A crash game runs on a simple loop. The multiplier starts at 1.00× and rises over time. At a random point, it crashes.
The player aims to cash out before the crash. The later they act, the higher the risk–but also the higher the multiplier.
Think of it as a balloon pump. Each pump inflates the balloon and raises the potential prize. But the more you pump, the greater the chance it bursts.
The system needs four elements: a timer to set intervals, a random number to define the crash point, a growth function for the curve, and a fairness rule to lock the outcome before the round starts.
A good real-world example is the chicken roll game. Technically, it’s well-built: stable tick rate, tight synchronization, and responsive cash-out logic. At the same time, it’s an engaging pastime with interesting mechanics – varying round tempos, visible multiplier growth, and clear crash signals.
Mathematical Model: Probability, Distribution, and Fairness
At its core lies one random variable – the crash point. It’s generated once and remains unchanged during the round.
Conceptually, it follows a time-to-failure distribution. The longer the round lasts, the higher the probability of a crash. Long multipliers are rare; short ones dominate.
Game economics depend on the shape of the distribution and the house margin. The margin is built into the formula or applied as a commission, depending on regulations.
Fairness is guaranteed through the provably fair approach. The server publishes a hash of a hidden seed before the round. After it ends, the seed is revealed. Anyone can verify the crash point by recalculating it. The result is predetermined, not manipulated.
Algorithm and Growth Mechanics: From Tick to Crash
The server starts a round and records the start time. A ticker updates the multiplier at short, fixed intervals.
The multiplier usually follows an exponential function–slow at first, faster near the end. The system continuously compares the current multiplier to the crash threshold. Once the threshold is reached, the round stops.
Trust depends on a single source of truth–only the server performs calculations. Clients merely display updates. This eliminates mismatches across screens.
Cash-outs must register before the crash. The server timestamps every request and locks in the player’s multiplier upon receipt, ensuring fairness even under network delay.
Implementation in C# (.NET): Architecture and Core Logic
It’s helpful to view the system as an orchestra of services, each handling one clear task.
- Randomness service: generates seeds, computes the crash point, and maintains the hash chain.
- Growth engine: calculates the current multiplier based on elapsed time.
- Bet manager: tracks bets and cash-outs, logging each multiplier and payout.
- Timer or scheduler: maintains steady ticks and controls round flow.
- Event layer: broadcasts updates to clients (via WebSockets or SignalR).
Why .NET fits well:
- Precise timing for smooth tick updates.
- Predictable multithreading for parallel rounds.
- Reliable data handling for bets, logs, and player events.
The typical sequence remains: start → grow → cash-outs → crash → settle. Each step is logged, making every round traceable and auditable.
Testing and Fairness Verification
Testing occurs on several levels.
Logic testing. Verify event order: start at 1.00×, continuous growth, crash at the exact threshold, accurate cash-outs. Small tick size and correct timestamps are crucial.
Randomness testing. Run tens of thousands of rounds. Compare short vs. long multipliers. The distribution must align with mathematical expectations. Persistent bias means the generator is flawed.
Load testing. Simulate hundreds of simultaneous players cashing out within milliseconds. The server must handle the load without losing or duplicating events.
Field testing. Real users confirm that the visual multiplier matches the server’s internal state.
The provably fair principle ties it all together: publish a hash before the round, reveal the data afterward, and allow independent verification. This ensures both technical and perceptual trust.
Practical Notes: User Experience and Performance
For players, the priority is stable growth without stutter. The interface should show a clear multiplier, a visible timer, and instant feedback when cashing out. Lag at crash time destroys confidence.
For the server, the priority is smooth performance under load. Use lightweight messages, compact formats, and efficient event queues. Fix latency bottlenecks in the network or database.
A strong reference is the chicken roll game. It demonstrates precise timing, accurate synchronization, and responsive player feedback. The design keeps users engaged through tempo mechanics, clear signals, and short, satisfying cycles.
Conclusion: From Theory to Prototype
The model is elegant. Randomness defines the crash point. Growth controls multiplier behavior. Timing ties everything together. Provably fair ensures transparency.
The path to a .NET prototype is direct. Split logic into small services. Use a unified time source on the server. Log every event. Run a thousand test sessions. Analyze distributions.
Follow these steps, and you’ll have a predictable mechanic, quick response, and transparent fairness verification – where theory meets execution, and execution matches player expectation.