About Me

Hi, I'm Dr Martin Eastwood. This is my home for exploring football analytics, where I bridge the gap between rigorous statistical theory and practical industry application.

On my blog, I explore how advanced statistical techniques, particularly Bayesian modeling and machine learning, can provide a deeper understanding of the beautiful game. My focus is on methods that properly handle uncertainty and incorporate domain knowledge - essential for making robust decisions in a sport often defined by limited data.

I'm the author of the open-source Python library, penaltyblog, which has been downloaded over 160,000 times. I'm proud that my tools and models are actively used by clubs, governing bodies, player agencies, and analysts globally to find a competitive edge.

What You'll Find Here

• Technical Deep-Dives: My detailed explorations of statistical methods applied to football, from Expected Threat calculations to detecting market inefficiencies through Bayesian inference.
• Implementation Guides: Step-by-step tutorials showing how to build and deploy analytics solutions in Python, with complete code and mathematical foundations.
• Open-Source Tools: The penaltyblog Python package, providing tested, documented implementations ready for professional deployment.
• Research Notes: My explorations of emerging techniques and experimental approaches that push the boundaries of football analytics.

My Technical Philosophy

My work emphasizes Bayesian statistical methods for their ability to quantify uncertainty and incorporate prior knowledge. This is crucial for the small sample sizes and inherent variability of football data and allows for more nuanced insights than traditional methods.

I prioritize transparency and reproducibility in everything I do. The code for my projects is available on GitHub, with comprehensive documentation to ensure analyses can be validated, extended, and adapted.

Community & Industry Engagement

Through the penaltyblog project, I actively engage with the wider sports analytics community to push the boundaries of what's possible with open-source tools. I am always interested in:

• Academic Contribution: Serving on review panels for Machine Learning conferences and journals, helping to evaluate and shape the latest research in the field
• Knowledge Sharing: Discussing how clubs and analysts are implementing Bayesian methods to improve decision-making
• Open-Source Contributions: Collaborating on new features for the penaltyblog library to make it more robust for the community
• Research Partnerships: Exploring novel modeling approaches and proof-of-concepts that bridge the gap between academia and the industry

If you are using penaltyblog in your workflow or have feedback on the project, I'd love to hear from you. Please get in touch.

My Background

My background combines statistical theory with software engineering. I am interested in the intersection of high-performance computing (like Cython and Rust) and Bayesian inference, ensuring that complex models are not just theoretically sound, but computationally efficient.

Using My Work