk Cebi; Sezi Cevik Onar; Basar Oztaysi; A. Cagri Tolga; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. p. 230-237 (Lecture Notes in Networks and Systems; Vol. 308).

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

TY - GEN

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AU - Yarışan, Ufuk

AU - Kaya, Tolga

PY - 2022

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N2 - Technological developments have affected the decision-making phase in football matches. The viewing pleasure of fans and the result of the match is a matter of debate determined by the referees. Throughout time, the objectiveness of referees was questioned by the technological tools. Video Assistant Referee (VAR) is an example approach to ensure whether the referees react to similar positions of different matches in the same manner or not. One of the problems of this topic is the controversial decisions of some referees leading to unexpected results. In this research, as a different approach to the referees’ objectivity problem, referees are tried to be classified based on the statistical outcome of the matches using unsupervised machine learning techniques. Meaningful clusters should not be found to be able to state the referees are objective. This study is conducted on 10-years English Premier League between 2009–2018 data. Principal Component Analysis is going to be used for grouping the variables to perform exploratory data analysis. K-Means, hierarchical clustering, and Fuzzy C-Means are going to be used for dividing the referees into various subgroups. R programming language is used for examining data. In conclusion of the analysis, four different referee groups are defined.

AB - Technological developments have affected the decision-making phase in football matches. The viewing pleasure of fans and the result of the match is a matter of debate determined by the referees. Throughout time, the objectiveness of referees was questioned by the technological tools. Video Assistant Referee (VAR) is an example approach to ensure whether the referees react to similar positions of different matches in the same manner or not. One of the problems of this topic is the controversial decisions of some referees leading to unexpected results. In this research, as a different approach to the referees’ objectivity problem, referees are tried to be classified based on the statistical outcome of the matches using unsupervised machine learning techniques. Meaningful clusters should not be found to be able to state the referees are objective. This study is conducted on 10-years English Premier League between 2009–2018 data. Principal Component Analysis is going to be used for grouping the variables to perform exploratory data analysis. K-Means, hierarchical clustering, and Fuzzy C-Means are going to be used for dividing the referees into various subgroups. R programming language is used for examining data. In conclusion of the analysis, four different referee groups are defined.

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A2 - Kahraman, Cengiz

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A2 - Oztaysi, Basar

A2 - Tolga, A. Cagri

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İspa M, Yarışan U, Kaya T. Clustering English Premier League Referees Using Unsupervised Machine Learning Techniques. In Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga AC, Sari IU, editors, Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Springer Science and Business Media Deutschland GmbH. 2022. p. 230-237. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-85577-2_27

Clustering English Premier League Referees Using Unsupervised Machine Learning Techniques

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this