Using an autoencoder trained simultaneously on soft diagrams (pitch control differentials), pass success probability surfaces, and action-prediction tasks to produce a low-dimensional bottleneck representation of any pitch frame. This enables geometry-aware clustering of game situations — 95 discovered situations across 30 pitch zones, each defined by combinations of defensive distance, number of teammates ahead, and ball positioning relative to defensive lines.
Multi-task autoencoder: input = spatial surfaces from 360 data; output = reconstruct inputs + predict next action. The bottleneck layer = situation representation. Cluster the representations to discover distinct game situations. Then compute per-situation metrics (xT, pass success, etc.) for disaggregated team evaluation.