Abstract: A computing system retrieves tracking data from a data store. The tracking data includes a plurality of frames of data for a plurality of events across a plurality of seasons. The computing system converts the tracking data into a plurality of graph-based representations. A graph neural network learns to generate an action prediction for each player in each frame of the tracking data. The computing system generates a trained graph neural network based on the learning. The computing system receives target tracking data for a target event. The target tracking data includes a plurality of target frames. The computing system converts the target tracking data to a plurality of target graph-based representations. Each graph-based representation corresponds to a target frame of the plurality of target frames. The computing system generates, via the trained graph neural network, an action prediction for each player in each target frame.