Predicting Human Card Selection in Magic: The Gathering with Contextual
  Preference Ranking
  Drafting, i.e., the selection of a subset of items from a larger candidate
set, is a key element of many games and related problems. It encompasses team
formation in sports or e-sports, as well as deck selection in many modern card
games. The key difficulty of drafting is that it is typically not sufficient to
simply evaluate each item in a vacuum and to select the best items. The
evaluation of an item depends on the context of the set of items that were
already selected earlier, as the value of a set is not just the sum of the
values 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: The
Gathering. We propose the use of a contextual preference network, which learns
to compare two possible extensions of a given deck of cards. We demonstrate
that the resulting network is better able to evaluate card decks in this game
than previous attempts.
