A Data-Driven Simulator for Assessing Decision-Making in Soccer

Decision-making is one of the crucial factors in soccer (association football). The current focus is on analyzing data sets rather than posing “what if” questions about the game. We propose simulation-based methods that allow us to answer these questions. To avoid simulating complex human physics and ball interactions, we use data to build machine learning models that form the basis of an event-based soccer simulator. This simulator is compatible with the OpenAI GYM API. We introduce tools that allow us to explore and gather insights about soccer, like (1) calculating the risk/reward ratios for sequences of actions, (2) manually defining playing criteria, and (3) discovering strategies through Reinforcement Learning.