Crypto “dice” lets you choose a target (win-chance) and a corresponding multiplier; the game’s random roll is derived by a provably fair commitment scheme using server seed, client seed, and nonce so you can verify each round after play.
Most leading operators set dice at 99% RTP (≈1% house edge). For example, Stake lists Dice at 99% RTP/1% edge and shows example tables that link high win-chance to low multipliers and vice versa.
The math that actually drives outcomes
House edge is the casino’s average profit as a percentage of your initial bet; RTP + house edge = 100%. This is a long-run expectation, not a short-term promise.
With a fixed-edge dice engine, the multiplier scales roughly like (RTP ÷ win-chance). At 1% edge, that’s about 0.99 / p. Stake’s own examples are consistent with this relation (e.g., 80% win ≈ 1.2375×; 2% win ≈ 49.5×).
Expected value per bet stays equal to −(house edge), regardless of your chosen win-chance or multiplier. You’re trading hit rate and volatility, not ROI. This follows directly from EV = p·multiplier − 1 and the fixed-edge payout schedule.
What “low-risk grind” really means
A low-risk grind uses a high win-chance (for example, 65%–95%), which yields many small wins and smoother equity curves but modest multipliers. Stake’s table illustrates typical pairs such as 80% win-chance ≈ 1.2375×. Over time, the EV per bet still matches the 1% house edge.
Smoother doesn’t mean safe. Long losing streaks are less frequent but still inevitable. For independent rounds with win probability p, the chance of L losses in a row is (1−p)^L, a direct application of the geometric distribution.
When it helps: extending session length for entertainment, completing loyalty missions, or practicing verification flows—while accepting the same long-run cost from the house edge.
What “high-risk burst” really means
A high-risk burst uses low win-chances (for example, 1%–10%) to chase large multipliers like 49.5× at 2% win probability. Sessions will be swingy, with longer dry spells before a hit—the expected number of trials to first success is 1/p.
Your EV per bet is still −1% in a 99% RTP model; only variance changes. That higher variance raises the likelihood of deep drawdowns within any finite bankroll.
When it helps: short “lottery-style” sessions where infrequent big pops are the entertainment goal, not steady returns.
Bankroll rules that keep both styles honest
Sizing: In negative-edge games, the Kelly criterion yields a zero or negative optimal bet fraction—i.e., the mathematically optimal “growth” bet is not to bet. If you still play for fun, keep stakes small relative to bankroll.
Streak planning: Use (1−p)^L to sanity-check how often a losing run might appear at your chosen p. For example, with p = 0.66, ten straight losses are extremely rare; with p = 0.02, long losing runs are common. This isn’t prediction—just risk framing from geometric probabilities.
Ruin awareness: Any negative-EV game exposes you to gambler’s ruin if you keep playing long enough, regardless of progression systems. Don’t chase losses.
Provably fair: always verify
Legitimate dice implementations document seed hashing and reveal the server seed after the session so you can recompute rolls and confirm there was no tampering. Use the site’s verifier or a community tool; don’t skip this step.
Choosing between grind and burst
Goal: longer playtime, steadier feel
Pick a higher win-chance band (for example, 70%–90%). Expect frequent small wins, lower volatility, and the same −1% long-run cost.
Goal: jackpot-style excitement in short sessions
Pick a lower win-chance band (for example, 1%–5%). Expect long dry spells and big multipliers when you hit—variance is the product. The long-run cost is still the house edge.
Either way: set a budget, predefine a stop-loss and session length, and avoid bet progressions that assume “a win is due.”
Worked examples (99% RTP dice)
Target smoother play (grind): choose p = 0.80 → multiplier ≈ 0.99 / 0.80 ≈ 1.2375× (Stake’s table). EV per 1-unit bet ≈ 0.80·1.2375 − 1 ≈ −0.01.
Target big pops (burst): choose p = 0.02 → multiplier ≈ 0.99 / 0.02 ≈ 49.5×. EV per 1-unit bet ≈ 0.02·49.5 − 1 ≈ −0.01. Expected tries to first win ≈ 50. Plan bankroll accordingly.
Quick FAQ
Does changing win-chance improve ROI?
No. With fixed house edge, changing p only trades hit rate for volatility; EV per bet stays −(house edge).
Where do I find official numbers?
Operator pages list RTP/edge and provide example tables mapping win-chance to multipliers; Stake’s Dice lists 1% edge with examples like 80% → 1.2375× and 2% → 49.5×.
How can I check fairness?
Use the site’s provably fair page: it shows the server-seed hash before play and reveals the seed after, letting you verify rolls with your client seed and nonce.
Why do long losing streaks still happen at high win-chance?
Because independent trials follow geometric probabilities; the chance of L losses in a row is (1−p)^L even when p is large.
