Reference OpenSkill in documentation for Weng-Lin rating system

atomflunder · May 24, 2024 · 16a75d8 · 16a75d8
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diff --git a/README.md b/README.md
@@ -15,7 +15,7 @@ Currently supported algorithms:
 - [Glicko](https://docs.rs/skillratings/latest/skillratings/glicko/)
 - [Glicko-2](https://docs.rs/skillratings/latest/skillratings/glicko2/)
 - [TrueSkill](https://docs.rs/skillratings/latest/skillratings/trueskill/)
-- [Weng-Lin (Bayesian Approximation System)](https://docs.rs/skillratings/latest/skillratings/weng_lin/)
+- [Weng-Lin (OpenSkill)](https://docs.rs/skillratings/latest/skillratings/weng_lin/)
 - [FIFA Men's World Ranking](https://docs.rs/skillratings/latest/skillratings/fifa/)
 - [Sticko (Stephenson Rating System)](https://docs.rs/skillratings/latest/skillratings/sticko/)
 - [Glicko-Boost](https://docs.rs/skillratings/latest/skillratings/glicko_boost/)

diff --git a/src/lib.rs b/src/lib.rs
@@ -24,7 +24,7 @@
 //! - [Glicko](https://docs.rs/skillratings/latest/skillratings/glicko/)
 //! - [Glicko-2](https://docs.rs/skillratings/latest/skillratings/glicko2/)
 //! - [TrueSkill](https://docs.rs/skillratings/latest/skillratings/trueskill/)
-//! - [Weng-Lin (Bayesian Approximation System)](https://docs.rs/skillratings/latest/skillratings/weng_lin/)
+//! - [Weng-Lin (OpenSkill)](https://docs.rs/skillratings/latest/skillratings/weng_lin/)
 //! - [FIFA Men's World Ranking](https://docs.rs/skillratings/latest/skillratings/fifa/)
 //! - [Sticko (Stephenson Rating System)](https://docs.rs/skillratings/latest/skillratings/sticko/)
 //! - [Glicko-Boost](https://docs.rs/skillratings/latest/skillratings/glicko_boost/)

diff --git a/src/weng_lin.rs b/src/weng_lin.rs
@@ -2,15 +2,17 @@
 //! Used in games such as Rocket League.
 //!
 //! Developed by Ruby C. Weng and Chih-Jen Lin.
-//! Unlike with the other algorithms, there does not seem to exist a *short* name everyone agrees upon,
-//! so we are just calling it Weng-Lin, for short, after the researchers.
+//! We are calling the algorithm Weng-Lin, for short, after the researchers.
+//! This algorithm is also known online as "OpenSkill", in reference to the TrueSkill algorithm.
 //! But the proper name would be `A Bayesian Approximation Method for Online Ranking`.
 //!
 //! Developed specifically for online games with multiple teams and multiple players,
 //! this algorithm aims to be simpler and faster (~2.5 - 6.5x) than TrueSkill while yielding similar accuracy.
 //!
 //! While TrueSkill is based upon a Gaussian distribution, this algorithm is based upon a logistical distribution, the Bradley-Terry model.
 //!
+//! For the TrueSkill algorithm, please see [`TrueSkill`](crate::trueskill).
+//!
 //! # Quickstart
 //!
 //! This is the most basic example on how to use the Weng-Lin Module.  
@@ -58,6 +60,7 @@
 //! - [Bradley-Terry model Wikipedia](https://en.wikipedia.org/wiki/Bradley–Terry_model)
 //! - [Approximate Bayesian computation Wikipedia](https://en.wikipedia.org/wiki/Approximate_Bayesian_computation)
 //! - [Logistic distribution Wikipedia](https://en.wikipedia.org/wiki/Logistic_distribution)
+//! - [OpenSkill (Python Package)](https://openskill.me/en/stable/)
 
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};