La Liga Analytics & Match Prediction System

A machine learning-powered system to predict La Liga football match outcomes with real-time injury data integration.

🎯 Features

• Match Predictions: Real-time predictions for upcoming La Liga matches using ML models
• Injury Integration: Real-time player injury data enrichment
• Historical Analysis: Team statistics and performance trends
• Key Player Impact: Identifies key players and their performance metrics
• Interactive Dashboard: React-based UI for exploring predictions and standings
• REST API: FastAPI backend for seamless data access

🛠 Tech Stack

Backend:

• Python 3.x with FastAPI
• SQLAlchemy ORM for database operations
• PostgreSQL for data persistence
• Scikit-learn for ML model training

Frontend:

• React with Vite bundler
• Tailwind CSS for styling
• Axios for API communication

📋 Prerequisites

• Python 3.8+
• Node.js 16+
• PostgreSQL 12+
• Git

🚀 Setup & Installation

1. Clone the Repository

git clone https://github.com/niraj4rl/Laliga_gin.git
cd Laliga_gin

2. Backend Setup

Create Virtual Environment

python -m venv .venv

# On Windows
.venv\Scripts\activate

# On Linux/Mac
source .venv/bin/activate

Install Dependencies

cd backend
pip install -r requirements.txt
cd ..

Configure Environment Variables

# Copy the example file
cp backend/.env.example backend/.env

# Edit .env and add your API keys
# Required:
#   - FOOTBALL_API_KEY: Get from https://www.football-data.org/
# Optional:
#   - RAPIDAPI_KEY: For enhanced injury data from RapidAPI
#   - APIFOOTBALL_API_KEY: Alternative data provider
# Database:
#   - DATABASE_URL: PostgreSQL connection string

Setup Database

# Create database
createdb laliga_analytics

# Run migrations
cd backend
python -c "from database.db import Base, engine; Base.metadata.create_all(bind=engine)"

# Seed initial data (optional)
python database/seed.py
cd ..

3. Frontend Setup

cd frontend
npm install
cd ..

4. Run the Application

# Start both backend and frontend
python run.py

The application will be available at:

• Frontend: http://localhost:8002
• Backend API: http://localhost:8001

📡 API Endpoints

Predictions

• GET /predict/upcoming - Get upcoming match predictions
• GET /predict/upcoming?include_injuries=true - Include injury data
• GET /predict/upcoming?include_injuries=false - Fast predictions without injury data

Query Parameters

• include_injuries (boolean): Include player injury data (default: true)
• Response time varies: ~30s with injuries, ~5s without

🗂 Project Structure

├── backend/
│   ├── api/
│   │   └── main.py          # FastAPI endpoints
│   ├── database/
│   │   ├── db.py            # SQLAlchemy models & connection
│   │   ├── schema.sql       # Database schema
│   │   ├── seed.py          # Initial data setup
│   │   └── download_real_data.py
│   ├── ml/
│   │   ├── feature_engineering.py
│   │   └── train_model.py
│   ├── services/
│   │   └── data_service.py
│   ├── .env.example         # Environment variables template
│   ├── .env                 # Local secrets (gitignored)
│   └── requirements.txt
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── services/
│   │   ├── App.js
│   │   └── index.css
│   ├── package.json
│   └── vite.config.js
├── data/
│   ├── features.csv
│   └── laliga_matches.csv
├── models/
│   └── model_meta.json
├── run.py                   # Main entry point
└── README.md

🔐 Security Notes

• Never commit .env file - It's gitignored for security
• Use .env.example as reference for required variables
• Rotate API keys if accidentally exposed
• All secrets are loaded from environment variables, not hardcoded

Environment Variables

# Database (Required)
DATABASE_URL=postgresql://username:password@localhost:5432/laliga_analytics

# API Keys
FOOTBALL_API_KEY=your_key_here          # Required - https://www.football-data.org/
RAPIDAPI_KEY=                            # Optional - for injury data
APIFOOTBALL_API_KEY=                     # Optional - alternative provider

# Features
ENABLE_INJURY_LOOKUP=1                  # 1 to enable, 0 to disable
UPCOMING_MATCH_LIMIT=5                  # Number of upcoming matches to fetch

🧹 Cleanup & Logs

Stop Running Servers

# PowerShell (Windows)
Get-NetTCPConnection -LocalPort 8001,8002 -State Listen | Stop-Process -Force

# Bash (Linux/Mac)
lsof -ti:8001,8002 | xargs kill -9

☁️ Deployment

This project is ready to deploy with:

• Backend on Render
• Frontend on Vercel

Backend (Render)

1. Go to Render and create a Web Service from this GitHub repo.
2. Render will detect render.yaml automatically.
3. Set required environment variables in Render:
   • DATABASE_URL
   • FOOTBALL_API_KEY
4. Optional variables:
   • RAPIDAPI_KEY
   • APIFOOTBALL_API_KEY
   • ENABLE_INJURY_LOOKUP (1 or 0)
   • UPCOMING_MATCH_LIMIT
5. Deploy and copy your backend URL (example: https://laliga-api.onrender.com).

Frontend (Vercel)

1. Import the same repo in Vercel.
2. Vercel will use vercel.json from project root.
3. Add env var in Vercel project settings:
   • VITE_API_BASE_URL=https://<your-render-backend-url>
4. Deploy.

Final Production CORS Step

After frontend is deployed, add this variable in Render backend:

• FRONTEND_URL=https://<your-vercel-domain>

Then redeploy backend once so CORS allows your live frontend domain.

📊 Model Information

• Training Data: Historical La Liga match data (2017-present)
• Features: Team form, head-to-head records, player injuries, home/away advantage
• Model: Ensemble learning approach (Random Forest + Gradient Boosting)
• Accuracy: ~68% on test set

🐛 Troubleshooting

Port Already in Use

# Find and stop process using port 8001 or 8002
netstat -ano | findstr :8001  # Windows
lsof -i :8001                 # Linux/Mac

Database Connection Failed

• Ensure PostgreSQL is running
• Verify DATABASE_URL is correct in .env
• Check database name matches: laliga_analytics

API Key Errors

• Verify FOOTBALL_API_KEY is valid at https://www.football-data.org/
• Injury data fails gracefully if RAPIDAPI_KEY is missing
• Use include_injuries=false for faster responses if API limits hit

🤝 Contributing

1. Create a feature branch: git checkout -b feature/YourFeature
2. Commit changes: git commit -m 'Add YourFeature'
3. Push to branch: git push origin feature/YourFeature
4. Open a Pull Request

📄 License

This project is licensed under the MIT License - see LICENSE file for details.

📧 Contact

Author: Niraj
GitHub: @niraj4rl
Email: pashte.niraj2005@gmail.com

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Last Updated: March 26, 2026
Repository: github.com/niraj4rl/Laliga_gin