kickscore

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kickscore is the dynamic skill rating system powering Kickoff.ai <https://kickoff.ai/>_.

In short, kickscore can be used to understand & visualize the skill of players (or teams) competing in pairwise matches, and to predict outcomes of future matches. It extends the Elo rating system <https://en.wikipedia.org/wiki/Elo_rating_system>_ and TrueSkill <https://en.wikipedia.org/wiki/TrueSkill>_.

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Getting started

To install the latest release directly from PyPI, simply type::

pip install kickscore

To get started, you might want to explore one of these notebooks:

• Basic example illustrating the API <examples/kickscore-basics.ipynb>_ (interactive version <https://colab.research.google.com/github/lucasmaystre/kickscore/blob/master/examples/kickscore-basics.ipynb>__)
• Visualizing the history of the NBA <examples/nba-history.ipynb>_ (interactive version <https://colab.research.google.com/github/lucasmaystre/kickscore/blob/master/examples/nba-history.ipynb>__)

References

• Lucas Maystre, Victor Kristof, Matthias Grossglauser, Pairwise Comparisons with Flexible Time-Dynamics_, KDD 2019

.. _Pairwise Comparisons with Flexible Time-Dynamics: https://arxiv.org/abs/1903.07746

.. |build-status| image:: https://travis-ci.org/lucasmaystre/kickscore.svg?branch=master :alt: build status :scale: 100% :target: https://travis-ci.org/lucasmaystre/kickscore

.. |coverage| image:: https://codecov.io/gh/lucasmaystre/kickscore/branch/master/graph/badge.svg :alt: code coverage :scale: 100% :target: https://codecov.io/gh/lucasmaystre/kickscore

.. |nba-history| image:: https://lum-public.s3-eu-west-1.amazonaws.com/kickscore-nba-history.svg :alt: evolution of NBA teams' skill over history :scale: 100%