O que 150 roguelike-deckbuilders dizem sobre wishlists

Roguelike deckbuilders are one of the most crowded corners of Steam right now — about 180 of them at last count. So we did something unglamorous: pulled the numbers on 150 of those games and went looking for what separates signal from superstition. The honest answer surprised us in a few places. A caveat up front: this is correlation on a single snapshot, not a law of nature, and we'll flag where the numbers get thin. And a timing note that matters — every count here was captured just before Next Fest, before the festival's wishlist surge, so read the absolute numbers as a pre-fest floor and the patterns, not the levels, as the takeaway. (Our most useful finding, it turned out, was catching the data as it was about to trick us — but we'll get to that.)

1. Wishlists are brutally top-heavy

The median game in the set has 360 wishlists. The mean is around 6,000 — and when the mean sits 17× above the median, you're not looking at a bell curve, you're looking at a power law. The top 10% of games hold roughly 85% of all the wishlists in the field.

Distribution of wishlists across the 150 games (lavender = median bucket, blue = mean bucket). The percentile ladder shows how fast the curve climbs at the very top.

If you're benchmarking your page against the breakout you saw on your timeline, you're benchmarking against the 1%. The realistic roguelike deckbuilder is a three-figure wishlist game.

2. Most "this tag sells" advice is noise

We tested every discriminating tag carried by at least eight games — 65 of them, after setting aside the three genre-defining tags (Roguelike, Deckbuilding, Roguelike Deckbuilder) that just describe the filter — for wishlist lift, and put a real statistical bar in front of each: a bootstrap confidence interval on the median that has to clear the dataset baseline before we'll call it real. Exactly three cleared it: Auto Battler (~4.1×), Sci-fi (~3.9×) and Turn-Based (~3.6×). The flashy outliers — the ones riding on a handful of split games — evaporated once we accounted for how noisy a median of a few skewed numbers really is.

Tag lift vs. the dataset median (log scale). ● solid = statistically stable; ○ faded = tentative (CI straddles the baseline). Green is above baseline, rose below.

With ~150 games and this much variance, a single tag almost never moves the median on its own. Tag roulette isn't a strategy.

3. Localization correlates hard — and it's mostly a trap

This one looks like a cheat code. English-only games sit at 0.53× the baseline median; games supporting German, Brazilian Portuguese or Traditional Chinese run 3–4.5× — while the most common sets (Simplified Chinese and Japanese, in roughly half the field) sit near baseline at ~1.4×. The lift tracks the languages that are expensive to commit to, not the ones nearly everyone already ships. Don't read it as "translate to win." It's almost entirely reverse causality — broad localization is a proxy for budget and ambition. The teams that can fund eight languages can also fund the capsule art, the trailer and the scope that actually drive wishlists.

Median wishlists by language support. The biggest lifts cluster on the less common, higher-effort localizations; the near-universal sets barely separate from baseline.

The languages are a symptom of a serious project, not the cause of its numbers.

4. The generic, casual lane is the soft one

We grouped the field by tag profile and plotted each neighbourhood's median wishlists against how crowded it is. The pattern that survives: the most generic corners — casual / relaxing builds and plain card-game ones — cluster near the bottom, while the strongest neighbourhood leans on a hook like replay value or thematic depth. It lines up with the tag drags above, where Relaxing, Minimalist and Puzzle all sit below baseline. One honest caveat: this niche is homogeneous enough that the clusters overlap heavily and reshuffle if you re-run them — so read the map, not the exact ranking, and treat them as rough neighbourhoods, not hard genres.

Each dot is a tag-profile neighbourhood — competition (cluster size) vs. performance (median wishlists). The generic, casual corners sit low; clusters overlap heavily, so treat them as rough groupings.

Generic doesn't pay. The most crowded, least-differentiated corners sit at the bottom — a real hook is where the wishlists live.

5. Check your data before you "discover" anything

The most useful finding wasn't about games at all. When we pulled follower counts and a third-party sales prediction, both showed a near-perfect 0.99 correlation with wishlists. Suspiciously perfect. It was arithmetic: for 128 of the 150 games, the follower count was exactly wishlists ÷ 12 — an imputed placeholder, not measured data. Only 22 games had genuinely independent followers, and on those the correlation fell to a normal 0.92. (Our own game, Rogue Reigns, is one of the measured ones — and sits below that line, converting wishlists to follows at about half the typical rate.)

Followers vs. wishlists (log–log). The faint grey dots are the artifact — 128 imputed games sitting exactly on wishlists ÷ 12. The coloured points are the 22 measured games; the dashed line is their fitted trend.

If we'd trusted the headline figure, we'd have published a confident post about a relationship that was just our own input divided by twelve. Wishlists turned out to be the only independent success signal in the whole export.

How we did it

150 of the ~180 roguelike deckbuilders on Steam, captured as a snapshot just before Steam Next Fest 2026 — i.e. before the festival's wishlist surge, so the absolute counts run lower than they would mid-fest. Because wishlists are power-law distributed, we used medians (never averages) and a bootstrap confidence interval to decide whether any "lift" was real or just small-sample noise. Tags and genres are treated as content metadata; wishlists as the one independent outcome. None of this is causal — read it as a map of where the field is dense, where it's thin, and which "best practices" survive contact with the numbers.