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Limbo paid less than advertised.

The tested Limbo capture produced a house edge roughly 2.7× the advertised rate.

Dice and Plinko — measured the same way, at the same time, on the same account — were statistically consistent with their advertised mathematics in these samples, which strongly argues the deficit is specific to Limbo and not a measurement artefact.

Because a BetFury result can't be recomputed by a player, the only remaining check is statistical: place a large, fixed number of bets and compare what the games actually pay against what they advertise. Money RTP = total paid ÷ total staked — mechanism-agnostic, identical for all three games. Dice and Plinko serve as controls; both are checkable against a fixed reference.

Why 60,000 bets at these settings. The precision of a measured RTP depends on per-bet variance as much as on bet count, so the run was designed around a low-variance setting: at a 1.10× target the game pays on roughly nine bets in ten, and 60,000 fixed-stake rounds bring the standard error on measured RTP down to ±0.135 points — a 95% confidence interval of ±0.26 points around the advertised 99.02%. That is quarter-point resolution on a question asked in whole points; the same count at high cash-out targets would resolve RTP only to within several points. The fixed 0.001 USDT stake keeps money RTP a pure function of the win count, and Dice and Plinko ran through the same account, window and stake, so the controls share identical exposure to any capture artefact.

Limbo shows a "Win Chance" on every bet — 90.01818% at the 1.10× setting. Over 60,000 bets those odds predict about 54,011 wins; players actually won 53,087, roughly 924 fewer than the game's own displayed odds promise. This is the cleanest evidence in the report, because it needs no assumptions about BetFury's formula — it holds them to the number printed on their own button. And 924 is not a rounding error: at BetFury's displayed odds the standard deviation over 60,000 bets is about 73 wins, so a shortfall of roughly 924 wins is 12.58 standard deviations below expectation — far outside any credible run of ordinary bad luck (the exact odds are in the note below).

At 1.00× Limbo ends before the multiplier moves — an outright loss on the first tick. Under the standard two-decimal Limbo formula consistent with a displayed 99.02% RTP, about 1.96% of rounds should land there — roughly one bust every 51 bets. We recorded far more.

954 extra instant losses in a single 60,000-bet run.

The numbers line up almost perfectly: about 924 fewer wins than the displayed odds predict, alongside about 954 excess 1.00× busts under the standard 99.02% Limbo model. That is strongly consistent with most of the shortfall appearing as additional first-tick losses — nearly double the instant losses the advertised game should produce.

Before the fully instrumented investigation, we ran an exploratory 60,000-bet Limbo capture at a 2.00× target. It recorded results, payouts and timestamps but not the seed/hash fields, so it is not pooled into the headline figure — it is published separately as corroboration.

Both real runs land near one instant bust every 28–29 bets; the advertised model predicts one every 51. The near-doubling from a 1.6-point RTP change is expected arithmetic, not a mismatch: the 1.00× rate equals (1.01 − R) ÷ 1.01, so it tracks the gap between R and 1.01 — and every point of return removed reappears almost one-for-one as additional instant-bust probability. The exploratory run also returned 97.89% money RTP against the displayed 99.02% — corroboration only; the headline rests on the instrumented 1.10× run.

Two targets, near-identical instant-loss rate.

The 1.00× rate was 3.453% at 2.00× and 3.550% at 1.10× — pointing to a persistent lower-return pattern across both captures rather than an unlucky cluster around one setting. The first run discovered the signal; the second was built to document it properly.

Within the 60,000 multipliers generated while betting at 1.10×, the implied return remained below 99.02% at every tested threshold from 1.01× to 10×. Individual estimates ranged from 96.24% to 97.41% and became noisier as the target rose. Fitting the captured multiplier distribution to the standard two-decimal Limbo model produces an underlying RTP estimate near 97.4%. We did not separately place bets at other targets; the exploratory run below did, at 2.00×, and also came back short.

The same shortfall, row by row. Implied win rate at each cash-out threshold within the captured 60,000 multipliers, times the payout — the same win-chance × payout arithmetic BetFury's own bet panel displays. Every row lands 1.6–2.8 points below the advertised 99.02%. The highlighted 1.10× row is the instrumented headline run, exact to the bet; per-target figures are recomputable from the published dataset.

The statistical backing, for the technically mindedexact binomial test · confidence interval · block check

BetFury does not publish a player-reproducible Limbo formula, so we cannot state what exact code or constant was running on its servers. But the standard two-decimal Limbo model can be fitted to the recorded results — and under that model, a single RTP constant controls the whole multiplier distribution:

In this model, one constant — R — sets the whole payback. With R = 0.9902 the model returns 99.02%, exactly what BetFury advertises and what its own bet panel encodes. A lower R shifts the entire distribution the same way: fewer wins at every cash-out target, and more first-tick busts. The question is simply which R best fits what we recorded.

And 0.9902 is not just the advertised number — it is the constant BetFury's own bet panel encodes at every cash-out target. At each of the eight targets below, the displayed win chance is exactly 0.9902 ÷ target to five decimal places:

The interface encodes R = 0.9902 at every target checked. Win chance × payout returns the same constant at every target we captured from the bet panel — which also means no fatter high-multiplier tail is claimed that could reconcile the captured distribution with 99.02%, and none could alter the measured return of the 1.10× bets we placed, since a 1.10× target pays a fixed 1.10× however far the round runs.

Three separate readouts of the same 60,000-bet capture point to the same range — related views of one distribution, each expressing the discrepancy differently:

These three readouts are related, not independent experiments — they come from the same capture. Together they show BetFury's captured multiplier distribution behaves like a standard Limbo model with an effective RTP parameter near 97.4% (R ≈ 0.974), not the displayed 99.02%. This is a statistical model fit, not a claim that we obtained BetFury's source code; the actual replication is the two separate 60,000-bet captures. The shortfall itself does not depend on this model: the 88.48% win rate against the displayed 90.01818% win chance, and the 97.33% return against the advertised 99.02%, are raw counts from the recorded bets and stand under any formula. The fit adds the interpretation — those counts are what a standard Limbo game configured ~1.6 points low would produce.

BetFury's displayed odds already rule out ordinary variance — no assumption about its formula required. At a displayed 90.02% win chance, 60,000 bets predict about 54,011 wins; we recorded 53,087, roughly 924 short. A shortfall that large or larger has a probability of about 4.1 × 10⁻³⁵ under the displayed odds. This calculation holds BetFury to the number on its own button and nothing else.

The multiplier distribution shows where the shortfall appears. Under the standard 99.02% two-decimal Limbo model, about 1,176 rounds should end immediately at 1.00×; we recorded 2,130. An earlier, separate run recorded another 2,072 instant busts — again almost twice the expected count. Two captures, two different targets, the same excess: that is not a credible ordinary-variance explanation.

The controls reduce the remaining concern. Dice and Plinko don't establish the Limbo result — Limbo's own win count does that — and they don't independently authenticate the Limbo capture. What they do show is that the same collection-and-analysis pipeline did not systematically produce low results across every game: run identically, both were statistically consistent with their expected mathematics, strongly arguing against a general account-wide or pipeline issue and isolating the detected discrepancy to Limbo.

The three games cannot be compared by asking whether each sample finished above or below 99.02%. Their payout distributions have different variance, so ordinary luck moves each game’s 60,000-bet average by a different amount. The right yardstick is each game’s own normal sampling swing:

Why 99.39% and 97.33% are not comparable deviations. The 1σ column is how far ordinary luck moves each game’s 60,000-bet average, computed from the games’ published odds and paytables — at the tested settings Dice and Plinko swing naturally about 3× and 4.5× wider than Limbo. Dice finished less than one normal swing above its target and Plinko landed almost exactly on it; both are ordinary sampling variance, and neither implies a true RTP above or below the advertised figure. Limbo’s high-win-rate 1.10× configuration should have converged the most tightly of the three — instead it finished 12.58 standard deviations below expectation; the exact binomial probability of a shortfall that large is ≈ 4.1 × 10⁻³⁵ (see the statistical note in 2.3).