Predicting Drafted Deck Strength for "Magic: the Gathering"

2026-07-06

AI Summary

This study addresses the challenge of predicting deck strength in Magic: The Gathering draft scenarios, where decisions must be made under incomplete information and complex card synergies. To tackle this problem, we propose a sequential encoder model that introduces, for the first time, a set-based, context-aware card embedding method to effectively capture combinatorial effects within pick sequences. Leveraging a large-scale dataset of real draft logs, our approach establishes the first learnable benchmark for deck strength prediction, significantly outperforming conventional linear models. The proposed framework provides a data-driven solution that enhances draft decision-making by accurately modeling the nuanced interactions among selected cards throughout the drafting process.

Abstract

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.

Problem

• Magic: the Gathering
• Draft
• deck strength prediction
• combinatorial synergies
• partial information

Innovation

• set-contextualized embeddings
• combinatorial synergies
• draft outcome prediction
• encoder-based model
• partial information decision-making