Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning

Paul Garnier

Abstract: Scientifically evaluating soccer players represents a challenging Machine Learning problem. Unfor- tunately, most existing answers have very opaque algorithm training procedures; relevant data are scarcely accessible and almost impossible to gener- ate. In this paper, we will introduce a two-part solu- tion: an open-source Player Tracking model and a new approach to evaluate these players based solely on Deep Reinforcement Learning, without human data training nor guidance. Our tracking model was trained in a supervised fashion on datasets we will also release, and our Evaluation Model relies only on simulations of virtual soccer games. Com- bining those two architectures allows one to evalu- ate Soccer Players directly from a live camera with- out large datasets constraints. We term our new ap- proach Expected Discounted Goal (EDG), as it rep- resents the number of goals a team can score or con- cede from a particular state. This approach leads to more meaningful results than the existing ones that are based on real-world data, and could easily be extended to other sports.