I trained a simple neural network on 77k professional Dota 2 matches to learn which heroes tend to be picked together during drafts.
From that, I extracted 32-dimensional hero embeddings - the model's "understanding" of each character - and projected them into 2D. The result is a surprisingly coherent similarity map, showing how the game's structure emerges naturally from data.
The post includes visuals, code, and an explanation of how it works.
From that, I extracted 32-dimensional hero embeddings - the model's "understanding" of each character - and projected them into 2D. The result is a surprisingly coherent similarity map, showing how the game's structure emerges naturally from data.
The post includes visuals, code, and an explanation of how it works.
Happy to discuss details or answer any questions!