Image courtesy of Scryfall.com
Embedding MTG Cards: A Tattered Mummy Case Study
If you’ve ever built a card catalog for your collection or curated a deck archetype, you’ve felt the pull of a simple question: how similar is this card to that one? Embeddings give us a powerful answer. By transforming card features—text, mana cost, color, and more—into a vector space, you can cluster essentially endless card data into meaningful groups. Today we zoom in on a compact black creature from Game Night 2019 to illustrate how a short, unassuming card can illuminate big-picture relationships in MTG. 🧙♂️🔥
What makes Tattered Mummy special enough to anchor a case study?
Let’s pull the card data from its core details and see how they spark grouping decisions in embeddings. Tattered Mummy is a Creature — Zombie Jackal with a mana cost of {1}{B} and a 2-mana converted mana cost (cmc). Its power and toughness sit at 1/2, a modest body that nonetheless carries a compelling ability: When this creature dies, each opponent loses 2 life. It’s a common rarity card from the 2019 Game Night box (GN2), illustrated by Slawomir Maniak, and it appears in both paper and MTGO formats. The flavor text—“The dead who wander beyond the safety of the city crave only to spread their curse.”—sets a moody tone that aligns with classic black-aligned themes: inevitability, attrition, and the sting of life loss. 💎⚔️
From an embeddings perspective, every attribute is a potential signal. The token’s identity as Black mana (color identity B) immediately positions it with other black creatures and life-drain or death-trigger synergies. Its creature type—Zombie Jackal—adds a layer of taxonomy that can help group it with other “Zombie” or “Jackal” creatures, which often share flavor and mechanical motifs. The ability text provides semantic hooks: “dies” and “lose life” appear in many black-focused lines, making it a natural neighbor to cards with death-trigger or life-loss payoffs. The set and rarity further refine neighborhood structure in embeddings, nudging this card toward commons with similar accessibility and play patterns. 🧙♂️🎲
How embeddings capture the essence of a card like this
In practice, an embedding model can fuse several feature streams into a single vector:
- Textual features: Oracle text and flavor text reveal mechanics and mood. Phrases like “When this creature dies” and “each opponent loses 2 life” map to a semantic space where life-loss triggers and death-trigger interactions cluster together.
- Structural features: Mana cost, color identity, card type, power/toughness, and set provide rigid anchors in the space. A {B} mana cost and a 1/2 body often share neighborhoods with other low-cost, early-game Black creatures or ambush attackers.
- Rarity and print context: Common cards from a shared release window tend to cluster, reflecting both mechanical density and collector-facing perceptions of value or playability.
- Flavor and artist cues: The flavor text and artwork invite a style-based grouping—grim, desert-dark vibes that white-bleed into the “undead roaming” trope; these aesthetic vectors can help embeddings capture non-mechanical similarities that matter for thematic deck-building and collection curation.
When you run embeddings on a curated MTG dataset, Tattered Mummy often sits near other low-cost black creatures with death-related effects or life-losing payoffs. It’s not just about “this card does life loss” in a vacuum; it’s about where life-loss cards live in vector space relative to other mechanics—how they rhyme with hand disruption, drain, or attrition strategies, and how flavor supports (or contradicts) these mechanics. 🧙♂️💎
Practical takeaways for builders and curators
If you’re a deck builder or archivist, here are concrete ways embeddings help you organize and discover:
- Find near neighbors: Discover cards that share death-trigger or life-loss motifs, even if they differ in color or name. This helps you draft or build shells that maximize synergy rather than chasing obvious color pie lanes.
- Discover thematic clusters: Group by flavor text or art direction to assemble cohesive “lore decks” or aesthetic playgroups for your LGS nights or content creation.
- Assist set analysis: Compare reprint pools (like GN2’s Game Night 2019) to see how mechanical density and rarity trends shift between print runs, aiding price scouting and inventory decisions.
- Enhance search experiences: If you’re building a catalog interface, embeddings enable semantic search: “life-loss triggers,” “death-dies effects,” or “black budget threats” return a broader set of relevant cards beyond keyword matches.
Flavor as a guide and a constraint
The flavor text of Tattered Mummy speaks in a hushed, cursed whisper that echoes classic undead tropes. In embedding space, this adds a cultural dimension to clustering—cards from similar undead or necrotic themes often drift toward adjacent regions of the vector field. When you pair the mechanics with flavor, you unlock a richer, more intuitive discovery experience for players who crave both function and story. “The dead who wander beyond the safety of the city crave only to spread their curse.”—a reminder that MTG’s design threads story through even the most modest commons. 🎨🧙♂️
“We don’t just collect cards; we curate experiences. Embeddings turn a stack of staples into a living map of possibilities.” — MTG data explorer
In practice, you can experiment with embeddings by building a small demo set around Game Night 2019 or around a particular color identity. Start with features like mana cost and color, then layer in textual features from oracle text and flavor. Observe how the space groups Tattered Mummy with other Black humans-and-undeads, or how it sits near other low-cost, death-triggered or life-loss cards. You’ll notice not only mechanical neighbors but also thematic kin—cards that feel right at home in the same night’s list or the same casual primer article. 🧙♂️🔥
For readers who are curious about practical crossover with real-world products, a comfortable desk setup can sharpen your MTG workflow. If you’re deep in the hobby, consider pairing organizing sessions with a neon-themed mouse pad to keep your hands and eyes in peak form while you explore large card catalogs. This is where the product world nudges the play world: a bright, responsive surface keeps you sprinting through datasets and deck ideas alike. 🎲💎
To recap: a single, unassuming card—Tattered Mummy—demonstrates how a compact data point can anchor a meaningful embedding-based analysis. Its black color identity, death-trigger payoff, and flavorful undead mood provide rich signals for clustering similar cards, discovering new synergies, and curating a cohesive collection. The lesson translates beyond one card: when you design embeddings, you’re not just measuring text and stats—you’re shaping how players think about mechanics, relationships, and stories across the entire multiverse. ⚔️🧙♂️