Football Prediction System

A comprehensive machine learning system for predicting football match outcomes with automated data collection, advanced modeling, and an interactive visualization dashboard.

👥 Contributors

Special thanks to the following contributors for their valuable contributions:

• Kartik Vadhawana (GitHub Username: Vkartik-3) - LinkedIn Profile
• Jas Shah (GitHub Username: Arbiter09) - LinkedIn Profile

📋 Table of Contents

• Overview
• Features
• System Architecture
• Tech Stack
• Data Pipeline
• Machine Learning Models
• Project Structure
• Installation
• Configuration
• Usage
• API Documentation
• Development Roadmap
• Contributing
• License

🔭 Overview

The Football Prediction System is an end-to-end machine learning platform that collects, processes, and analyzes football match data to predict match outcomes. The system features automated data collection pipelines, advanced machine learning models, and an interactive dashboard for visualizing predictions and model performance.

Current prediction accuracy stands at 70% with F1 scores of 47.1% (RandomForest) and 52.6% (Ensemble model).

✨ Features

Data Collection & Processing

• Multi-source data collection (web scraping, APIs, databases)
• Automated ETL workflows with Apache Airflow
• Comprehensive feature engineering pipeline
• Data validation and quality checks

Machine Learning

• Multiple prediction models (RandomForest, XGBoost, Ensemble)
• Model versioning and performance tracking
• Hyperparameter optimization
• Advanced evaluation metrics

Web Application

• FastAPI backend with RESTful endpoints
• React frontend with responsive design
• Interactive dashboards and visualizations
• Model comparison and performance analysis tools

Team Analysis

• Historical performance statistics
• Head-to-head comparisons
• Form analysis and trending metrics
• Fixture difficulty assessment

🏗️ System Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  Data Sources   │───▶│  Data Pipeline  │───▶│  Feature Store  │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                                       │
                                                       ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Dashboard     │◀───│   API Server    │◀───│  ML Models      │
└─────────────────┘    └─────────────────┘    └─────────────────┘

🛠️ Tech Stack

Backend

• Python 3.8+
• FastAPI: Web framework
• SQLAlchemy: ORM for database interactions
• Apache Airflow: Workflow automation
• Pandas/NumPy: Data processing
• Scikit-learn/XGBoost: Machine learning
• Optuna: Hyperparameter optimization

Frontend

• React: UI library
• TypeScript: Type-safe JavaScript
• Redux: State management
• D3.js/Chart.js: Data visualization
• Tailwind CSS: Styling

Data Storage

• PostgreSQL: Main database
• Redis: Caching
• S3/MinIO: Model artifact storage

DevOps

• Docker: Containerization
• GitHub Actions: CI/CD (planned)
• ELK Stack: Monitoring (planned)

📊 Data Pipeline

The data pipeline is built using Apache Airflow to automate the collection, processing, and storage of football match data:

1. Data Collection

   • Web scraping from multiple sources (FBref, Transfermarkt, WhoScored)
   • API integrations for odds, weather, and supplementary data
   • Historical database queries

2. Data Processing

   • Cleaning and normalization
   • Feature engineering
   • Data validation and quality control

3. Feature Generation

   • Team performance metrics (form, streaks, goals)
   • Head-to-head statistics
   • Match context features (timing, weather, location)
   • Advanced metrics (xG, PPDA, pressure metrics)

4. Storage

   • Raw data in file storage
   • Processed data in PostgreSQL database
   • Feature store for model training

🧠 Machine Learning Models

Current Models

• RandomForest: Base model with core features (~47.1% F1)
• XGBoost: Gradient boosting implementation
• Ensemble: Weighted combination of models (~52.6% F1)

Planned Enhancements

• Stacked Ensemble: Neural network meta-learner on top of base models
• Deep Learning: LSTM/Transformer networks for sequence modeling
• Feature Selection: Automated feature importance analysis
• AutoML: Automatic model selection and optimization

Model Versioning

The system implements a comprehensive model versioning system that tracks:

• Training data snapshot
• Hyperparameters
• Feature set
• Performance metrics
• Timestamps

📁 Project Structure

football_prediction_system/
├── data_pipeline/                # Data collection and ETL processes
│   ├── airflow/                  # Airflow DAGs and plugins
│   ├── collectors/               # Data collection modules
│   └── processors/               # Data processing logic
├── app/                          # Web application
│   ├── api/                      # FastAPI backend
│   │   ├── models/               # Database models
│   │   ├── routers/              # API endpoints
│   │   └── services/             # Business logic
│   └── frontend/                 # React frontend
│       ├── components/           # UI components
│       ├── pages/                # Application pages
│       └── services/             # API clients
├── ml/                           # Machine learning
│   ├── models/                   # Model definitions
│   ├── features/                 # Feature engineering
│   ├── training/                 # Training scripts
│   └── evaluation/               # Model evaluation
├── database/                     # Database migrations and scripts
├── tests/                        # Test suite
├── configs/                      # Configuration files
└── scripts/                      # Utility scripts

🚀 Installation

Prerequisites

• Python 3.8+
• Node.js 14+
• PostgreSQL 12+
• Docker and Docker Compose (optional)

Clone the Repository

git clone https://github.com/yourusername/football-prediction-system.git
cd football-prediction-system

Backend Setup

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up the database
python -m scripts.setup_db

# Run database migrations
alembic upgrade head

Frontend Setup

cd app/frontend
npm install

Airflow Setup

# Set Airflow home directory
export AIRFLOW_HOME=$(pwd)/data_pipeline/airflow

# Initialize the database
airflow db init

# Create an admin user
airflow users create \
    --username admin \
    --firstname Admin \
    --lastname User \
    --role Admin \
    --email [email protected] \
    --password admin

# Start the webserver and scheduler
airflow webserver --port 8080 &
airflow scheduler &

⚙️ Configuration

Configuration files are stored in the configs directory. Copy the example configs and modify as needed:

cp configs/app.example.yaml configs/app.yaml
cp configs/db.example.yaml configs/db.yaml
cp configs/airflow.example.yaml configs/airflow.yaml

Key configuration parameters:

• Database connection strings
• API keys for data sources
• Model hyperparameters
• Logging settings
• Airflow settings

📖 Usage

Running the API Server

cd app
uvicorn api.main:app --reload

Running the Frontend

cd app/frontend
npm start

Accessing the Application

• Frontend: http://localhost:3000
• API: http://localhost:8000
• API Documentation: http://localhost:8000/docs
• Airflow: http://localhost:8080

Training Models

# Train a specific model
python -m ml.training.train --model random_forest

# Train all models
python -m ml.training.train --all

Running Predictions

# Generate predictions for upcoming matches
python -m ml.predict --upcoming

# Test model on historical data
python -m ml.evaluate --model ensemble --test-set recent

📘 API Documentation

The API documentation is automatically generated using Swagger UI and can be accessed at /docs endpoint when the API server is running.

Key Endpoints

Endpoint	Method	Description
/api/v1/matches	GET	List matches with filtering options
/api/v1/predictions	GET	Get predictions for matches
/api/v1/teams/{team_id}	GET	Get team information
/api/v1/models	GET	List available models
/api/v1/models/{model_id}/metrics	GET	Get model performance metrics
/api/v1/head-to-head/{team1_id}/{team2_id}	GET	Get head-to-head statistics

🗓️ Development Roadmap

Phase 3 (Current)

• Implement stacked ensemble with neural network
• Optimize hyperparameters with Optuna
• Add player-level features
• Fix Airflow configuration issues

Phase 4

• Implement WebSockets for live updates
• Create real-time prediction updates
• Set up prediction change alerts
• Develop live visualization components

Phase 5

• Add computer vision for tactical analysis
• Implement NLP for news/social media
• Develop player performance metrics
• Create natural language match reports

Phase 6

• Build formation simulation tools
• Implement what-if scenarios
• Create tactical pattern recognition
• Develop match simulation

Phase 7

• Set up CI/CD with GitHub Actions
• Containerize application with Docker
• Implement database optimization
• Set up monitoring with ELK stack

👥 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

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