  Drafting in Magic the Gathering is a sub-game within a larger trading card
game, where several players progressively build decks by picking cards from a
common pool. Drafting poses an interesting problem for game and AI research due
to its large search space, mechanical complexity, multiplayer nature, and
hidden information. Despite this, drafting remains understudied, in part due to
a lack of high-quality, public datasets. To rectify this problem, we present a
dataset of over 100,000 simulated, anonymized human drafts collected from
Draftsim.com. We also propose four diverse strategies for drafting agents,
including a primitive heuristic agent, an expert-tuned complex heuristic agent,
a Naive Bayes agent, and a deep neural network agent. We benchmark their
ability to emulate human drafting, and show that the deep neural network agent
outperforms other agents, while the Naive Bayes and expert-tuned agents
outperform simple heuristics. We analyze the accuracy of AI agents across the
timeline of a draft, and describe unique strengths and weaknesses for each
approach. This work helps to identify next steps in the creation of humanlike
drafting agents, and can serve as a benchmark for the next generation of
drafting bots.
