Overview
La Quiniela is Spain's traditional football betting game where participants predict the outcomes of 15 matches (1 = Home Win, X = Draw, 2 = Away Win). This project leverages machine learning to analyze historical match data, team statistics, and various performance metrics to generate intelligent predictions.
The Challenge
Football prediction is inherently complex due to:
- High variability in match outcomes
- Multiple factors influencing results (form, injuries, venue, weather)
- Bookmaker odds already incorporating vast amounts of information
- The draw problem - draws are notoriously difficult to predict
Traditional betting strategies often fail because they don't account for the interconnected nature of these factors.
Technical Solution
Architecture
The system is built with a modern stack designed for scalability and real-time predictions:
Backend (Python + FastAPI)
- RESTful API for prediction requests
- Async processing for parallel model inference
- Caching layer for frequently requested predictions
Machine Learning Pipeline
- Feature engineering from historical La Liga/La Quiniela data
- Ensemble models combining multiple algorithms
- Regular model retraining with new match data
Frontend (React)
- Clean, intuitive interface for match predictions
- Historical accuracy tracking
- Mobile-responsive design
Key Features
Data Collection & Processing
- Automated scraping of match statistics
- Historical La Quiniela results database
- Real-time odds integration
Prediction Models
- Gradient Boosting for match outcome prediction
- Neural networks for pattern recognition
- Ensemble voting for final predictions
Confidence Scoring
- Each prediction includes a confidence percentage
- Recommendations for doubles/triples on uncertain matches
- Historical accuracy metrics by confidence level
Machine Learning Approach
Feature Engineering
The model uses 50+ features including:
- Team Performance Metrics: Recent form, goals scored/conceded, home/away splits
- Head-to-Head History: Historical matchups, venue-specific performance
- Contextual Factors: Match importance, rest days, travel distance
- Market Signals: Betting odds movements, market consensus
Model Performance
| Metric | Value |
|---|---|
| Overall Accuracy | ~58% |
| Home Win Precision | 65% |
| Draw Detection | 45% |
| Away Win Precision | 52% |
Note: Random baseline for 3-way prediction is 33%
Technology Stack
| Component | Technology |
|---|---|
| Backend API | Python, FastAPI |
| ML Framework | scikit-learn, XGBoost |
| Data Processing | Pandas, NumPy |
| Frontend | React, TypeScript |
| Database | PostgreSQL |
| Deployment | Docker, AWS |
What I Learned
This project deepened my expertise in:
- Applied Machine Learning - Moving from theory to production ML systems
- Feature Engineering - Understanding which signals matter for prediction
- API Design - Building robust, scalable endpoints
- Full-Stack Development - Connecting ML models to user-facing applications
Future Enhancements
- Deep learning models for sequence prediction (match-by-match form)
- Live odds integration for dynamic prediction updates
- Telegram bot for instant prediction delivery
- Historical simulation for betting strategy backtesting
This project combines my passion for football with data science, demonstrating practical application of machine learning to real-world prediction challenges.