openskill 6.2.0
Multiplayer Rating System. No Friction.
Requires Python: ~=3.10
Keywords: ranking, trueskill, statistics, rating, math, rank

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  <img alt="Multiplayer Rating System. No Friction." src="https://i.imgur.com/QJUy18S.png">
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<p align="center" style="text-align: center">A faster and open license asymmetric multi-team, multiplayer rating system comparable to TrueSkill.</p>

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            src="https://raw.githubusercontent.com/vshymanskyy/StandWithUkraine/main/badges/StandWithUkraine.svg"
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    <a
        href="https://github.com/vivekjoshy/openskill.py/actions/workflows/main.yml">
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                alt="Tests"
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        href="https://codecov.io/gh/vivekjoshy/openskill.py">
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        href="https://openskill.me/en/latest/?badge=latest">
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## Description

![PyPI - Python Version](https://img.shields.io/pypi/pyversions/openskill) ![Conda (channel only)](https://anaconda.org/conda-forge/openskill/badges/version.svg) ![PyPI - Implementation](https://img.shields.io/pypi/implementation/openskill)

[![Discord](https://img.shields.io/discord/1127581396345556994?logo=discord&label=Official%20Discord%20Server&color=%235865F2&link=https%3A%2F%2Fdiscord.com%2Finvite%2F4JNDeHMYkM)](https://discord.com/invite/4JNDeHMYkM)

In the multifaceted world of online gaming, an accurate multiplayer rating system plays a crucial role. A multiplayer rating system measures and compares players' skill levels in competitive games to ensure balanced match-making, boosting overall gaming experiences. Currently, TrueSkill by Microsoft Research is a notable rating system, but gaming communities are yearning for faster, more adaptable alternatives.

Here are *some*, but not all, of the reasons you should drop TrueSkill
and bury Elo once and for all:

```mermaid
graph TD
    subgraph OpenSkill["OpenSkill Features"]

    subgraph Game["Game"]
    GF1[Multiplayer]
    GF2[Multifaction]
    GF3[Asymmetric Faction Size]
    GF4[Predict Win, Draw, Rank]
    GF5[Weights, Score Margins]
    end

    subgraph Technical["Technical"]
    TF1[100% Pure Python]
    TF2[CPython and PyPy Support]
    TF3[C-compiled Wheels]
    TF4[100% Test Coverage]
    TF5[5 Separate Models]
    end

    subgraph Performance["Performance"]
    PF1[150% faster than TrueSkill]
    PF2[Accuracy matches TrueSkill]
    PF3[Open License]
    PF4[Partial Play]
    PF5[Fine-grained Parameter Control]
    end

    end

    style GF1 fill:#f37231,color:#ffffff,stroke:#f37231
    style GF2 fill:#f37231,color:#ffffff,stroke:#f37231
    style GF3 fill:#f37231,color:#ffffff,stroke:#f37231
    style GF4 fill:#f37231,color:#ffffff,stroke:#f37231
    style GF5 fill:#f37231,color:#ffffff,stroke:#f37231

    style TF1 fill:#f37231,color:#ffffff,stroke:#f37231
    style TF2 fill:#f37231,color:#ffffff,stroke:#f37231
    style TF3 fill:#f37231,color:#ffffff,stroke:#f37231
    style TF4 fill:#f37231,color:#ffffff,stroke:#f37231
    style TF5 fill:#f37231,color:#ffffff,stroke:#f37231

    style PF1 fill:#f37231,color:#ffffff,stroke:#f37231
    style PF2 fill:#f37231,color:#ffffff,stroke:#f37231
    style PF3 fill:#f37231,color:#ffffff,stroke:#f37231
    style PF4 fill:#f37231,color:#ffffff,stroke:#f37231
    style PF5 fill:#f37231,color:#ffffff,stroke:#f37231
```


## Installation
```shell
pip install openskill
```

## Usage

The official documentation is hosted [here](https://openskill.me/en/stable/).
Please refer to it for details on how to use this library.

### Limited Example

```python
>>> from openskill.models import PlackettLuce
>>> model = PlackettLuce()
>>> model.rating()
PlackettLuceRating(mu=25.0, sigma=8.333333333333334)
>>> r = model.rating
>>> [[a, b], [x, y]] = [[r(), r()], [r(), r()]]
>>> [[a, b], [x, y]] = model.rate([[a, b], [x, y]])
>>> a
PlackettLuceRating(mu=26.964294621803063, sigma=8.177962604389991)
>>> x
PlackettLuceRating(mu=23.035705378196937, sigma=8.177962604389991)
>>> (a == b) and (x == y)
True
```

# Support
If you're struggling with any of the concepts, please search the discussions section to see if your question has already been answered.
If you can't find an answer, please open a new [discussion](https://github.com/vivekjoshy/openskill.py/discussions) and we'll try to help you out.
You can also get help from the official [Discord Server](https://discord.com/invite/4JNDeHMYkM). If you have a feature request, or want to report
a bug please create a new [issue](https://github.com/vivekjoshy/openskill.py/issues/new/choose) if one already doesn't exist.

# References
This project is originally based off the [openskill.js](https://github.com/philihp/openskill.js) package. All of the Weng-Lin models are based off the work in this wonderful [paper](https://jmlr.org/papers/v12/weng11a.html) or are the derivatives of algorithms found in it.

- Julia Ibstedt, Elsa Rådahl, Erik Turesson, and Magdalena vande Voorde. Application and further development of trueskill™ ranking in sports. 2019.
- Ruby C. Weng and Chih-Jen Lin. A bayesian approximation method for online ranking. Journal of Machine Learning Research, 12(9):267–300, 2011. URL: http://jmlr.org/papers/v12/weng11a.html.

## Implementations in other Languages
- [Javascript](https://github.com/philihp/openskill.js)
- [Elixir](https://github.com/philihp/openskill.ex)
- [Java](https://github.com/pocketcombats/openskill-java)
- [Kotlin](https://github.com/brezinajn/openskill.kt)
- [Lua](https://github.com/bstummer/openskill.lua)
- [Ruby](https://github.com/erdostom/openskill-ruby)