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

TL;DR

This paper introduces a contextual preference network to predict human card choices in Magic: The Gathering, effectively capturing the importance of set context in drafting decisions and outperforming previous methods.

Contribution

The paper presents a novel contextual preference network that models drafting decisions considering set context, improving evaluation accuracy over prior approaches.

Findings

The network better predicts human drafting choices.

It outperforms previous deck evaluation methods.

Context-aware modeling enhances draft quality prediction.

Abstract

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.