# AI solutions for drafting in Magic: the Gathering

arXiv: https://arxiv.org/abs/2009.00655 · HTML: https://mlsanity.com/paper/2009.00655/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 8 of 8)

- [Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking](https://mlsanity.com/paper/2105.11864/) (2021)
- [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)
- [Predicting Drafted Deck Strength for "Magic: the Gathering"](https://mlsanity.com/paper/2607.04782/) (2026)
- [An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis](https://mlsanity.com/paper/2402.00031/) (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 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.