DraftFM: A FoundationModel for Day-Zero Drafting in Magic: The Gathering
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Computer Science > Machine Learning
arXiv:2608.19568 (cs)
[Submitted on 20 Aug 2026]
Title:DraftFM: A FoundationModel for Day-Zero Drafting in Magic: The Gathering
Authors:Brian Ward
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Abstract:Drafting a new Magic: The Gathering expansion begins before any pick from it has been observed: the complete card list is public, but the draft logs that supervised pick models train on do not yet exist. We study this day-zero regime directly. DraftFM is a discrete-choice policy that scores exactly the cards available in the current pack, conditioned on the drafted pool and the state of the draft. Every card enters as a frozen 775-dimensional function of its public card record, structured features and a fixed text embedding, with no card identities, set identities, or usage statistics anywhere in the model, so an unseen card is scored by the same machinery as a familiar one. A 1.6-million-parameter network fitted on 149 million human picks from 29 expansions predicts held-out picks in three expansions withheld in their entirety, reaching 50.8%, 60.4%, and 56.7% top-1 agreement, where uniform chance at the opening pick is about 7%. Refitted on all 32 observed expansions, the same architecture produced a card ranking for the then-unreleased set The Hobbit, sealed with its complete cryptographic provenance and published roughly 36 hours before the set became draftable on MTG Arena. The sealed ranking agrees with six independent expert reviewers roughly as much as those reviewers agree with one another. Evaluation against realized outcomes is committed to a follow-on note, whatever it shows.
| Comments: | 28 pages, 6 figures, 13 tables. Sealed pre-registered forecast published ~36h before release; scoring note to follow. Also on SSRN (7257098). Code: this https URL Sealed forecast: this https URL (tag draftfm-v1.0) Weights: this https URL Data: this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.6; I.2.1 |
| Cite as: | arXiv:2608.19568 [cs.LG] |
| (or arXiv:2608.19568v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.19568
arXiv-issued DOI via DataCite (pending registration)
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