A crash game is a fast round-based wager where a multiplier ticks up from 1.00× until it “crashes.” You win if you cash out before the crash, otherwise you lose your stake. It’s driven by an RNG/provably-fair process and each round is independent.
Many titles include an auto-cashout control: you pre-set a multiplier (for example, 1.80×) and the system automatically cashes you out the instant that level is hit. This helps remove hesitation and stick to a plan.
The two numbers that actually matter: RTP and house edge
House edge is 100% − RTP. Bustabit, the original crypto crash, publishes a 1% house edge (99% RTP). Popular mainstream “crash-style” titles like Spribe’s Aviator publish 97% RTP. The target you choose for auto-cashout does not change these figures.
Hit-rate math for auto-cashout targets
For crash engines designed like Bustabit’s, community and operator math give a simple rule of thumb for the chance a round reaches your target X:
Probability(reach ≥ X) ≈ RTP ÷ X. With a 1% edge, that’s p ≈ 0.99/X. With 97% RTP, p ≈ 0.97/X.
Example hit rates using that model:
- 99% RTP:
1.20× → ~82.50% hit, 1.50× → ~66.00%, 2.00× → ~49.50%, 3.00× → ~33.00%, 5.00× → ~19.80%, 10.00× → ~9.90%. - 97% RTP (e.g., Aviator):
1.50× → ~64.67%, 2.00× → ~48.50%, 3.00× → ~32.33%.
These are long-run probabilities implied by the game math; actual sessions will vary.
The uncomfortable truth about ROI
Let X be your auto-cashout and p the hit probability. On a 1-unit stake, your expected return per bet is
EV = p·(X−1) − (1−p) = p·X − 1.
With p ≈ RTP/X, that becomes EV ≈ RTP − 1 = −(house edge). In other words, changing your auto-cashout target changes your hit rate and volatility, but not your expected ROI per bet: in the 99% RTP model it stays −1%, and in 97% RTP it stays −3%.
Volatility and streaks: what tuning really controls
Lower targets raise hit rate and smooth results; higher targets lower hit rate, raise variance and fat-tail outcomes. Over multiple independent rounds, the chance of a dry spell matters for bankroll planning. For example (99% RTP model):
- X = 1.50×, p ≈ 0.66 → probability of 10 losses in a row ≈ (1−0.66)¹⁰ ≈ 0.002%.
- X = 2.00×, p ≈ 0.495 → 10 losses in a row ≈ 0.108%.
- X = 3.00×, p ≈ 0.33 → 10 losses in a row ≈ 1.82%.
Risk-of-ruin grows with lower p and with bet sizing that’s too large for your bankroll; the concept is well-studied in gambling and finance.
Provably fair and what it means for auto-cashout
Most crash titles commit to a hashed server seed, combine it with your client seed and a nonce to derive each outcome, and later reveal the server seed so you can verify results. This protects integrity but does not change RTP or your EV.
Latency, slippage, and why auto-cashout helps
Manual cash-outs can fail if your click arrives after the server has already determined a crash; operators like Bustabit describe how network latency can cause missed or delayed cash-outs. Pre-setting auto-cashout reduces that human-reaction risk because the server triggers it at the threshold. Still, read your game’s help page for specifics.
A practical tuning framework
- Pick your RTP context
Know the title’s RTP: Bustabit ~99% (1% edge), Aviator ~97% (3% edge). Your ROI per bet is locked to that edge. Tune targets for experience, not to “beat” the edge. - Choose a hit-rate band that fits your temperament
Use p ≈ RTP/X to back-solve X. For a ~50% hit rate:
- 99% RTP → X ≈ 0.99/0.50 ≈ 1.98×
- 97% RTP → X ≈ 0.97/0.50 ≈ 1.94×
- Size small relative to bankroll
With negative edge games, the Kelly criterion implies the optimal growth bet is zero; if you still play for entertainment, keep stakes small and consistent to avoid large drawdowns. - Plan for streaks
Estimate odds of L consecutive losses as (1−p)^L before raising stakes or targets. This helps set loss limits and session lengths. - Use auto-cashout plus a session rule
Pre-set your target to remove hesitation; pair it with a simple session rule (for example, stop after N rounds or after a fixed loss limit). Most crash games support auto-cashout natively.
Worked examples
Targeting steadier hits (99% RTP model)
Suppose you want roughly two wins out of three on average. Set X ≈ 1.50× (p ~ 66%). Expect smoother sequences but frequent small wins, and the same −1% EV per bet.
Targeting bigger pops (97% RTP model)
At X = 3×, p ≈ 32.33%. You’ll see longer losing streaks and swingier sessions, and your EV remains −3% per bet. Consider smaller stakes and stricter loss limits.
FAQs
What’s the “best” auto-cashout target for ROI?
There isn’t one. Under the standard model p ≈ RTP/X, EV per bet stays equal to −(house edge) regardless of X. Targets only trade hit rate versus volatility.
Does provably fair randomness change my odds?
It lets you verify outcomes via seeds and nonces, increasing transparency, but it doesn’t improve RTP.
Why does Aviator feel different from Bustabit?
Different RTPs and implementations. Aviator lists 97% RTP, while Bustabit lists ~99% RTP; at the same target, your hit rate will be lower in lower-RTP titles.
Is auto-cashout safer than clicking manually?
It reduces risk from human reaction time and some latency issues, but nothing can cash you out after a crash. Read your game’s latency notes.
Sources and further reading
Bustabit help and house edge; network latency notes.
Community math for crash probability at a target X.
Aviator official page showing 97% RTP.
Crash game overview and RNG basics.
Kelly criterion and bet sizing intuition.
Auto-cashout feature explained across crash titles.
