- The paper presents OpenSkill, a Python library that uses Bayesian approximation methods to compute fast and fair skill ratings in complex, multi-team gaming environments.
- It benchmarks performance against TrueSkill, achieving approximately 3x faster computation while maintaining comparable accuracy on datasets from Overwatch, Chess, and PUBG.
- The library offers multiple models and pairing strategies, providing flexibility for diverse applications in game development, sports analytics, and multi-agent research.
This paper introduces OpenSkill (2401.05451), a Python library for calculating player skill ratings in multiplayer, multi-team gaming environments. It positions itself as a faster, open-source alternative to proprietary systems like Microsoft's TrueSkill, specifically addressing the limitations of traditional systems like Elo and Glicko 2, which struggle with scenarios involving more than two players or teams of unequal sizes ("asymmetric" games) and multiple competing factions ("multi-faction" games).
The core problem OpenSkill tackles is providing fair and accurate skill ratings in complex game settings where player performance varies over time and depends on opponents. It aims to mitigate issues like "Elo Hell," where players feel stuck at a rank due to system flaws or team dynamics. The library implements a Bayesian approximation method based on the research by Weng and Lin (2011) [JMLR v12 p267].
Key Features and Implementation Details:
- Target Audience: Game developers, researchers using multi-agent environments (like Neural MMO (Suarez et al., 2019)), and applications needing ranking/matching (recommendation systems, sports analytics, dating apps).
- Core Model: Represents player skill using two values:
mu (μ), the estimated skill level, and sigma (σ), the uncertainty about that skill level. Ratings are updated after each match outcome using Bayesian inference.
- Models Offered: Includes five distinct models, with Plackett-Luce being the recommended default.
- Plackett-Luce: Based on the logistic distribution, extending the Bradley-Terry model by incorporating variance parameters. It models the probability of a specific team winning among multiple competitors.
- Thurstone-Mosteller: Based on the Gaussian distribution.
- Pairing Strategies: Supports both:
- Full Pairing: Uses all player comparison data within a match for maximum accuracy but higher computational cost.
- Partial Pairing: Uses only a subset of pairings for faster computation at the cost of some accuracy.
- Performance: Benchmarks show OpenSkill (specifically the Plackett-Luce model) achieves accuracy comparable to a popular Python TrueSkill implementation but performs significantly faster (reported as ~3x faster on Overwatch data and showing similar trends on Chess and PUBG datasets).
- Time Decay: Supports adjusting the
sigma value to account for skill decay due to player inactivity.
- Ease of Use: Provides a simple Python API. Installation is via
pip install openskill.
Basic Usage Example:
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from openskill.models import PlackettLuce
from openskill import Rating # Import Rating class directly for clarity
model = PlackettLuce()
rating1 = model.rating() # Player A on Team 1
rating2 = model.rating() # Player B on Team 1
rating3 = model.rating() # Player X on Team 2
rating4 = model.rating() # Player Y on Team 2
teams = [[rating1, rating2], [rating3, rating4]]
new_teams_ratings = model.rate(teams)
[[new_rating1, new_rating2], [new_rating3, new_rating4]] = new_teams_ratings
print(f"Player A new rating: {new_rating1}")
print(f"Player X new rating: {new_rating3}")
print(f"Teammates updated equally: {(new_rating1 == new_rating2) and (new_rating3 == new_rating4)}")
prob_A_beats_X = model.ordinal([new_rating1], [new_rating3]) # Can compare individuals or groups
print(f"Probability Player A's skill is greater than Player X's: {prob_A_beats_X:.2f}") |
Limitations and Future Work:
- Partial Play: While theoretically possible, incorporating scenarios where players participate for only part of a match is challenging due to a lack of verification data and standardized metrics.
- Weight Integration: The system currently does not allow weighting player contributions within a team differently (e.g., based on in-game performance metrics). This is identified as a key area for future development.
Practical Implications:
OpenSkill offers a practical, performant solution for developers needing a robust ranking system for modern multiplayer games with complex team structures. Its speed advantage over existing TrueSkill implementations can be crucial for maintaining a responsive user experience in online environments where frequent updates are needed. The availability of different models and pairing strategies allows developers to trade off accuracy and computational cost based on their specific needs. Its foundation in established Bayesian methods [JMLR v12 p267] provides a statistically sound basis for skill evaluation. The library's focus on Pythonic features, documentation, and test coverage enhances its usability in production environments.