pyfootix 0.3.1

Footix is a Python package for sports betting analysis and modeling, with a focus on football (soccer).

🐓 Footix: Smart Sports Analysis & Prediction Toolkit

Features • Installation • Quick Start

🎮 Overview

Footix is your intelligent companion for sports analysis and prediction. Leveraging advanced machine learning algorithms and comprehensive data analysis, it helps you make data-driven decisions in sports betting and analysis.

✨ Features

• 📊 Advanced Data Analysis

  • Import data from multiple sports databases
  • Clean and preprocess sports statistics
  • Comprehensive historical data analysis

• 🤖 Smart Prediction Engine

  • Machine learning-powered outcome prediction

• 💰 Strategic Betting Tools

  • Risk assessment algorithms
  • Bankroll management system
  • Multiple betting strategy templates

🚀 Installation

Install Footix with pip:

pip install pyfootix

🎯 Quick Start

from footix.models.bayesian import BayesianModel
from footix.data_io.footballdata import ScrapFootballData


# Load match data (example: Ligue 1 fixtures)
dataset = ScrapFootballData(competition="FRA Ligue 1", season="2024-2025", path ="./data", force_reload=True).get_fixtures()

# Initialize and fit the Bayesian model
model = BayesianModel(n_teams=18, n_goals=20)
model.fit(X_train=dataset)

# Predict probabilities for a specific match
probas = model.predict(home_team="Marseille", away_team="Lyon").return_probas()
print(f"Home: {probas[0]:.2f}, Draw: {probas[1]:.2f}, Away: {probas[2]:.2f}")

📤 Exporting Predictions

You can export Bayesian predictions to JSON using:

• Core Python utilities for script/automation workflows

See the full tutorial in docs/source/prediction_export_tutorial.rst.

📝 License

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

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.