Investigation: Betfury Provably Fair & RTP

Executive Summary

This investigation evaluates three of BetFury’s in-house “Original” games — Limbo, Dice and Plinko — against two independent questions:

1Provably Fair Evaluation

1.1What provably fair should look like

1.2What BetFury actually does

Captured from the live game socket — the complete outgoing bet, everything the browser sends to place a wager:

// games.dice.bet — the entire wager sent to BetFury
{ "is_under": true, "prediction": 50, "amount": "0.10000000",
  "isFast": false, "isAuto": false, "frontID": 1784114526766 }

1.3The revealed proof contains the result

BetFury’s bet record reveals a randomSeed and a hash where SHA256(randomSeed) == hash. But read the seed:

Limbo:  1.91_XiLQkYQZxcwNxF       →  1.91 was the multiplier
Dice:   52_FcqvWBAQrkwQbZ         →  52 was the roll
Plinko: 1000101001000011_owOW…    →  the 16-step ball path

Every proof is <result>_<server string>. It contains the finished result — not the inputs and formula needed to derive it. BetFury’s own docs confirm the shape: “Random seed is a combination of Random winning number and Server seed.”

1.4The RNG certificate

The public BetFury testing document we located — SIQ Test Report T-J0068-I0003, from 2022 — provides laboratory testing and software analysis of a submitted Math.random()-based PRNG sample, and states its own scope:

The report does include a Limbo section — and it’s worth being precise about what that section checks, because it’s easy to mistake for a fairness guarantee. It tests that the game’s underlying random draw is statistically uniform: that the raw numbers come out evenly, with no bias. The sample passes. That is a real and useful test — but it answers a narrower question than fairness.

The report provides evidence that the submitted PRNG sample passed its statistical and software checks. It does not establish that the live games used the same implementation, or that their payout maths produced the represented RTP — the two things that decide what a player actually receives. A generator can pass every randomness test and still underpay if the payout constant that converts those numbers into a return is set below the advertised rate, which is exactly the Limbo pattern we measured: fair-looking numbers, a house edge ~2.7× the advertised rate.

1.5Provably fair requirements vs BetFury

2RTP Measurement

2.1Method & conditions

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.

2.2Results

2.3Limbo — detail

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).

The clearest single symptom: the 1.00× instant bust

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.

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.

2.4A standard Limbo reference model (R ≈ 0.974) closely fits the captured pattern

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:

// simplified standard reference model — NOT BetFury source code
rawMultiplier       = R / (1 − u)                        // u uniform in [0,1); R = RTP constant
displayedMultiplier = max(1.00, floor(rawMultiplier × 100) / 100)   // 2-dp floor; below 1.00 = instant bust

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:

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:

2.5Corroboration — the signal appeared twice

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.

2.6Why it isn’t variance — the controls

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.

Same 60,000 bets, radically different statistical meaning

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:

3Findings & Recommendations

3.1Scope of the claim

3.2What BetFury needs to explain

3.3Recommendations

4Reproducibility

Every figure is recomputable from the published data. Because the seeds contain the results and SHA-256 is unkeyed, a matching hash proves internal consistency, not origin — so anyone can run the scripts on a fresh capture of their own to test whether the shortfall reproduces.

$ node scripts/verify.mjs
limbo   60000 | SHA256(seed)==hash 60000/60000 | result-in-seed 60000/60000  ✓
plinko  60000 | SHA256(seed)==hash 60000/60000 | result-in-seed 60000/60000  ✓
dice    60000 | SHA256(seed)==hash 60000/60000 | result-in-seed 60000/60000  ✓
TOTAL   180000 rounds | 180000/180000 seed matches its hash

$ node scripts/analyze.mjs
  limbo   RTP 97.326%  win 88.478% vs 90.018% shown  binomial p=4.1e-35
  plinko  RTP 99.008%  no discrepancy
  dice    RTP 99.393%  no discrepancy (control)

5Final Conclusion