  Analysis of the popular expected goals (xG) metric in soccer has determined
that a (slightly) smaller number of high-quality attempts will likely yield
more goals than a slew of low-quality ones. This observation has driven a
change in shooting behavior. Teams are passing up on shots from outside the
penalty box, in the hopes of generating a better shot closer to goal later on.
This paper evaluates whether this decrease in long-distance shots is warranted.
Therefore, we propose a novel generic framework to reason about decision-making
in soccer by combining techniques from machine learning and artificial
intelligence (AI). First, we model how a team has behaved offensively over the
course of two seasons by learning a Markov Decision Process (MDP) from event
stream data. Second, we use reasoning techniques arising from the AI literature
on verification to each team's MDP. This allows us to reason about the efficacy
of certain potential decisions by posing counterfactual questions to the MDP.
Our key conclusion is that teams would score more goals if they shot more often
from outside the penalty box in a small number of team-specific locations. The
proposed framework can easily be extended and applied to analyze other aspects
of the game.
