  Scientifically evaluating soccer players represents a challenging Machine
Learning problem. Unfortunately, most existing answers have very opaque
algorithm training procedures; relevant data are scarcely accessible and almost
impossible to generate. In this paper, we will introduce a two-part solution:
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. Combining those two architectures allows
one to evaluate Soccer Players directly from a live camera without large
datasets constraints. We term our new approach Expected Discounted Goal (EDG),
as it represents the number of goals a team can score or concede 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.
