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coverage](https://codecov.io/gh/lucasmaystre/kickscore/branch/master/graph/badge.svg)](https://codecov.io/gh/lucasmaystre/kickscore)\n\n`kickscore` is the dynamic skill rating system powering [Kickoff.ai](https://kickoff.ai/).\n\nIn short, `kickscore` can be used to understand \u0026 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).\n\n![evolution of NBA teams\\' skill over history](https://lum-public.s3-eu-west-1.amazonaws.com/kickscore-nba-history.svg)\n\n## Getting started\n\nTo install the latest release directly from PyPI, simply type:\n\n    pip install kickscore\n\nTo get started, you might want to explore one of these notebooks:\n\n- [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))\n- [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))\n\n## References\n\n- Lucas Maystre, Victor Kristof, Matthias Grossglauser, [Pairwise Comparisons with Flexible Time-Dynamics](https://arxiv.org/abs/1903.07746), KDD 2019\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucasmaystre%2Fkickscore","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flucasmaystre%2Fkickscore","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucasmaystre%2Fkickscore/lists"}