Sports Analytics / Python & Go — 2025 — 2026
Slaviyy
Automated football prediction engine & expected goals (xG) analytics platform across 5 European leagues.
slaviyy.python
Core Implementation Snippet# Slaviyy Poisson xG Probability Calculator
import scipy.stats as stats
def calculate_match_probabilities(home_xg, away_xg):
matrix = {}
for h in range(6):
for a in range(6):
prob = stats.poisson.pmf(h, home_xg) * stats.poisson.pmf(a, away_xg)
matrix[(h, a)] = prob
home_win = sum(p for (h, a), p in matrix.items() if h > a)
draw = sum(p for (h, a), p in matrix.items() if h == a)
away_win = sum(p for (h, a), p in matrix.items() if a > h)
return {"home_win": home_win, "draw": draw, "away_win": away_win}Markets ModeledxG, BTTS, O/U
Data IngestionAutomated CSV/API
StoragePostgreSQL
FrontendReact
Role
Full-Stack & ML Backend Developer
Timeline
2025 — 2026
Technologies
GoPythonMachine LearningPoisson DistributionPostgreSQLReactREST
Overview & Problem Solved
An end-to-end sports forecasting platform that ingests raw match fixtures and historical football data, runs statistical Poisson probability models, and delivers tiered confidence predictions (High, Likely, Risky) with data-backed reasoning.
System Architecture
Go backend for high-speed API ingestion, Python prediction worker utilizing historical dataset regression & Poisson probability distributions for expected goals (xG) and market spreads, backed by PostgreSQL and a React frontend.
Key Outcomes
- ▪Engineered automated match data ingestion pipeline pulling live fixtures and processing historical CSV datasets.
- ▪Integrated Python statistical prediction engine computing xG, BTTS, clean sheets, and over/under markets.
- ▪Built intuitive React dashboard displaying tiered predictions with percentage-based reasoning.