Classifying the Success of Transfers Made by Turkish Super-League Teams Using Advanced Machine Learning Techniques

Serhan Berke Erden; Ünal Berke Kaymak; Tolga Kaya

doi:10.1007/978-3-031-39777-6_32

Classifying the Success of Transfers Made by Turkish Super-League Teams Using Advanced Machine Learning Techniques

Serhan Berke Erden^*, Ünal Berke Kaymak, 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

It is a matter of controversy whether the transfers made in the football industry are efficient or not. The aim of the study is to explore the efficiency of transfers made in the football industry using machine learning techniques. In this context, a methodology to model the success of transfers based on Turkish Super League data is suggested. In the modelling processes, the data of the transfers taken from the Tranfermarkt website were used. The target variable is created as binary and the classification problem is the consideration. Accordingly, the data of 16 teams and 2261 players in total were analysed using advanced machine learning methods. Results reveal that transfers of young and homegrown players are relatively more efficient compare to those of the others.

Original language	English
Title of host publication	Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference
Editors	Cengiz Kahraman, Irem Ucal Sari, Basar Oztaysi, Sezi Cevik Onar, Selcuk Cebi, A. Çağrı Tolga
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	262-268
Number of pages	7
ISBN (Print)	9783031397769
DOIs	https://doi.org/10.1007/978-3-031-39777-6_32
Publication status	Published - 2023
Event	Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference - Istanbul, Turkey Duration: 22 Aug 2023 → 24 Aug 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	759 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference
Country/Territory	Turkey
City	Istanbul
Period	22/08/23 → 24/08/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Classification
Football
Machine Learning
Transfer Efficiency
Turkish Super League

Access to Document

10.1007/978-3-031-39777-6_32

Cite this

Erden, S. B., Kaymak, Ü. B., & Kaya, T. (2023). Classifying the Success of Transfers Made by Turkish Super-League Teams Using Advanced Machine Learning Techniques. In C. Kahraman, I. U. Sari, B. Oztaysi, S. Cevik Onar, S. Cebi, & A. Ç. Tolga (Eds.), Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference (pp. 262-268). (Lecture Notes in Networks and Systems; Vol. 759 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-39777-6_32

Erden, Serhan Berke ; Kaymak, Ünal Berke ; Kaya, Tolga. / Classifying the Success of Transfers Made by Turkish Super-League Teams Using Advanced Machine Learning Techniques. Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference. editor / Cengiz Kahraman ; Irem Ucal Sari ; Basar Oztaysi ; Sezi Cevik Onar ; Selcuk Cebi ; A. Çağrı Tolga. Springer Science and Business Media Deutschland GmbH, 2023. pp. 262-268 (Lecture Notes in Networks and Systems).

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title = "Classifying the Success of Transfers Made by Turkish Super-League Teams Using Advanced Machine Learning Techniques",

abstract = "It is a matter of controversy whether the transfers made in the football industry are efficient or not. The aim of the study is to explore the efficiency of transfers made in the football industry using machine learning techniques. In this context, a methodology to model the success of transfers based on Turkish Super League data is suggested. In the modelling processes, the data of the transfers taken from the Tranfermarkt website were used. The target variable is created as binary and the classification problem is the consideration. Accordingly, the data of 16 teams and 2261 players in total were analysed using advanced machine learning methods. Results reveal that transfers of young and homegrown players are relatively more efficient compare to those of the others.",

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Erden, SB, Kaymak, ÜB & Kaya, T 2023, Classifying the Success of Transfers Made by Turkish Super-League Teams Using Advanced Machine Learning Techniques. in C Kahraman, IU Sari, B Oztaysi, S Cevik Onar, S Cebi & AÇ Tolga (eds), Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference. Lecture Notes in Networks and Systems, vol. 759 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 262-268, Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference, Istanbul, Turkey, 22/08/23. https://doi.org/10.1007/978-3-031-39777-6_32

Classifying the Success of Transfers Made by Turkish Super-League Teams Using Advanced Machine Learning Techniques. / Erden, Serhan Berke; Kaymak, Ünal Berke; Kaya, Tolga.
Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference. ed. / Cengiz Kahraman; Irem Ucal Sari; Basar Oztaysi; Sezi Cevik Onar; Selcuk Cebi; A. Çağrı Tolga. Springer Science and Business Media Deutschland GmbH, 2023. p. 262-268 (Lecture Notes in Networks and Systems; Vol. 759 LNNS).

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

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T1 - Classifying the Success of Transfers Made by Turkish Super-League Teams Using Advanced Machine Learning Techniques

AU - Erden, Serhan Berke

AU - Kaymak, Ünal Berke

AU - Kaya, Tolga

PY - 2023

Y1 - 2023

N2 - It is a matter of controversy whether the transfers made in the football industry are efficient or not. The aim of the study is to explore the efficiency of transfers made in the football industry using machine learning techniques. In this context, a methodology to model the success of transfers based on Turkish Super League data is suggested. In the modelling processes, the data of the transfers taken from the Tranfermarkt website were used. The target variable is created as binary and the classification problem is the consideration. Accordingly, the data of 16 teams and 2261 players in total were analysed using advanced machine learning methods. Results reveal that transfers of young and homegrown players are relatively more efficient compare to those of the others.

AB - It is a matter of controversy whether the transfers made in the football industry are efficient or not. The aim of the study is to explore the efficiency of transfers made in the football industry using machine learning techniques. In this context, a methodology to model the success of transfers based on Turkish Super League data is suggested. In the modelling processes, the data of the transfers taken from the Tranfermarkt website were used. The target variable is created as binary and the classification problem is the consideration. Accordingly, the data of 16 teams and 2261 players in total were analysed using advanced machine learning methods. Results reveal that transfers of young and homegrown players are relatively more efficient compare to those of the others.

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ER -

Erden SB, Kaymak ÜB, Kaya T. Classifying the Success of Transfers Made by Turkish Super-League Teams Using Advanced Machine Learning Techniques. In Kahraman C, Sari IU, Oztaysi B, Cevik Onar S, Cebi S, Tolga AÇ, editors, Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference. Springer Science and Business Media Deutschland GmbH. 2023. p. 262-268. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-39777-6_32

Classifying the Success of Transfers Made by Turkish Super-League Teams Using Advanced Machine Learning Techniques

Abstract

Publication series

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Bibliographical note

Keywords

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