Thanks Nils, it’s a good point and is on my todo list to dig further into how long it takes for something like FSAA to stabilise to something useful early on in a player’s career. Even with a fairly wide HDI at that stage, there’s still potentially benefits to its use

Not yet, but good idea!

Absolutely. Also, people started sharing big (for then anyway) pre-trained networks that made it much easier to get started. I built many models in my day job back then by fine tuning BERT and ImageNet that I would have struggled to train from scratch without investing massively in compute.

We can also split Massey Ratings into attack & defence:

🔴 LFC: best attack in the league paired with a mid-table defence
⚪️ ARS: Elite at both ends. They have the #1 defence and the #3 attack
🌳 Forest: A disaster at both ends of the pitch

Thanks to everyone who suggested features and reported issues. Your input shapes the package's development.

Questions or feedback welcome at pena.lt/y/contact

Install: pip install penaltyblog
GitHub: github.com/martineastwo...

📚 Interactive Colab notebooks are available in the docs - experiment with real examples without any local setup.

I'll be steadily expanding these over the coming weeks to cover all functionality in the package.

Docs: penaltyblog.readthedocs.io

🔧 Improved implied odds module:

- New logarithmic overround removal method for better accuracy
- Structured results instead of raw arrays
- Better handling of edge cases

Making it easier to work with bookmaker probabilities in your analyses.

💰 Expanded betting utilities:

- Kelly Criterion for multiple outcomes
- Arbitrage opportunity detection
- Value bet identification
- Hedge bet calculations
- Odds format conversion (decimal/fractional/American)

All functions now return structured outputs for easier integration.

📚 Docs: penaltyblog.readthedocs.io/en/latest/in...
💻 GitHub: github.com/martineastwo...
🐍 pip install penaltyblog

Feedback welcome, let me know what you build!

🔍 New: Flow Query DSL

Filter datasets with safe, Pythonic expressions:
- AST-parsed (no eval)
- Variables, regex, dates
- Access nested fields

📈 Goal models are now 5-10× faster

- Cython-powered analytical gradients for speed + stability
- Fine-tune with minimizer_options:

⚽ New: Pitch Plotting API

Build interactive football visualisations with:
- Multiple layouts & themes
- Scatter, heatmaps, arrows, comets
- Custom hover tooltips

There's also a new blog post here that explains more about the latest updates:

pena.lt/y/2025/06/10...

If you're interested in finding out more:

Why I built it 👉 pena.lt/y/2025/05/25...

Docs 👉 penaltyblog.readthedocs.io/en/latest/in...

At the heart of matchflow is the Flow class - a lazy, composable way to work with nested football data.

You can filter, group, and summarize JSON-like data without flattening or loading everything into memory.

👇 Example:

Thanks! Just to remove another variable - by simulating the result I definitely know which forecast is better as the simulated result is sampled from that particular forecast. When using the actual result you could argue that the true probabilities it's sampled from are unknown.