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

arXiv: https://arxiv.org/abs/2105.11864 · HTML: https://mlsanity.com/paper/2105.11864/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 6 of 6)

- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [The Many AI Challenges of Hearthstone](https://mlsanity.com/paper/1907.06562/) (2019)
- [Speech Emotion Recognition via Contrastive Loss under Siamese Networks](https://mlsanity.com/paper/1910.11174/) (2019)
- [AI solutions for drafting in Magic: the Gathering](https://mlsanity.com/paper/2009.00655/) (2020)
- [Evolutionary Approach to Collectible Card Game Arena Deckbuilding using Active Genes](https://mlsanity.com/paper/2001.01326/) (2020)

## Built on by (most cited first, 7 of 7)

- [Contrastive Learning of Preferences with a Contextual InfoNCE Loss](https://mlsanity.com/paper/2407.05898/) (2024)
- [Learning With Generalised Card Representations for "Magic: The Gathering"](https://mlsanity.com/paper/2407.05879/) (2024)
- [UrzaGPT: LoRA-Tuned Large Language Models for Card Selection in Collectible Card Games](https://mlsanity.com/paper/2508.08382/) (2025)
- [A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems](https://mlsanity.com/paper/2107.04438/) (2021)
- [Neural Network-based Information Set Weighting for Playing Reconnaissance Blind Chess](https://mlsanity.com/paper/2407.05864/) (2024)
- [A Taxonomy of Collectible Card Games from a Game-Playing AI Perspective](https://mlsanity.com/paper/2410.06299/) (2024)
- [DraftFM: A FoundationModel for Day-Zero Drafting in Magic: The Gathering](https://mlsanity.com/paper/2608.19568/) (2026)

## Abstract (arXiv)

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.