Image courtesy of Scryfall.com
Embeddings and Card Clustering in MTG
When we talk about organizing MTG cards by similarity, we’re really talking about turning a sprawling tabletop catalog into a living, navigable map. Embeddings—the numeric fingerprints produced by language models and feature extractors—let us translate the rich, text-heavy world of card text, lore, and mechanics into a vector space where proximity equals similarity. A practical test case for this concept is the green ramp spell Grow from the Ashes, a Foundations-era sorcery with a kick that rewards you with extra land plays. 🧙♂️🔥 Its spell data—{2}{G} mana cost, Kicker {2}, and the line about putting basic lands onto the battlefield—provides a compact but potent signal for clustering: color identity, mana investment, and the ramp mechanic all point toward a “green ramp” blob in embedding space. 💎⚔️
What makes this card a compelling exemplar is that it sits at the intersection of several canonical MTG clustering signals: color (green), card type (sorcery), primary function (land ramp), and escalation via kicker. In a vector space built from text and structured features, such cards cluster near other ramp spells, even across core sets and printings. The Foundations reprint (fdn) places it among green staples that historically pull the game’s tempo forward by expanding the mana base. It’s a clean, well-behaved signal that a good embedding can latch onto, making it a natural anchor for experiments in card similarity. 🧪🎨
What features feed embeddings for MTG cards?
There are several layers you can stitch together to form a rich representation:
- Oracle text and mana cost – The literal gameplay signals for what the card does and how much it costs to cast. Grow from the Ashes uses a modest {2}{G}, with a kicker that alters its battlefield impact dramatically when paid.
- Color identity and mana archetype – Green-aligned ramp sits in a distinct cluster from red removal or blue card draw. The color signal is often the strongest cue for clustering in MTG data.
- Card type, rarity, and set metadata – A common sorcery in a core set like Foundations nudges you toward a broad ramp family, while higher rarities or multicolor hybrids tilt toward other clusters.
- Flavor text, lore snippets, and artist notes – While not always present in slotted features, these elements can enrich embeddings when available, nudging semantically related cards closer together.
- Printed vs. digital nuances – Printings, reprints, and set-specific mechanics (like Kicker) add structure that helps disambiguate otherwise similar lines of text.
A practical workflow for MTG card embeddings
To turn this into a working clustering pipeline, you can follow a pragmatic sequence:
- Aggregate card data from reliable sources (Oracle text, mana cost, color identity, card type, set, rarity, and keywords like Kicker).
- Create text fields by concatenating oracle text with flavor text and key metadata, then tokenize for embedding.
- Choose an embedding model suited for short-form text and structured data (for example, a transformer-based sentence encoder fine-tuned on card text or a hybrid approach that combines text with categorical features).
- Normalize numeric features (cmc, color counts, kicker cost) and concatenate them with the textual embeddings to form a final feature vector.
- Apply a clustering algorithm such as K-means, DBSCAN, or HDBSCAN to identify natural groupings—green ramp, card draw engines, removal, and more.
- Evaluate clusters against known archetypes (ramps, mana acceleration tools, color pairs) and iterate on feature weighting to improve coherence.
In practice, a card like Grow from the Ashes serves as a strong test beacon because it toggles a basic ramp action with a conditional kicker. When not kicked, you fetch a single basic land; when kicked, you fetch two. That duality translates neatly into a vector that shifts toward “single-land ramp” versus “double-land ramp” subclusters, highlighting how embeddings can capture both static attributes (color, mana cost) and dynamic decisions (kicker, number of lands searched). 🧙♂️💡
“If your goal is to map MTG’s vast card landscape, you don’t just look at what a card does—you look at how it changes the game’s tempo and resource graph. Embeddings let us quantify those shifts and discover groups we might miss with eyes alone.”
Why this card makes a good case study for clustering research
Grow from the Ashes is a near-ideal exemplar for several reasons. First, its kicker mechanic introduces a binary conditional that clearly differentiates two related but distinct outcomes—one-land vs two-land searches. That split is a classic cue for path-dependent clustering, where the same spell can belong to two subtly different archetypes depending on how it’s cast. Second, being a Foundations core-set release underlines the enduring utility of ramp spells across eras; it ties into a broader narrative about how green’s bread-and-butter advantage (mana acceleration) persists, making it a stable anchor point across datasets. And third, its straightforward text and well-defined action reduce noise, enabling cleaner embeddings that reveal meaningful structure rather than incidental variance. 🔎🧭
From a design perspective, the card reveals how mechanics can be encoded for machines. The Kicker tag is a keyword that, when embedded, tends to pull the card closer to other kicker-enabled spells, regardless of set or flavor. This makes Grow from the Ashes a practical lens for discussing how to weigh mechanical signals versus flavor or lore when building a clustering model. The result is a more robust, fan-friendly map of MTG’s ecology—one that can highlight niche groups like “green ramp with kicker” or “two-land fetchers that accelerate the board.” 🎲🎨
Gameplay intuition meets data insight
For players, the embedding-based view translates into actionable insights: which cards tend to support similar strategies, how to pair ramp and Landfall-y effects, and which card designs create the most cohesive clusters across formats. If you’re building a toolbox for deck-building, this kind of clustering helps you suggest complementary cards, refine archetype definitions, and even surface edge cases that aren’t obvious from raw card text alone. And yes, you’ll also discover delightful curiosities—like how a single green sorcery with a flexible kicker can sit confidently beside other ramp staples like Cultivate or Kodama’s Reach, forming a dense, land-rich neighborhood in vector space. 🧙♂️🔥💎