Image courtesy of Scryfall.com
Grouping Similar MTG Cards with Embeddings: A Glint Case Study
If you’ve ever alphabetized a binder by color and then stared at the sea of blue cards wondering which ones truly belong together, you’re not alone. In the modern era of data-driven gaming, we can push binder organization from “mostly right” to “curated and clever” by borrowing the language of embeddings from the world of AI. Think of embeddings as tiny, mathy fingerprints for each card that capture not just mana costs or colors, but the vibe, mechanics, and even flavor that make a card feel part of a family. 🧙♂️🔥💎
What embeddings actually capture in MTG cards
Embeddings translate a card’s attributes into a vector in a high-dimensional space. For a blue instant like Glint from Dragons of Tarkir, you’d fold in details such as the mana cost ({1}{U}), the instant speed, the target rules (buff a creature you control, grant hexproof until end of turn), and the creature-targeting nuance. You’d also weigh color identity (U), rarity (common), and set context (DTK, Dragons of Tarkir) to position Glint within a broader cluster of similar spells—those that protect, empower, or hiccup an essential play. The result is a position in a multidimensional map where visually distant cards in the literal sense can be neighbors in the embedding space because they share strategic DNA. 🧙♂️🎲
In practice, you’d build a feature vector that might include: color indicators, mana cost components, card type, subtypes, keywords, targeting rules, and textual flavor. Then you run a clustering algorithm—k-means, DBSCAN, or a neat hierarchical approach—to group cards by proximity in that vector space. The goal isn’t to replace human judgment, but to surface relationships you might miss in a narrow categorical view: for example, discovering that a handful of blue instants with temporary hexproof or temporary power boosts cluster with other protection-oriented tricks across sets. ⚔️
Glint as a representative teachable moment
Glint is an instant spell with a modest mana cost of {1}{U} and a straightforward but potent effect: target creature you control gets +0/+3 and gains hexproof until end of turn. It’s blue through and through, leaning on hexproof as a shield to weather opposing spells or removal while you push in for value. The card’s flavor text—“Rakshasa waste no opportunity to display their wealth and power, even in the midst of a sorcerous duel”—adds a layer of narrative flourish that a good embedding model can capture if you include flavor text as part of the feature set. It’s a neat reminder that an often-ignored axis in grouping is the story behind the card—the aesthetic that makes you smile when you draw it in a late-game moment. 💎🎨
In a practical sense, Glint sits in a space with other blue instants or protection-oriented plays from across eras, not limited to Dragons of Tarkir. When you embed and cluster, the card’s proximity to other “temporary shield and buff” or “temporary hexproof” tools becomes obvious, regardless of whether those cards share a set, have the same mana curve, or even appear in the same color wheel. The deterministic rule is: if the play pattern and text tend to co-occur in decks, the embedding should reflect that kinship. 🧙♂️
Why this matters for players, collectors, and designers
For players building decks, embeddings offer a way to discover synergy, not just by card name or attribute, but by behavior in your games. If you’re drafting or building a control shell, a clustering view can highlight underappreciated tools—cards that quietly fit your strategy alongside flashier options. If you’re collecting or tabulating price and rarity, embeddings can reveal subtle relationships between editions, foils, and reprints that affect value and desirability. Glint, a common instant with a foil option and a modest market presence, benefits from this lens too: you can identify which non-foil and foil blue instants cluster around it in terms of utility, not just price. The Scryfall data shows a modest USD price around a few pennies with room for foil, which matters when you’re considering long-tail sets and future reprints in a cluster. 🧲🔥
From a design perspective, embedding-based grouping informs new card design decisions by highlighting patterns across sets. If a designer notices that a particular protection motif tends to cluster with bounce or flicker effects, they might explore expanding that design space with fresh verbs (protect, shield, phase, blink) to create a recognizable but evolving playstyle. Dragons of Tarkir, with its distinctive wedge of dragon and clan lore, provides fertile ground for testing how flavor, mechanic, and identity align—and how those alignments shift when cards move into modern or eternal formats. ⚔️🎨
Practical steps you can try at home
- Collect a diverse card sample: blue spells across several sets, including instants that grant conditional protection or temporary stats boosts.
- Extract features: mana cost, color identity, card type, target rules, and a line or two of text for the effect description. Include flavor text if you want to capture storytelling nuance.
- Convert features into embeddings: use a text- or feature-based embedding model, blending numeric costs with textual descriptors.
- Run a clustering algorithm: group cards by proximity, then review clusters to identify meaningful families (e.g., protective instants, hexproof builders, tempo plays).
- Validate with play experience: test whether cards in the same cluster actually perform similarly in decks and games.
For the curious reader who wants to see more hands-on, you can explore practical deck-building prompts that leverage this approach—like selecting cards with overlapping protective motifs to shore up a blue control shell or to discover underseen tools that push your deck’s tempo to the next level. And if you’re charting your collection for fun or for value, these groups become a handy map, guiding you to detect overlooked foils and non-foils that share a core function, even when they hail from different eras. 🧭
As you experiment with clustering Glint alongside its blue peers, you’ll notice a recurring theme: a small, efficient spell can be a pivotal pivot point in a game, just as a precise embedding can be a pivot in a data-driven card map. The joy of MTG is the constant reconnection between play, lore, and design—and embeddings give us a fresh lens to enjoy all three. Whether you’re sparking nostalgia for DTK’s art and flavor or finessing a modern deck with an eye toward future reprints, there’s always a new neighborhood to discover in the multiverse. 💫