47M+
Games modeled
Real recorded 1v1 games behind the win-chance model.
PUBLIC CASE NOTES
HOW THE ANALYSIS WORKS
Rigged Royale reads your recent battle log and asks one question: was your matchup draw unusually hostile, or just normal variance? It answers with a 0–100 score built from a win-chance model, a live meta baseline, and an evidence-weighted caution rule. No access to Supercell's matchmaking code, so it never claims to prove rigging — it measures how far your sample sits from a fair draw, and shows every number behind the read.
10,000
Battles archived
Player battle logs merged into the analysis archive.
198
Players on file
Public case files that clear the ranking minimum.
101
Fair-draw decks
Ranked meta decks forming the live fairness baseline.
97%
Meta coverage
Share of sampled play the panel spans, rebuilt every 7 days.
01 - THE PIPELINE
From battle log to verdict, in four moves
Every report walks the same path. Nothing here is a hand-written rulebook — each step is measured against real data.
01
Sample
200
battles / report
Every readable 1v1 battle from the API is kept and merged across visits. Team battles are excluded — two decks, one result, can't be graded as a matchup.
02
Model
P(win)
per matchup, 0–1
An ML model reads both full 8-card decks, levels, towers and bracket and outputs your win chance. Trained on 47M real games, not intuition.
03
Fair draw
101
weighted opponents
Your win chance vs the opponents you faced is compared to your win chance vs the live meta a fair matchmaker would actually draw — weighted by real play frequency.
04
Verdict
0–100
50 = exactly fair
Three factors are scored, weighted by their own evidence, then pulled toward 50 on thin samples. Below 50 is harder than fair; above is softer.
02 - THE SCORE MATH, LIVE
Build a verdict yourself
This runs the real formula. A factor score is 50 + 50 × (gap / saturation), capped at 0 and 100; weights scale by evidence and renormalize; the blend is shrunk toward 50 by n / (n + 9). Drag the inputs and watch each rule fire.
THE EVIDENCE / DRAG TO TEST
THE VERDICT / LIVE
38/100
RIGGED-ISH
The strings are visible if you squint.
Matchup draw (59% weight)32
Bad-game timing (41% weight)39
Card levels (0% weight)50
Blended 34.8 — evidence-weighted mix of the three factors before the caution shrink.
Keeps 82% of its distance from 50 at 40 battles. Track a player to grow this toward 100%.
Saturation. The matchup factor floors/maxes at a 25-point win-chance gap; timing at 18 points.
Evidence. Base weights 50/40/10% are starting points; a factor with thin evidence bleeds weight to the others.
Caution. Five battles keep only a sliver of their deviation; a hundred keep almost all of it. Tracking sharpens the read over time.
03 - THE FAIR-DRAW BASELINE
This is the live meta you're judged against
A win chance alone means nothing — a weak deck loses to everyone, and that isn't rigging. So your draw is compared to what your own deckshould expect against the decks people actually play. That panel is live, per bracket, and weighted by real frequency — not the players this site has analyzed, so one unlucky player can't drag “fair” toward their own bad draws.
Top 6 ranked decks in the current panel of 101, by the frequency weight a fair matchmaker would hand each one.
01
8.6%
draw weight
02
7.2%
draw weight
03
7.0%
draw weight
04
7.0%
draw weight
05
7.0%
draw weight
06
4.9%
draw weight
The matchup factor is your deck's average win chance against the opponents you faced, minus its average win chance against this exact weighted panel.
04 - THE WIN-CHANCE MODEL
Where measurement beats prediction
The model grades the whole deck — whether your cards can answer the opponent's win conditions — instead of guessing from one card pair. Predictions are symmetric: your win chance vs a deck is exactly one minus its win chance vs you.
47M
Recorded games
The training corpus — real ladder outcomes.
11,003
Deck-plan matchups
Distinct plan-vs-plan pairings aggregated from the corpus.
1,527
Measured overrides
Pairings past 3,000 games — observed win rate replaces the estimate.
50%
Coverage floor
Below this share of answered battles, the matchup verdict is left pending — never guessed.
When the model is down. If the model can't answer at least 50% of the weighted battles, the matchup factor stays pending and the score leans on timing and levels. A simpler local logistic model (matchup, level and trophy gaps) stands in for the skill rating until it returns — you see a “verdict pending” note, not a fabricated number.
05 - THE MATCHMAKING TIMELINE
Reading when the hard games landed
The matchup factor asks how many hard games you drew. Timing asks WHEN. It plots your win chance for games started after a loss, on a 1-, 2-, and 3+-win streak, against your overall average. If the line sinks the longer the streak runs, the matchmaker handed harder draws exactly as you climbed.
Illustrative example — not live data
62%post-loss
55%1-streak
47%2-streak
38%3+ streak
dashed line = your overall average win chance (50% here). Illustrative shape of a “rigged as you climb” timeline.
It's interactive on a real report: click a streak bar to highlight exactly those battles in the evidence list. Winning or losing never rewards or punishes the score by itself — the result only locates which games were on a streak; the factor reads the matchmaker's win chance there. Wins and losses feed the separate skill rating instead. Games in the first 3 slots after a real deck change (2+ cards swapped) are read the same way.
06 - WHAT THE BASE LOOKS LIKE
The population these numbers sit against
Descriptive context pooled across every qualifying public case file. It's how the DB Average player is built — real observed rates, not a hand-set benchmark.
198
Players pooled
Qualifying case files folded into the baseline.
49.5%
Bad-matchup rate
Share of games the base draws an unfavorable matchup.
13.4%
On a 3+ streak
57.0% of those were unfavorable draws.
1.40x
Deck-change lift
Bad-matchup rate multiplier right after a deck change.
07 - THE SKILL RATING
A separate read: pilot vs deck
Not part of the rigged score. It asks a different question: did you win more or fewer games than the matchup, level and trophy gaps predicted?
Σ P(win)
Expected wins — sum a predicted win chance over every game.
vs actual
Compare to real wins; each game weighted by how certain it was.
0–100
50 = exactly as expected. The ± is a 95% interval; thin samples read provisional.
It's a proxy for piloting only. It can't see connection quality, starting-hand order, elixir trades, or any individual in-match decision.
08 - CONFIDENCE AND LIMITS
What the score does not prove
Confidence tiers
Low below 8 battles, medium to 14, high from 15. Over 20% unknown cards drops it one level.
Tracking sharpens it
The Track button lets a scheduled job add battles beyond the short API window. A bigger sample raises confidence and settles the score — the recommended way to a precise read.
This is observational
Supercell says 1v1 opponents are matched by trophies, not deck or levels. We measure the result — a hostile draw — not the private selection logic. A low score is an anomaly, not a confession.
Modes matter
Event rules can change what levels or deck structure mean. The mode filters isolate those environments instead of mixing them into one read.
No login required
Every number comes from public player and battle-log data. The site never asks for a game login or account credentials.
Small samples lie
The caution shrink (n/(n+9)) is deliberate: a handful of unlucky games can't earn "Certified Rigged". Deviation has to survive the sample size.