A Machine Learning Ensembling Approach to Predicting Transfer Values

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.
SN Computer Science


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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.