RIGGEDROYALE
AnalyzePlayersDecksCardsData LabMethodology

This material is unofficial and is not endorsed by Supercell. For more information see Supercell's Fan Content Policy. Despite our name, Clash Royale is not actually rigged: Supercell itself explains that matchmaking is based on Trophies and King Level, not on your cards or your wallet. "Rigged" here is satire — we just measure how unlucky you got.

Public methodology·Public player board·Top decks·Public correlations·Fan content under the Supercell Fan Content Policy.

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THE DECK LAB

SCORE ANY DECK AGAINST THE LIVE META

Build a deck and the model scores it against the live meta, or find single-card swaps that raise its win rate. Model Rankings shows the model's current top decks.

1 · Build the deck

EVO
EVO
Cards
8/8
Avg elixir
3.4
Special
2/3
Evo
2/2
Hero
0/2

Tap a card's EVO or HERO pill for a special form — 1 Evo, 1 Hero, plus 1 Wild slot either can take (3 max). The model scores evolutions; hero forms are shown for the build.

Card library

113 cards

2 · Score it

Score the current deck against the live meta.

500
50 · faster6,000 · full panel

Time

~3s est.

Actual coverage

48.3%

More decks increase meta coverage and runtime. Coverage comes from current live deck frequencies; times remain rough estimates.

UPGRADE PRIORITY ESTIMATE

Uses recent card usage, a modeled one-level effect, and relative upgrade-cost weights. It does not know your gold, wild cards, or exact inventory.

Pro

Included with Pro — sign in to get started. See what Pro includes.

THE MODEL'S OWN RANKING

TOP DECKS ACCORDING TO THE WIN-CHANCE MODEL

RankedLadder

Scoring the meta with the win-chance model — first build can take a minute…

ANALYST NOTESHow to read this data
  • The model board scores each popular deck against the frequency-weighted meta panel. It reads matchups, not piloting difficulty.
  • The deck lab and the experimental ML upgrade run on the same model and the same meta panel, and are also exposed as commercial API endpoints under /api/v1.
  • Descriptive data. Small samples, mode mix, patches, card levels, and selection bias can all move a deck before the wider player base confirms it.

594 popular decks screened; 10 distinct picks each scored against 100 frequency-weighted meta decks / League 7 / model v5-e2aa7bfc0c76 / built Jul 14, 03:33 PM

01

EVO
EVO
0.2% of scored meta playbest 80%worst 27%volatility 27

58.8%

model WR vs meta

02

EVO
EVO
0.1% of scored meta playbest 84%worst 22%volatility 33

58.6%

model WR vs meta

03

EVO
EVO
1.3% of scored meta playbest 83%worst 30%volatility 25

57.8%

model WR vs meta

04

EVO
EVO
0.0% of scored meta playbest 78%worst 29%volatility 24

57.7%

model WR vs meta

05

EVO
EVO
0.0% of scored meta playbest 83%worst 30%volatility 25

57.6%

model WR vs meta

06

EVO
EVO
0.0% of scored meta playbest 82%worst 35%volatility 25

57.4%

model WR vs meta

07

EVO
EVO
0.1% of scored meta playbest 78%worst 26%volatility 27

57.3%

model WR vs meta

08

EVO
EVO
0.1% of scored meta playbest 80%worst 22%volatility 23

57.1%

model WR vs meta

09

EVO
EVO
0.0% of scored meta playbest 78%worst 36%volatility 19

56.9%

model WR vs meta

10

EVO
EVO
0.7% of scored meta playbest 87%worst 19%volatility 27

56.8%

model WR vs meta