PassNet: Learning pass probability surfaces from single-location labels. An architecture for visually-interpretable soccer analytics

ICLR 2020 Anonymous

We propose a fully convolutional network architecture that is able to estimate a full surface of pass probabilities from single-location labels derived from high frequency spatio-temporal data of professional soccer matches. The network is able to perform remarkably well from low-level inputs by learning a feature hierarchy that produces predictions at different sampling levels that are merged together to preserve both coarse and fine detail... (read more)

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