Magic: The Gathering Drafting Data

Drafting is a game mode in collectible card games where players build their decks from a restricted pool of cards. Throughout one draft, players are offered a series of selections, from which they must build their deck. Although drafting is a popular game variant in \textit{Magic: The Gathering}, few machine learning models have been developed to learn card selection strategies. We model drafts with a Siamese neural network that is trained on real-world data and predicts human expert selection. Our model learns an embedding space of preferences by comparing cards in the context of a deck. We examine card representations, evaluate our model on a large-scale dataset, and show that our model achieves 45\% zero-shot drafting accuracy on cards that are completely unseen in training. This suggests that the model understands general card semantics and is able to evaluate their strength. In addition, we provide an in-depth exploration of the embedding space. We find that card embeddings capture a significant amount of interpretable information, such as the sizes of decks, and the strengths of individual cards. We also find that the preference-conditioned embedding space learns the similarity of cards, which can enable downstream tasks in the future.