Package Updates

Pitch
- Initial release of interactive Pitch plotting library
MatchFlow
- Flow now has it's own query language, with support for boolean expressions and field comparisons via .query
Goals Models
- All Goals Model's .fit functions now take an optional dictionary of arguments to pass to scipy's optimiser
- All GoalsModels now fit using an optional gradient (defaults to True), which improves the fit time by approx 5-10x
FootballProbabilityGrid
- Updated class to include more betting markets
- Now supports fractional Asian handicaps and totals
- Optionally normalizes probabilities to sum to 1 (default: True)
- Calculations now use vectorized numpy operations for improved performance
- Caching of results for repeated queries to improve efficiency
Goal Expectancy
- Added support for removing overrounding from input probabilities
- Improved handling of edge cases in probability distributions
- Altered to using probabilities rather than odds
- Added more diagnostic output for debugging
- Optionally normalizes probabilities to sum to 1

Documentation Improvements

Added Pitch documentation
Updated Flow documentation with .query examples
Completely rewritten documentation for Goals Models and goal expectancy
Removed obsolete examples

- Python
Published by martineastwood 6 months ago

https://github.com/martineastwood/penaltyblog - v1.4.1

Package Updates

Fixed bug in Flow.cache executor logic

- Python
Published by martineastwood 8 months ago

https://github.com/martineastwood/penaltyblog - v1.4.0

v1.4.0 (2025-06-19)

Package Updates

Introduced optional FlowOptimizer for smart plan rewrites
- New optimize=True flag on all flows (off by default)
- Safe, conservative rewrites: pushdown, fusion, and simplification
- Enhanced .explain(compare=True) for before/after plan introspection
- Optimizer is backwards-compatible and fully opt-in
Added .plot_plan() on Flow and FlowGroup to visualize pipeline structure
.explain() now works on FlowGroup, and supports compare=True
New .with_schema({...}) method to cast and validate fields
- Example: Flow.with_schema({"x": int, "ts": parse_datetime})
Added .rolling_summary() to FlowGroup for windowed group summaries
(e.g. rolling 5-minute aggregates per player or team)
Added .time_bucket() to FlowGroup for time-based binning summaries
Added .show() method to pretty-print results using tabulate
Flow.collect() now supports optional progress bars during execution

Documentation Improvements

Refreshed documentation to include:
- FlowOptimizer and optimize=True
- .with_schema(), .rolling_summary(), .show()
- Plan introspection via .explain(compare=True) and .plot_plan()
- Enhanced type hints throughout the package for improved compatibility with mypy

- Python
Published by martineastwood 8 months ago

https://github.com/martineastwood/penaltyblog - v1.3.0

Package Updates

Initial release of MatchFlow

Documentation Improvements

Added MatchFlow documentation
Added MatchFlow recipes documentation
Added API references for all of penaltyblog
Added stub file for metric Cython code
Added stub file for models Cython code

- Python
Published by martineastwood 9 months ago

https://github.com/martineastwood/penaltyblog - v1.2.0

v1.2.0 (2025-04-10)

Package Updates

Updated Elo Ratings model to be more football-specific so that it now includes home field advantage and can predict draw probabilities
Added new Cythonised Ignorance Score metric
Added new Cythonised Multiclass Briar Score metric
RPS functions now raise a ValueError exception if outcome is out of bounds

Documentation Improvements

Updated Elo documentation
Added Pi Ratings documentation
Added examples for ignorance score
Added examples for multiclass briar score
Updated examples for RPS

- Python
Published by martineastwood 11 months ago

https://github.com/martineastwood/penaltyblog - v1.1.0

Performance Enhancements

Rewrote Dixon-Coles model using Cython, achieving approximately 250x speed improvement.
Rewrote Poisson model using Cython, achieving approximately 250x speed improvement.
Implemented Negative Binomial Goals Model in Cython for enhanced performance.
Added high-performance Cython implementation of the Bivariate Poisson Goals Model based on Karlis & Ntzoufras.
Introduced Cython implementation of the Bivariate Weibull Count Copula Goals Model based on Boshnakov et al.
Developed Pi Ratings System based on Constantinou et al.
Migrated ranked probability score functions to Cython for improved speed.

Package Updates

Temporarily removed Stan-based models due to dependency management challenges. Investigating improved packaging strategies for future reintegration.
Temporarily removed Rue and Salvesen model pending revision to accurately reflect its intended methodology (previously implemented as a hybrid Dixon-Coles variant).

Documentation Improvements

Updated and expanded model examples in the documentation.
Enhanced type hints throughout the package for improved compatibility with mypy.
Updated documentation to pydata Sphinx theme.

CI/CD and Testing

Expanded GitHub Actions workflows to perform unit tests across all supported Python versions.
Extended GitHub Actions workflows to perform unit tests on Windows, macOS, and Linux.
Configured GitHub Actions to automatically build wheels for all supported Python versions across Windows, macOS, and Linux.

- Python
Published by martineastwood 11 months ago

https://github.com/martineastwood/penaltyblog - v1.04

Moved Stan model code into separate files as the previous method of loading from a string via a temporary file was causing issues for some users on Windows