Slaviyy
Full-Stack & ML Backend Developer
Built a sports forecasting engine covering 5 major European leagues. The service runs two coordinated backends: a fast Go ingestion service that parses real-time fixture feeds and historical CSV data, and a Python worker that calculates Poisson probabilities for expected goals (xG) and betting spreads. An interactive React dashboard visualizes tiered prediction confidence.
Role Overview
Slaviyy generates automated expected goals (xG) projections, handicap probabilities, and tiered confidence metrics across the top European football divisions.
Architecture & Implementation
The pipeline is split by operational strengths: Go handles high-concurrency ingestion of external fixture feeds and CSV histories, while Python runs the mathematical modeling. Predictions write back into PostgreSQL where compound indexes support fast querying from the dashboard.
Frontend & Mobile Integration
The React interface groups match projections into clear confidence tiers (High, Likely, Risky) paired with visual probability bars.
Key Outcomes
- ▪Data ingestion pipeline in Go normalizes live fixture feeds and historical datasets into PostgreSQL.
- ▪Statistical model: Python engine calculates Poisson distributions for xG, clean sheets, and over/under markets.
- ▪Maintained sub-second query performance on PostgreSQL across multi-season match records.