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Martin Eastwood @martineastwood.co.uk · 09/06/2026

🚀 penaltyblog v1.11.0 is out. This release adds: ⚡ predict_many() for fast batch forecasting 🏟️ Neutral venue support 📈 A bundled pre-trained xT model 🚄 ~4× faster Weibull Copula fitting 🤖 Context7 MCP documentation support

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Martin Eastwood @martineastwood.co.uk · 27/03/2026

Looking forward to the Field Of Play Conference today. If you’re there as well then come over and say hi 👋

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Martin Eastwood @martineastwood.co.uk · 28/02/2026

🚀 penaltyblog v1.9.0 is live! 🐍⚽️ New in this version: ✅ create_dixon_coles_grid(): Use lambdas from external ML models. ✅ goal_expectancy_extended(): Infer rho/lambdas from odds. 🛠️ Improved quarter lines (2.25/2.75) logic for totals market. ⚡️ More optimisations to FootballProbabilityGrid

A Python code snippet demonstrating the new 'create_dixon_coles_grid' function in the penaltyblog library. The code shows how to initialize a probability grid using expected goals (lambdas) for the home and away teams, and then calculate home win probabilities, over/under 2.5 goal totals, and Asian Handicaps directly from that grid.

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Martin Eastwood @martineastwood.co.uk · 08/01/2026

Bayesian goal models are back in penaltyblog v1.8.0 - and this time, they’re dependency-free! ⚽️📈

A dark-themed code editor window showing Python code using the penaltyblog library. It demonstrates initializing a HierarchicalBayesianGoalModel with home/away goals and teams, fitting the model using MCMC sampling with parameters like 2000 samples and 4 chains, and finally predicting match probabilities for an Arsenal vs. Manchester City fixture

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Martin Eastwood @martineastwood.co.uk · 29/12/2025

⚽ penaltyblog v1.7.1 is now available: Added params_array and param_indices functions to goal models to make it easier to work with the model's parameters. Thank you to Sebastian Velandia for this contribution! pip install --upgrade penaltyblog

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Martin Eastwood @martineastwood.co.uk · 09/12/2025

⚽ penaltyblog v1.7.0 is now available: Direct integration with the Opta (Stats Perform) API. You can now stream matches & events lazily without downloading the JSON first. Includes helpers for human-readable filtering (no more memorising IDs). pip install --upgrade penaltyblog

Code example showing how the penaltyblog python package can now integrate directly with the StatsPerform Opta API to download data directly.

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Martin Eastwood @martineastwood.co.uk · 04/12/2025

Have been doing Advent of Code in Nim and Kotlin this year and enjoying learning both. I can see Nim becoming one of my favourite languages!

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Martin Eastwood @martineastwood.co.uk · 20/10/2025

New article: "Shrinkage, Uncertainty, and Son Heung-min: Using Bayesian Methods to Identify Finishing Ability" which discusses using a Bayesian hierarchical approach to quantifying player finishing ability, with credible intervals to express uncertainty. pena.lt/y/2025/10/01...

pena.lt

Shrinkage, Uncertainty, and Son Heung-min: Using Bayesian Methods to Identify Finishing Ability

Why most finishing metrics are flawed and how a Bayesian approach gives us a truer picture of a player's finishing ability...

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Martin Eastwood @martineastwood.co.uk · 17/10/2025

🎉 penaltyblog v1.6.1 is out! ✨ What's new: - Python 3.14 support - scipy 1.16+ compatibility - Better numerical stability for Negative Binomial model - New Colab notebook for implied probabilities example pip install --upgrade penaltyblog

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Martin Eastwood @martineastwood.co.uk · 07/10/2025

Here's how the Premier League table really looks according to Massey Ratings, which account for strength of schedule. 📈 Arsenal (+1.5) & Man City (+1.3) are clear strongest overall 😬 Man Utd (-0.2) rank in the bottom half 📉 West Ham & Forest (-1.2) are worst teams by far

Massey Ratings for the English Premier League

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Martin Eastwood @martineastwood.co.uk · 23/09/2025

⚽ penaltyblog v1.6.0 is now available! 📊 MatchFlow updates: - SQL-style joins for nested football data (left, right, outer, inner, anti) - Cloud storage support: read/write directly to AWS S3, Google Cloud Storage, and Azure Blob - Automatic type inference for join keys pip install penaltyblog

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Martin Eastwood @martineastwood.co.uk · 18/08/2025

🚀 New article on my blog walking through the latest updates to the penaltyblog python package for football (soccer) analytics & betting ✅ New interactive pitch plots ✅ 5-10× faster goal models ✅ New Flow query DSL 👉 pena.lt/y/2025/08/14...

pena.lt

Penaltyblog v1.5.0: Faster Models, Smarter Queries, and a Sharper Edge

v1.5.0 delivers interactive charts, faster models, upgraded football probability grid, and a powerful Flow query language - all designed to make your analysis sharper and quicker...

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Martin Eastwood @martineastwood.co.uk · 15/08/2025

🚀 penaltyblog v1.5.0 is here! ✅ Interactive pitch plots ✅ 5-10× faster goal models ✅ New Flow query DSL What’s new 👇

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Martin Eastwood @martineastwood.co.uk · 16/07/2025

📊 New article on my blog: How Accurate Are Soccer Odds? A Data Dive into 250 Million Betting Lines 🔍 How sharp are different bookmakers? 📈 How accurate are bookmaker's odds? 🎯 Are the odds well-calibrated? ➡️ pena.lt/y/2025/07/16...

pena.lt

How Accurate Are Soccer Odds? A Data Dive into 250 Million Betting Lines

A data-driven deep dive into how accurately bookmakers price global soccer markets...

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Martin Eastwood @martineastwood.co.uk · 19/06/2025

🚀 penaltyblog v1.4.0 is out! Now includes a query plan optimiser for smarter Flow pipelines: • Optional FlowOptimizer for smart rewrites (optimize=True) • New .plot_plan() for pipeline viz • .with_schema() for field validation • Rolling- and time-based summaries github.com/martineastwo...

github.com

GitHub - martineastwood/penaltyblog: ⚽ High-performance football analytics toolkit: build data pipelines, scrape data, model matches, rank teams, and bet smarter | Powered by pena.lt/y/blog 🚀

⚽ High-performance football analytics toolkit: build data pipelines, scrape data, model matches, rank teams, and bet smarter | Powered by pena.lt/y/blog 🚀 - martineastwood/penaltyblog

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Martin Eastwood @martineastwood.co.uk · 14/04/2025

New on my blog: Pi Ratings — a football-first alternative to Elo. ✅ Handles draws ✅ Uses score margins ✅ Beats Elo on predictions Includes code, benchmarks & more: 👉 pena.lt/y/2025/04/14...

pena.lt

Pi Ratings: The Smarter Way to Rank Football Teams

A smarter, football-focused alternative to Elo — using Pi Ratings to track team strength and predict matches...

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Martin Eastwood @martineastwood.co.uk · 10/04/2025

penaltyblog v1.2.0 is released! ✅ Elo Ratings now football-specific (home advantage + draw probabilities) ⚡️ Fast Cython implementations of Ignorance Score & Multiclass Brier Score 📝 Updated docs / examples for Elo, Pi Ratings, RPS & more github.com/martineastwo...

github.com

GitHub - martineastwood/penaltyblog: ⚽ High-performance football analytics toolkit: scrape data, model matches, rank teams, and bet smarter | Powered by pena.lt/y/blog 🚀

⚽ High-performance football analytics toolkit: scrape data, model matches, rank teams, and bet smarter | Powered by pena.lt/y/blog 🚀 - martineastwood/penaltyblog

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Reposted by Martin Eastwood

Nils Mackay @nilsmackay.bsky.social · 01/04/2025

[NEW] First blog in a long time! In which I show a pretty straightforward way to derive teams' attacking and defensive strengths from bookmaker odds. This method is not only easy to implement, but also very accurate compared to results-based models. mackayanalytics.nl/2025/04/01/g...

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Martin Eastwood @martineastwood.co.uk · 18/03/2025

Looking forward to the Field Of Play Conference today. If you’re there as well today then come over and say hi 👋

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Martin Eastwood @martineastwood.co.uk · 16/03/2025

⚽ New blog post - which football prediction model is the best? Poisson, Weibull, or Negative Binomial — each has its strengths and weaknesses. Learn how to optimise them for accurate match predictions using my penaltyblog Python package. pena.lt/y/2025/03/10... #FootballAnalytics #DataScience

pena.lt

Football Prediction Models: Which Ones Work the Best?

Comparing football goals models — Poisson, Dixon-Coles, and more — to see which predicts best and how to optimize them...

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Martin Eastwood @martineastwood.co.uk · 15/03/2025

penaltyblog v1.1.0 is released! 🔥 250x faster models with Cython 📊 New models: Negative Binomial, Weibull, ZIP & Bivariate 🔢 Pi Ratings System for ranking teams 🔗 Full changelog: penaltyblog.readthedocs.io/en/master/changelog/index.html #Python #SportsAnalytics #MachineLearning

penaltyblog.readthedocs.io

Changelog — penaltyblog documentation

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Martin Eastwood @martineastwood.co.uk · 27/02/2025

@pysport.org Looks like you've got the wrong Martin on your website as the contributor for my penaltyblog python package - opensource.pysport.org/project/pena...

opensource.pysport.org

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Martin Eastwood @martineastwood.co.uk · 12/12/2024

It's been a while, but I've finally released a new version of my penaltyblog python package that replaces PyMC with Stan for the Bayesian models. This should make it much easier to install, and it will now import much faster too. github.com/martineastwo...

github.com

GitHub - martineastwood/penaltyblog: Library from http://pena.lt/y/blog for modelling and working with football (soccer) data

Library from http://pena.lt/y/blog for modelling and working with football (soccer) data - martineastwood/penaltyblog

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Martin Eastwood @martineastwood.co.uk · 07/12/2024

Been meaning to update my penaltyblog python package for a while now but it depends on PyMC for the Bayesian modelling and I can no longer get PyMC's dependencies to install via pip. Ah well, guess it's time to start learning Stan instead.

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