A Machine Learning Ensembling Approach to Predicting Transfer Values

Date

2022-03-01

Author

Aydemir, Ayşe Elvan
Taşkaya Temizel, Tuğba
Temizel, Alptekin

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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 diferent dynamics in transfer market. The proposed approach outperforms the state-of-the art models and commonly used benchmarks.

Subject Keywords

Predictive modelling , Football analytics , Player valuation , Data fusion , Model ensembling

URI

https://link.springer.com/article/10.1007/s42979-022-01095-z
https://hdl.handle.net/11511/97145

Journal

SN Computer Science

DOI

https://doi.org/10.1007/s42979-022-01095-z

Collections

Graduate School of Informatics, Article

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Citation Formats

A. E. Aydemir, T. Taşkaya Temizel, and A. Temizel, “A Machine Learning Ensembling Approach to Predicting Transfer Values,” SN Computer Science, vol. 3, no. 201, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://link.springer.com/article/10.1007/s42979-022-01095-z.