Prediction of turkish super league match results using supervised machine learning techniques

Ahmet Emin Saricaoğlu; Abidin Aksoy; Tolga Kaya

doi:10.1007/978-3-030-23756-1_34

Prediction of turkish super league match results using supervised machine learning techniques

Ahmet Emin Saricaoğlu, Abidin Aksoy^*, 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

5 Citations (Scopus)

Abstract

Over the last number of years, sports analytics has become more popular in supporting personnel decisions, evaluating player and team performances, and predicting game results in various sports. One of the most traditional sports, football is also modernizing its ways based on sports analytics techniques. The purpose of this study is to propose a football match prediction model for Turkish Super League (TSL) using supervised machine learning techniques. To do this, based on the TSL data of last five years (2013 to 2018), game result prediction models were established using classification techniques including logistics regression, linear and quadratic discriminant analyses, K-nearest neighbors, support vector machines, and random forests. An ensemble of 10 models based on seven different techniques is suggested.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Publisher	Springer Verlag
Pages	273-280
Number of pages	8
ISBN (Print)	9783030237554
DOIs	https://doi.org/10.1007/978-3-030-23756-1_34
Publication status	Published - 2020
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey Duration: 23 Jul 2019 → 25 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1029
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2019
Country/Territory	Turkey
City	Istanbul
Period	23/07/19 → 25/07/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Ensemble modeling
Football analytics
Machine learning
Match result prediction
Supervised learning

Access to Document

10.1007/978-3-030-23756-1_34

Cite this

Saricaoğlu, A. E., Aksoy, A., & Kaya, T. (2020). Prediction of turkish super league match results using supervised machine learning techniques. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference (pp. 273-280). (Advances in Intelligent Systems and Computing; Vol. 1029). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_34

Saricaoğlu, Ahmet Emin ; Aksoy, Abidin ; Kaya, Tolga. / Prediction of turkish super league match results using supervised machine learning techniques. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A.Cagri Tolga. Springer Verlag, 2020. pp. 273-280 (Advances in Intelligent Systems and Computing).

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abstract = "Over the last number of years, sports analytics has become more popular in supporting personnel decisions, evaluating player and team performances, and predicting game results in various sports. One of the most traditional sports, football is also modernizing its ways based on sports analytics techniques. The purpose of this study is to propose a football match prediction model for Turkish Super League (TSL) using supervised machine learning techniques. To do this, based on the TSL data of last five years (2013 to 2018), game result prediction models were established using classification techniques including logistics regression, linear and quadratic discriminant analyses, K-nearest neighbors, support vector machines, and random forests. An ensemble of 10 models based on seven different techniques is suggested.",

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Saricaoğlu, AE, Aksoy, A & Kaya, T 2020, Prediction of turkish super league match results using supervised machine learning techniques. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Advances in Intelligent Systems and Computing, vol. 1029, Springer Verlag, pp. 273-280, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Turkey, 23/07/19. https://doi.org/10.1007/978-3-030-23756-1_34

Prediction of turkish super league match results using supervised machine learning techniques. / Saricaoğlu, Ahmet Emin; Aksoy, Abidin; Kaya, Tolga.
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A.Cagri Tolga. Springer Verlag, 2020. p. 273-280 (Advances in Intelligent Systems and Computing; Vol. 1029).

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

TY - GEN

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AU - Saricaoğlu, Ahmet Emin

AU - Aksoy, Abidin

AU - Kaya, Tolga

PY - 2020

Y1 - 2020

N2 - Over the last number of years, sports analytics has become more popular in supporting personnel decisions, evaluating player and team performances, and predicting game results in various sports. One of the most traditional sports, football is also modernizing its ways based on sports analytics techniques. The purpose of this study is to propose a football match prediction model for Turkish Super League (TSL) using supervised machine learning techniques. To do this, based on the TSL data of last five years (2013 to 2018), game result prediction models were established using classification techniques including logistics regression, linear and quadratic discriminant analyses, K-nearest neighbors, support vector machines, and random forests. An ensemble of 10 models based on seven different techniques is suggested.

AB - Over the last number of years, sports analytics has become more popular in supporting personnel decisions, evaluating player and team performances, and predicting game results in various sports. One of the most traditional sports, football is also modernizing its ways based on sports analytics techniques. The purpose of this study is to propose a football match prediction model for Turkish Super League (TSL) using supervised machine learning techniques. To do this, based on the TSL data of last five years (2013 to 2018), game result prediction models were established using classification techniques including logistics regression, linear and quadratic discriminant analyses, K-nearest neighbors, support vector machines, and random forests. An ensemble of 10 models based on seven different techniques is suggested.

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KW - Football analytics

KW - Machine learning

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A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A.Cagri

PB - Springer Verlag

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Saricaoğlu AE, Aksoy A, Kaya T. Prediction of turkish super league match results using supervised machine learning techniques. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC, editors, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Springer Verlag. 2020. p. 273-280. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23756-1_34

Prediction of turkish super league match results using supervised machine learning techniques

Abstract

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Bibliographical note

Keywords

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