Android 1xBet Crash Predictor
Budget / SalaryHourly project
TypeFreelance project
LocationRemote
Posted1 hour ago
I want a lightweight Android app that can read the live feed of 1xBet’s Crash game and forecast the crash point a few seconds before each round stops. Mobile is the only environment I need covered, so everything you build should install cleanly on recent Android versions without side-loading gymnastics.
The core of the project is the prediction engine. Whether you choose pure probability modelling, machine-learning, or a hybrid approach, the algorithm must process real-time data from the game screen or API and return a single number—the expected multiplier at which the round will bust. A simple, distraction-free interface that shows the current round, your predicted crash point, a confidence score, and a quick history log is enough for version one; fancy charts can wait.
You’re free to code in Kotlin, Java, Flutter, or React Native as long as the final deliverable is a reliable APK. Please keep third-party dependencies minimal and document anything that requires a paid key or special permission.
Acceptance criteria
• Prediction appears before the crash at least 95 % of the time during a 100-round test session.
• Average error (|predicted − actual|) is clearly reported and stays under 0.30x.
• Delivered items: signed APK ready for install, full source code with build instructions, and a short README that explains the data capture method and algorithm logic.
If you have previous work in gaming analytics, that will help me gauge the likely accuracy curve, but clean, well-commented code and transparent maths matter most.
The core of the project is the prediction engine. Whether you choose pure probability modelling, machine-learning, or a hybrid approach, the algorithm must process real-time data from the game screen or API and return a single number—the expected multiplier at which the round will bust. A simple, distraction-free interface that shows the current round, your predicted crash point, a confidence score, and a quick history log is enough for version one; fancy charts can wait.
You’re free to code in Kotlin, Java, Flutter, or React Native as long as the final deliverable is a reliable APK. Please keep third-party dependencies minimal and document anything that requires a paid key or special permission.
Acceptance criteria
• Prediction appears before the crash at least 95 % of the time during a 100-round test session.
• Average error (|predicted − actual|) is clearly reported and stays under 0.30x.
• Delivered items: signed APK ready for install, full source code with build instructions, and a short README that explains the data capture method and algorithm logic.
If you have previous work in gaming analytics, that will help me gauge the likely accuracy curve, but clean, well-commented code and transparent maths matter most.
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