OpenSkill

OpenSkill: A faster asymmetric multi-team, multiplayer rating system - Published in JOSS (2024)

https://github.com/vivekjoshy/openskill.py

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
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Found .zenodo.json file
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DOI references
Found 3 DOI reference(s) in README
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Academic publication links
Links to: joss.theoj.org
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (11.0%) to scientific vocabulary

Keywords

elo openskill openskill-py pypy python ranking ranking-system rating rating-system

Keywords from Contributors

meshing standardization pde interpretability metaheuristic annotation parallel closember bids simulations

Last synced: 6 months ago · JSON representation ·

Repository

Multiplayer Rating System. No Friction.

Basic Info

Host: GitHub
Owner: vivekjoshy
License: mit
Language: Jupyter Notebook
Default Branch: main
Homepage: https://openskill.me
Size: 13.4 MB

Statistics

Stars: 316
Watchers: 7
Forks: 19
Open Issues: 4
Releases: 35

Topics

elo openskill openskill-py pypy python ranking ranking-system rating rating-system

Created about 5 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing Funding License Code of conduct Citation Codeowners Security

README.md

Multiplayer Rating System. No Friction.

A faster and open license asymmetric multi-team, multiplayer rating system comparable to TrueSkill.

Description

PyPI - Python Version Conda (channel only) PyPI - Implementation

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. 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 and we'll try to help you out. You can also get help from the official Discord Server. If you have a feature request, or want to report a bug please create a new issue if one already doesn't exist.

References

This project is originally based off the openskill.js package. All of the Weng-Lin models are based off the work in this wonderful paper 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

Owner

Name: Vivek Joshy
Login: vivekjoshy
Kind: user
Location: Kerala, India
Company: @OpenDebates

Website: https://taven.me
Twitter: daegontaven
Repositories: 1
Profile: https://github.com/vivekjoshy

Twenty-something polymath with delusions of grandeur.

Citation (CITATION.cff)

cff-version: "1.2.0"
authors:
- family-names: Joshy
  given-names: Vivek
  orcid: "https://orcid.org/0000-0003-2443-8827"
doi: 10.5281/zenodo.8280051
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Joshy
    given-names: Vivek
    orcid: "https://orcid.org/0000-0003-2443-8827"
  date-published: 2024-01-09
  doi: 10.21105/joss.05901
  issn: 2475-9066
  issue: 93
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 5901
  title: "OpenSkill: A faster asymmetric multi-team, multiplayer rating
    system"
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.05901"
  volume: 9
title: "OpenSkill: A faster asymmetric multi-team, multiplayer rating
  system"

GitHub Events

Total

Create event: 25
Release event: 10
Issues event: 6
Watch event: 38
Delete event: 20
Issue comment event: 29
Push event: 47
Pull request review event: 1
Pull request review comment event: 1
Pull request event: 35
Fork event: 6

Last Year

Create event: 25
Release event: 10
Issues event: 6
Watch event: 38
Delete event: 20
Issue comment event: 29
Push event: 47
Pull request review event: 1
Pull request review comment event: 1
Pull request event: 35
Fork event: 6

Committers

Last synced: 7 months ago

All Time

Total Commits: 331
Total Committers: 14
Avg Commits per committer: 23.643
Development Distribution Score (DDS): 0.375

Past Year

Commits: 44
Committers: 6
Avg Commits per committer: 7.333
Development Distribution Score (DDS): 0.159

Top Committers

Name	Email	Commits
Taven	8****n	207
Vivek Joshy	8****y	83
dependabot[bot]	4****]	15
allcontributors[bot]	4****]	8
Philihp Busby	p**p@g**m	5
github-actions[bot]	4****]	3
Jack McIvor	j**r@g**m	2
retrooper	r**r@p**m	2
Benjamin Mugnier	m**n@g**m	1
Calvin P. Colson	1****n	1
Jon Crall	e**c@g**m	1
Stephen Bartos	S**s@g**m	1
bstummer	5****r	1
takanoro	t**o@p**e	1

Committer Domains (Top 20 + Academic)

pm.me: 1 packetevents.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 25
Total pull requests: 139
Average time to close issues: about 1 month
Average time to close pull requests: 8 days
Total issue authors: 16
Total pull request authors: 14
Average comments per issue: 2.56
Average comments per pull request: 1.36
Merged pull requests: 91
Bot issues: 0
Bot pull requests: 73

Past Year

Issues: 5
Pull requests: 39
Average time to close issues: 23 days
Average time to close pull requests: 6 days
Issue authors: 3
Pull request authors: 7
Average comments per issue: 0.6
Average comments per pull request: 1.59
Merged pull requests: 27
Bot issues: 0
Bot pull requests: 14

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Top Authors

Issue Authors

vivekjoshy (8)
Jayensee (3)
philihp (1)
toshi71 (1)
mrkvicka22 (1)
sarim-zafar (1)
spookybear0 (1)
asyncth (1)
antl3x (1)
chrischen (1)
jonathan-scholz (1)
martinazapletalova (1)
ioannis12 (1)
erikj95 (1)
rudnirol (1)

Pull Request Authors

dependabot[bot] (55)
vivekjoshy (47)
allcontributors[bot] (13)
philihp (5)
github-actions[bot] (4)
retrooper (4)
takanoro (2)
bemug (2)
jack-mcivor (2)
Erotemic (1)
CalColson (1)
transifex-integration[bot] (1)
StephenBartos (1)
bstummer (1)

Top Labels

Issue Labels

enhancement (13) bug (6) help wanted (3) wontfix (2) question (2) documentation (2) invalid (1) rfc (1)

Pull Request Labels

dependencies (56) bug (8) enhancement (7) translations (4) python (3) documentation (1) tests (1)

Packages

Total packages: 2
Total downloads:
- pypi 9,247 last-month

Total dependent packages: 2
(may contain duplicates)
Total dependent repositories: 66
(may contain duplicates)
Total versions: 47
Total maintainers: 1

pypi.org: openskill

Multiplayer Rating System. No Friction.

Documentation: https://openskill.me/en/stable
License: mit
Latest release: 6.1.3
published 8 months ago

Versions: 35
Dependent Packages: 2
Dependent Repositories: 66
Downloads: 9,247 Last month

Rankings

Dependent repos count: 1.8%

Dependent packages count: 3.2%

Stargazers count: 4.6%

Downloads: 4.8%

Average: 4.8%

Forks count: 9.8%

Maintainers (1)

daegontaven

Funding

https://github.com/sponsors/vivekjoshy

Last synced: 6 months ago

conda-forge.org: openskill

Homepage: https://github.com/vivekjoshy/openskill.py
License: MIT
Latest release: 2.5.1
published over 3 years ago

Versions: 12
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Stargazers count: 26.8%

Dependent repos count: 34.0%

Average: 38.8%

Forks count: 43.4%

Dependent packages count: 51.2%

Last synced: 6 months ago

Dependencies

docs/requirements.txt pypi

Sphinx *
furo *

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actions/checkout v3 composite
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actions/checkout v3 composite
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