Predicting Stunfisk Meta Decks with Machine Learning

In TCG ·

Stunfisk card art from Unified Minds (SM11-67)

Image courtesy of TCGdex.net

ML insights for Stunfisk in the current meta

Machine learning reshapes how players and collectors think about deck viability, and Stunfisk provides a perfect case study. In the Unified Minds era, this unassuming Basic Lightning-type Pokémon arrives with a curious energy cost and a trap-ready attack that can swing a game when the math lines up just right. By analyzing data points like HP, retreat cost, type interactions, and the specific conditions of its Attack Trap Bolt, a model can forecast how often Stunfisk would realistically land a decisive hit in a crowded meta. The result is not a crystal ball, but a data-driven lens on where this Common card might shine or stumble in Expanded play—and where it might become a sleeper pick for budget-conscious builders.

Stunfisk at a glance: card data that matters

  • Name: Stunfisk
  • Set: Unified Minds (SM11)
  • Card number: sm11-67
  • Rarity: Common
  • Type: Lightning
  • Stage: Basic
  • HP: 90
  • Attack: Trap Bolt — cost Fighting; damage 30+, with the effect “If, before doing damage, your opponent’s Active Pokémon has more remaining HP than this Pokémon, this attack does 30 more damage.”
  • Weakness: Fighting (×2)
  • Resistance: Metal (−20)
  • Retreat cost: 1
  • Illustrator: MAHOU
  • Legal in formats: Expanded (Standard legality is False for this card in the current rotation window)

From an analytical standpoint, the Trap Bolt ability is the heartbeat of Stunfisk’s strategic profile. The attack’s base 30 damage is modest, but the potential for +30 damage based on HP comparison adds a dynamic edge that depends on tempo, board state, and sequencing. The Fighting energy cost for Trap Bolt is a notable friction point: it means you must plan energy acceleration or accept that short-term confusion when trying to pay the cost with non-Fighting sources. The dual-edged sword of a Fighting weakness against common Fighting-type threats in many metas creates a consistent risk, yet clever play and matchups can tilt the scales in Expanded formats where Stunfisk isn’t fighting for the spotlight alone.

Why ML sees Stunfisk as a niche but intriguing piece

  • Strategic volatility: The conditional damage boost turns Trap Bolt into a stall-to-offense tool—useful in control or stall-y technical builds that aim to outwait faster decks.
  • Deck-compatibility signals: ML models factor in HP, retreat cost, and energy requirements to identify archetypes where a low-cost, defensively positioned Basic can survive long enough to set up a late-game knockout.
  • Market and rarity dynamics: As a Common card from a widely printed set, Stunfisk offers a low barrier to entry for players exploring machine-learned meta forecasts, with holo variants occasionally buoyed by collector interest despite the common rarity.

In practice, a model would weigh Stunfisk’s Expanded-legal status against its vulnerability to Fighting-type aggression and the probability that the opponent’s Active Pokémon has higher remaining HP when you want to trigger the 30-damage bump. The result: Stunfisk often lands in a tier of “situational pick” that can surprise metagames when paired with supporting Pokémon and Trainer lines that help manage energy, HP, and tempo.

Deck-building implications guided by data

For players exploring ML-informed deck building, Stunfisk points toward a few concrete considerations. First, ensure you’ve accounted for the energy engine needed to pay its Trap Bolt cost. If your shell favors Fighting energy or can reliably accelerate it, Stunfisk can occasionally reach that sweet spot where 60 damage or more (30 base plus 30 conditional) lands exactly when an opponent overcommits to a single big attacker. Second, because Stunfisk’s weakness to Fighting is a persistent risk, you’ll want to integrate typing and protection that can weather cross-type threats—either by buffering with Rugged Helmet-like effects, or by choosing matchups where Fighting types are less dominant. Finally, think about the tempo you want: a mid-range, stall-inspired approach can leverage Stunfisk’s HP and retreat cost to buy a turn or two of setup while you assemble a more aggressive plan behind it.

From a forecasting standpoint, ML would flag synergy opportunities with trainer lines and pivot points that reduce the cost friction for Trap Bolt, while also highlighting countermeasures to typical Fighting-type decks. In other words, Stunfisk is a small but meaningful data point in a broader predictive model that aims to map niche cards into realistic, repeatable outcomes across varied meta slices.

Market vibes: pricing and collectability snapshot

The pricing data for Stunfisk from Unified Minds paints a pragmatic picture for budget-minded collectors and builders. CardMarket reports a low average around 0.06 EUR for non-holo copies, with holo variants hovering around 0.29 EUR on average, and a gentle uptrend indicated by the “trend” metrics. On the U.S. side, TCGPlayer shows a low-price floor near 0.03 USD for non-holo copies, with mid prices around 0.17 USD and a marketPrice around 0.15 USD; reverse-holo copies fetch higher ranges, reflecting collector interest. For a card that is common in a set known for wide distribution, these numbers align with expectations—Stunfisk remains accessible, while holo finishes provide a modest upside for those who value aesthetics and display appeal.

ML-driven estimates of future value would factor in supply stability and the possibility of reprint dynamics that could alter price trends. The art by MAHOU adds to the collectability narrative, especially for players who appreciate compact, punchy visuals on bench staples. In short, Stunfisk offers a practical entry point for new players and a low-risk, watchful hold for collectors who want to track a card’s price evolution as meta whispers shift across formats.

As data continues to illuminate deck viability, Stunfisk demonstrates how even a humbler card can hold strategic relevance in certain niches. The interplay of HP, a conditional damage attack, and a vulnerability to common fighting threats makes it a thoughtful candidate for ML-informed experimentation—especially in formats where expanded play keeps a wider array of toolkits in rotation. ⚡🔥💎

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