AFL Player Performance Analyzer

🏉 AI-powered AFL player analytics for insights on matchups, injuries, and performance.

🔍 Leverage machine learning to uncover trends, predict outcomes, and analyze key AFL statistics.

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📖 Project Overview

The AFL Player Performance Analyzer is an open-source initiative using machine learning and AI to analyze Australian Football League (AFL) player performance. By evaluating data from recent seasons, this project aims to provide:

• 📊 Performance Insights – Goals, disposals, and efficiency tracking.
• ⚔️ Player Matchups – Compare head-to-head matchups with statistical analysis.
• 🏥 Injury Prediction – Forecast injury likelihood based on historical and contextual data.
• 🔄 Game & Team Impact – Identify player contributions and game-changing moments.

💡 All insights are presented in an interactive dashboard, making it accessible for:

• AFL fans who want deeper insights.
• Analysts seeking data-driven decision-making.
• Bettors looking for an edge in predictions.

🔥 If you find this project useful, please consider ⭐ starring it! It helps others discover it.

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🎯 Project Goals

✅ Revolutionize AFL Analytics – Use advanced AI methods to derive novel insights.
✅ Open-Source Collaboration – Build in public, encouraging contributions from the community.
✅ Interactive Insights – Present data through an intuitive dashboard for seamless exploration.
✅ Disruption – Shift the industry’s approach to AFL analysis with modern, scalable techniques.

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🚀 Features

🔹 Core Metrics:

• Track player performance trends (goals, disposals, efficiency).
• Compare matchups with contextual analytics.

🔹 Advanced Predictions:

• Injury likelihood modeling using past injuries & game context.
• Player & team impact metrics to analyze game-changing moments.

🔹 Interactive Dashboard:

• Filter by player, team, season, or match.
• Visualize trends, matchups, and impact scores.

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📂 Project Structure

AFL-Player-Performance-Analyzer/
├── data/                  # Raw and processed datasets
├── notebooks/             # Jupyter notebooks for exploration and prototyping
├── src/                   # Source code for ML models and analysis
│   ├── models/            # Machine learning models
│   ├── visualization/     # Code for generating visualizations
├── interface/             # Interactive dashboard or web app
├── tests/                 # Unit and integration tests
├── .gitignore
├── requirements.txt       # Python dependencies
├── README.md              # Project documentation
├── LICENSE

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▶ Getting Started

🔧 Prerequisites

Ensure Python 3.8+ is installed. Install dependencies using:

pip install -r requirements.txt

▶ Running the Dashboard

streamlit run interface/app.py

▶ Running in Jupyter Notebook

jupyter lab

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🛠 Contributing

We welcome contributions! Here’s how you can help:

1. Fork the repository.
2. Create a branch (feature-new-analysis).
3. Commit your changes.
4. Submit a Pull Request (PR).

🔍 Check open issues to find something to work on!

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📊 Data Sources

• Raw Data: data/raw/afl_player_stats_2023_2024.csv
  • Source: Generated using fitzRoy R package.
  • Seasons: 2023–2024.

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⭐ Support

If you find this project useful:

• Star the repo ⭐ (top right corner)
• Share it on social media
• Suggest improvements in the Issues tab

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📜 License

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

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