  We present a fully convolutional neural network architecture that is capable
of estimating full probability surfaces of potential passes in soccer, derived
from high-frequency spatiotemporal data. The network receives layers of
low-level inputs and learns a feature hierarchy that produces predictions at
different sampling levels, capturing both coarse and fine spatial details. By
merging these predictions, we can produce visually-rich probability surfaces
for any game situation that allows coaches to develop a fine-grained analysis
of players' positioning and decision-making, an as-yet little-explored area in
sports. We show the network can perform remarkably well in the estimation of
pass success probability, and present how it can be adapted easily to approach
two other challenging problems: the estimation of pass-selection likelihood and
the prediction of the expected value of a pass. Our approach provides a novel
solution for learning a full prediction surface when there is only a
single-pixel correspondence between ground-truth outcomes and the predicted
probability map. The flexibility of this architecture allows its adaptation to
a great variety of practical problems in soccer. We also present a set of
practical applications, including the evaluation of passing risk at a player
level, the identification of the best potential passing options, and the
differentiation of passing tendencies between teams.
