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


Erden H. S., Kaymak Ü. B., Kaya T.

Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference, İstanbul, Turkey, 22 - 24 August 2023, vol.759 LNNS, pp.262-268 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 759 LNNS
  • Doi Number: 10.1007/978-3-031-39777-6_32
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.262-268
  • Keywords: Classification, Football, Machine Learning, Transfer Efficiency, Turkish Super League
  • Istanbul Technical University Affiliated: Yes

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.