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A Machine Learning Ensembling Approach to Predicting Transfer Values

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Abstract

Predicting transfer values of association football players, despite its importance, has been studied in a limited way in the literature. The existing approaches have mainly focused on explanatory models that cannot be used in predicting future values. In this paper, we propose a method where we fuse in-game performance data, player popularity metrics from the web and actual transfer values. The method uses a model ensembling approach to capture different dynamics in transfer market. The proposed approach outperforms the state-of-the art models and commonly used benchmarks.

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Authors and Affiliations

  1. Enskai Ltd., Sofia, 1000, Bulgaria

    Ayse Elvan Aydemir

  2. Graduate School of Informatics, METU, Ankara, 06800, Turkey

    Ayse Elvan Aydemir, Tugba Taskaya Temizel & Alptekin Temizel

Authors
  1. Ayse Elvan Aydemir
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  2. Tugba Taskaya Temizel
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  3. Alptekin Temizel
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Correspondence to Ayse Elvan Aydemir.

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Aydemir, A.E., Taskaya Temizel, T. & Temizel, A. A Machine Learning Ensembling Approach to Predicting Transfer Values. SN COMPUT. SCI. 3, 201 (2022). https://doi.org/10.1007/s42979-022-01095-z

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