In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space. STEVE only relies on freely available information about the matches teams played in the past. These vectors can serve as input to various machine learning tasks. Evaluating on the task of team market value estimation, STEVE outperforms all its competitors. Moreover, we use STEVE for similarity search and to rank soccer teams.
@article{arxiv.1908.00698,
title = {Soccer Team Vectors},
author = {Robert Müller and Stefan Langer and Fabian Ritz and Christoph Roch and Steffen Illium and Claudia Linnhoff-Popien},
journal= {arXiv preprint arXiv:1908.00698},
year = {2020}
}
Comments
11 pages, 1 figure; This paper was presented at the 6th Workshop on Machine Learning and Data Mining for Sports Analytics at ECML/PKDD 2019, W\"urzburg, Germany, 2019