1xbet Crash Data Analyzer
Budget / SalaryHourly project
TypeFreelance project
LocationRemote
Posted1 hour ago
I’m developing a Crash 1xbet predictor and the first milestone is a solid data backbone. What I need right now is a lightweight app (web or desktop, whichever you are fastest with) that lets me enter raw game information by hand, stores it safely, and immediately turns it into useful insights.
Core objective
The build must focus on data collection and analysis. Because I don’t have a public API to hit, every record will be added through manual input. Once stored, the data should be processed in real-time so I can quickly spot trends that feed the eventual prediction model.
Data you’ll be handling
• User betting patterns
• Real-time game statistics (crash multipliers, timestamps, etc.)
Key features
– Clean manual-entry form with validation and duplicate checks
– Lightweight database or structured files to hold the history (open to SQLite, Postgres, or even Google Sheets if faster to iterate)
– Instant analytics: average multipliers, streak detection, profit/loss summaries, and any quick visuals you can generate (charts or tables are fine)
– Export option to CSV or JSON for future algorithm training
– Brief setup notes so I can run or extend the tool without you
Acceptance criteria
1. I can add at least 100 entries in one session without a crash or noticeable lag.
2. Basic stats refresh automatically after each save.
3. Data exports open correctly in Excel and Python.
Keep the interface simple; speed and clarity matter more than polish at this stage.
Core objective
The build must focus on data collection and analysis. Because I don’t have a public API to hit, every record will be added through manual input. Once stored, the data should be processed in real-time so I can quickly spot trends that feed the eventual prediction model.
Data you’ll be handling
• User betting patterns
• Real-time game statistics (crash multipliers, timestamps, etc.)
Key features
– Clean manual-entry form with validation and duplicate checks
– Lightweight database or structured files to hold the history (open to SQLite, Postgres, or even Google Sheets if faster to iterate)
– Instant analytics: average multipliers, streak detection, profit/loss summaries, and any quick visuals you can generate (charts or tables are fine)
– Export option to CSV or JSON for future algorithm training
– Brief setup notes so I can run or extend the tool without you
Acceptance criteria
1. I can add at least 100 entries in one session without a crash or noticeable lag.
2. Basic stats refresh automatically after each save.
3. Data exports open correctly in Excel and Python.
Keep the interface simple; speed and clarity matter more than polish at this stage.
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