# Predicting Drafted Deck Strength for "Magic: the Gathering"

arXiv: https://arxiv.org/abs/2607.04782 · HTML: https://mlsanity.com/paper/2607.04782/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Equinox: neural networks in JAX via callable PyTrees and filtered transformations](https://mlsanity.com/paper/2111.00254/) (2021)
- [EmbeddingGemma: Powerful and Lightweight Text Representations](https://mlsanity.com/paper/2509.20354/) (2025)
- [Mastering Strategy Card Game (Hearthstone) with Improved Techniques](https://mlsanity.com/paper/2303.05197/) (2023)
- [AI solutions for drafting in Magic: the Gathering](https://mlsanity.com/paper/2009.00655/) (2020)
- [Learning With Generalised Card Representations for "Magic: The Gathering"](https://mlsanity.com/paper/2407.05879/) (2024)

## Built on by (most cited first, 1 of 1)

- [DraftFM: A FoundationModel for Day-Zero Drafting in Magic: The Gathering](https://mlsanity.com/paper/2608.19568/) (2026)

## Abstract (arXiv)

Many real-world games do not admit a fixed, compact rule set: instead, their dynamics are defined by interactions among a large and often evolving collection of game pieces, making general-purpose policy learning impractical. Magic: the Gathering (MTG) exemplifies this setting, where the cards themselves define and alter gameplay rules, strategic constraints, and long-term outcomes, while the pool of available cards is ever-changing. We study Draft, a constrained deck-building format of MTG in which eight players make 39-45 sequential selections from semi-random packs to construct a 40-card deck under partial information. By isolating the card selection process from gameplay, Draft provides a tractable yet non-trivial setting for studying decision-making driven by combinatorial card synergies. We propose an encoder-based model that produces set-contextualized card embeddings to encode the draft decision sequence, with a consistent improvement over linear baselines on large-scale real-world data, establishing a first learned benchmark for outcome prediction in MTG Draft. Our code is available at github.com/akulen/MtGDraftEncoder.