Towards the Construction of a Soccer Foundation Model Using Simulation Data

Abstract

Recent advancements in large foundation models have spanned various domains, including natural language processing, autonomous driving, and multivariate time series forecasting. Meanwhile, applied sports analysis has become widespread, with a particular focus on constructing quantitative evaluation methods for players and teams through the modeling of match situations. Despite this, a soccer foundation model capable of performing various tasks within a single model remains unexplored. This study explores a potential soccer foundation model by applying a multivariate time series prediction architecture for forecasting soccer trajectory data. We propose using log data from soccer simulation leagues for training, taking into account 1) the small scale of real trajectory data and 2) the effectiveness of synthetic data in constructing foundation models as indicated by previous research. Furthermore, we evaluate the effectiveness of the embedding representations by qualitatively comparing their similarities with actual soccer trajectories, confirming their applicability in downstream tasks.