Using Graph Convolutional Networks (GCN) with a Word2Vec-inspired training approach to generate dense vector representations ("embeddings") of football actions from 360 data. Each action is represented as a graph (players as nodes, relationships as edges) and trained to predict surrounding actions in a sequence — the same principle behind Word2Vec in NLP. Similar actions (same phase, similar player configurations) cluster together in embedding space, enabling few-shot classification of phases of play, similarity search across different matches, and even football "analogies" (action A is to action B as action C is to ?).
(1) Represent each action as a graph using 360 data: nodes = players with position features, edges = relationships (teammate/opponent). (2) Train a GCN to predict the surrounding actions in a possession sequence (Word2Vec's skip-gram approach applied to football). (3) The trained model produces a fixed-length vector embedding for any action. (4) Phase classification: label ~500 actions manually (30 minutes of work), train a simple classifier on embeddings → classify all remaining actions. (5) Similarity search: find the most similar historical actions to a given action via nearest-neighbor in embedding space. (6) Analogies: perform vector arithmetic (action1 - action2 + action3) to find actions that satisfy football analogies.
Key insight: embeddings capture player configuration context even when the number of visible players differs between frames. Two "pass to keeper" actions with 10 vs. 20 visible players can still be recognized as similar because the graph structure captures the relevant relationships.