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Drafting, i.e., the selection of a subset of items from a larger candidateset, is a key element of many games and related problems. It encompasses teamformation in sports or e-sports, as well as deck selection in many modern cardgames. The key difficulty of drafting is that it is typically not sufficient tosimply evaluate each item in a vacuum and to select the best items. Theevaluation of an item depends on the context of the set of items that werealready selected earlier, as the value of a set is not just the sum of thevalues of its members - it must include a notion of how well items go together.  In this paper, we study drafting in the context of the card game Magic: TheGathering. We propose the use of a contextual preference network, which learnsto compare two possible extensions of a given deck of cards. We demonstratethat the resulting network is better able to evaluate card decks in this gamethan previous attempts.

Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking