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