🔗 Bilyoner

Bilyoner AI Match Prediction Platform

Introduction

An AI-powered sports match prediction platform developed for Bilyoner, one of Turkey’s leading sports analytics and betting information platforms. The system leverages machine learning models and microservices architecture to deliver data-driven match predictions, statistical analysis, and real-time insights across multiple sports leagues.

Architecture: Microservices with Spring Boot

The platform was built using a microservices architecture with Java Spring Boot, enabling independent deployment, scaling, and maintenance of each domain-specific service.

Core Microservices:

  • Match Data Service: Ingests and normalizes match data from multiple league APIs (football, basketball, tennis, esports)
  • Prediction Engine Service: Runs ML models to generate match outcome probabilities, score predictions, and trend analysis
  • Statistics Service: Computes real-time team/player statistics, head-to-head records, form analysis, and historical performance metrics
  • User Preferences Service: Manages user follow lists, notification preferences, and personalized prediction feeds
  • Notification Service: Delivers push notifications for match start times, live score updates, and prediction alerts

AI/ML Integration

The prediction engine combines multiple approaches:

  • Statistical Modeling: Historical performance analysis using regression models on team form, head-to-head records, home/away splits
  • Machine Learning Models: Trained classifiers that factor in 60+ features including player availability, weather conditions, league standings, and recent performance trends
  • Real-Time Processing: Live match data feeds processed through streaming pipelines to update predictions dynamically during matches

Key Technical Challenges

  1. Data Pipeline at Scale: Processing thousands of matches across dozens of leagues with sub-minute latency from event occurrence to prediction update
  2. Model Accuracy & Calibration: Ensuring predicted probabilities are well-calibrated (i.e., a 70% predicted win rate actually wins ~70% of the time) through continuous model retraining and validation
  3. Microservice Communication: Implementing reliable inter-service communication with circuit breakers, retry logic, and distributed tracing for debugging production issues

Technologies Used

Java + Spring Boot: Core framework for all microservices, leveraging Spring Cloud for service discovery, configuration management, and API gateway patterns.

Microservices Patterns: Service registry (Eureka), API Gateway (Spring Cloud Gateway), circuit breakers (Resilience4j), distributed tracing, and event-driven communication.

AI/ML Stack: Python-based ML models served via REST endpoints, integrated with Java microservices through inter-service APIs. Feature engineering pipelines for 60+ match features.