Crate skillratings

Expand description

Skillratings provides a collection of well-known (and lesser known) skill rating algorithms, that allow you to assess a player’s skill level instantly.
You can easily calculate skill ratings instantly in 1vs1 matches, Team vs Team matches, or in tournaments / rating periods.
This library is incredibly lightweight (no dependencies by default), user-friendly, and of course, blazingly fast.

Currently supported algorithms:

Most of these are known from their usage in chess and various other games.
Click on the documentation for the modules linked above for more information about the specific rating algorithms, and their advantages and disadvantages.

Installation

If you are on Rust 1.62 or higher use cargo add to install the latest version:

cargo add skillratings

Alternatively, you can add the following to your Cargo.toml file manually:

[dependencies]
skillratings = "0.26"

Serde support

Serde support is gated behind the serde feature. You can enable it like so:

Using cargo add:

cargo add skillratings --features serde

By editing Cargo.toml manually:

[dependencies]
skillratings = {version = "0.26", features = ["serde"]}

Usage and Examples

Below you can find some basic examples of the use cases of this crate.
There are many more rating algorithms available with lots of useful functions that are not covered here.
For more information head over to the modules linked above or below.

Player-vs-Player

Every rating algorithm included here can be used to rate 1v1 games.
We use Glicko-2 in this example here.

use skillratings::{
    glicko2::{glicko2, Glicko2Config, Glicko2Rating},
    Outcomes,
};

// Initialise a new player rating.
// The default values are: 1500, 350, and 0.06.
let player_one = Glicko2Rating::new();

// Or you can initialise it with your own values of course.
// Imagine these numbers being pulled from a database.
let (some_rating, some_deviation, some_volatility) = (1325.0, 230.0, 0.05932);
let player_two = Glicko2Rating {
    rating: some_rating,
    deviation: some_deviation,
    volatility: some_volatility,
};

// The outcome of the match is from the perspective of player one.
let outcome = Outcomes::WIN;

// The config allows you to specify certain values in the Glicko-2 calculation.
let config = Glicko2Config::new();

// The glicko2 function will calculate the new ratings for both players and return them.
let (new_player_one, new_player_two) = glicko2(&player_one, &player_two, &outcome, &config);

// The first players rating increased by ~112 points.
assert_eq!(new_player_one.rating.round(), 1612.0);

Team-vs-Team

Some algorithms like TrueSkill or Weng-Lin allow you to rate team-based games as well.
This example shows a 3v3 game using TrueSkill.

use skillratings::{
    trueskill::{trueskill_two_teams, TrueSkillConfig, TrueSkillRating},
    Outcomes,
};

// We initialise Team One as a Vec of multiple TrueSkillRatings.
// The default values for the rating are: 25, 25/3 ≈ 8.33.
let team_one = vec![
    TrueSkillRating {
        rating: 33.3,
        uncertainty: 3.3,
    },
    TrueSkillRating {
        rating: 25.1,
        uncertainty: 1.2,
    },
    TrueSkillRating {
        rating: 43.2,
        uncertainty: 2.0,
    },
];

// Team Two will be made up of 3 new players, for simplicity.
// Note that teams do not necessarily have to be the same size.
let team_two = vec![
    TrueSkillRating::new(),
    TrueSkillRating::new(),
    TrueSkillRating::new(),
];

// The outcome of the match is from the perspective of team one.
let outcome = Outcomes::LOSS;

// The config allows you to specify certain values in the TrueSkill calculation.
let config = TrueSkillConfig::new();

// The trueskill_two_teams function will calculate the new ratings for both teams and return them.
let (new_team_one, new_team_two) = trueskill_two_teams(&team_one, &team_two, &outcome, &config);

// The rating of the first player on team one decreased by around ~1.2 points.
assert_eq!(new_team_one[0].rating.round(), 32.0);

Free-For-Alls and Multiple Teams

The Weng-Lin algorithm supports rating matches with multiple Teams.
Here is an example showing a 3-Team game with 3 players each.

use skillratings::{
    weng_lin::{weng_lin_multi_team, WengLinConfig, WengLinRating},
    MultiTeamOutcome,
};

// Initialise the teams as Vecs of WengLinRatings.
// Note that teams do not necessarily have to be the same size.
// The default values for the rating are: 25, 25/3 ≈ 8.33.
let team_one = vec![
    WengLinRating {
        rating: 25.1,
        uncertainty: 5.0,
    },
    WengLinRating {
        rating: 24.0,
        uncertainty: 1.2,
    },
    WengLinRating {
        rating: 18.0,
        uncertainty: 6.5,
    },
];

let team_two = vec![
    WengLinRating {
        rating: 44.0,
        uncertainty: 1.2,
    },
    WengLinRating {
        rating: 32.0,
        uncertainty: 2.0,
    },
    WengLinRating {
        rating: 12.0,
        uncertainty: 3.2,
    },
];

// Using the default rating for team three for simplicity.
let team_three = vec![
    WengLinRating::new(),
    WengLinRating::new(),
    WengLinRating::new(),
];

// Every team is assigned a rank, depending on their placement. The lower the rank, the better.
// If two or more teams tie with each other, assign them the same rank.
let rating_groups = vec![
    (&team_one[..], MultiTeamOutcome::new(1)),      // team one takes the 1st place.
    (&team_two[..], MultiTeamOutcome::new(3)),      // team two takes the 3rd place.
    (&team_three[..], MultiTeamOutcome::new(2)),    // team three takes the 2nd place.
];

// The weng_lin_multi_team function will calculate the new ratings for all teams and return them.
let new_teams = weng_lin_multi_team(&rating_groups, &WengLinConfig::new());

// The rating of the first player of team one increased by around ~2.9 points.
assert_eq!(new_teams[0][0].rating.round(), 28.0);

Expected outcome

Every rating algorithm has an expected_score function that you can use to predict the outcome of a game.
This example is using Glicko (not Glicko-2!) to demonstrate.

use skillratings::glicko::{expected_score, GlickoRating};

// Initialise a new player rating.
// The default values are: 1500, and 350.
let player_one = GlickoRating::new();

// Initialising a new rating with custom numbers.
let player_two = GlickoRating {
    rating: 1812.0,
    deviation: 195.0,
};

// The expected_score function will return two floats between 0 and 1 for each player.
// A value of 1 means guaranteed victory, 0 means certain loss.
// Values near 0.5 mean draws are likely to occur.
let (exp_one, exp_two) = expected_score(&player_one, &player_two);

// The expected score for player one is ~0.25.
// If these players would play 100 games, player one is expected to score around 25 points.
// (Win = 1 point, Draw = 0.5, Loss = 0)
assert_eq!((exp_one * 100.0).round(), 25.0);

Rating period

Every rating algorithm included here has a ..._rating_period that allows you to calculate a player’s new rating using a list of results.
This can be useful in tournaments, or if you only update ratings at the end of a certain rating period, as the name suggests.
We are using the Elo rating algorithm in this example.

use skillratings::{
    elo::{elo_rating_period, EloConfig, EloRating},
    Outcomes,
};

// We initialise a new Elo Rating here.
// The default rating value is 1000.
let player = EloRating { rating: 1402.1 };

// We need a list of results to pass to the elo_rating_period function.
let mut results = Vec::new();

// And then we populate the list with tuples containing the opponent,
// and the outcome of the match from our perspective.
results.push((EloRating::new(), Outcomes::WIN));
results.push((EloRating { rating: 954.0 }, Outcomes::DRAW));
results.push((EloRating::new(), Outcomes::LOSS));

// The elo_rating_period function calculates the new rating for the player and returns it.
let new_player = elo_rating_period(&player, &results, &EloConfig::new());

// The rating of the player decreased by around ~40 points.
assert_eq!(new_player.rating.round(), 1362.0);

Switching between different rating systems

If you want to switch between different rating systems, for example to compare results or to do scientific analyisis, we provide Traits to make switching as easy and fast as possible.
All you have to do is provide the right Config for your rating system.

Disclaimer: For more accurate and fine-tuned calculations it is recommended that you use the rating system modules directly.
The Traits are primarily meant to be used for comparisions between systems.

In the following example, we are using the RatingSystem (1v1) Trait with Glicko-2:

use skillratings::{
    glicko2::{Glicko2, Glicko2Config},
    Outcomes, Rating, RatingSystem,
};

// Initialise a new player rating with a rating value and uncertainty value.
// Not every rating system has an uncertainty value, so it may be discarded.
// Some rating systems might consider other values too (volatility, age, matches played etc.).
// If that is the case, we will use the default values for those.
let player_one = Rating::new(Some(1200.0), Some(120.0));
// Some rating systems might use widely different scales for measuring a player's skill.
// So if you always want the default values for every rating system, use None instead.
let player_two = Rating::new(None, None);

// The config needs to be specific to the rating system.
// When you swap rating systems, make sure to update the config.
let config = Glicko2Config::new();

// For 1v1 matches we are using the `RatingSystem` trait with the provided config.
// If no config is available for the rating system, pass in empty brackets.
// You may also need to use a type annotation here for the compiler.
let rating_system: Glicko2 = RatingSystem::new(config);

// The outcome of the match is from the perspective of player one.
let outcome = Outcomes::WIN;

// Calculate the expected score of the match.
let expected_score = rating_system.expected_score(&player_one, &player_two);
// Calculate the new ratings.
let (new_one, new_two) = rating_system.rate(&player_one, &player_two, &outcome);

// After that, access new ratings and uncertainties with the functions below.
assert_eq!(new_one.rating().round(), 1241.0);
// Note that because not every rating system has an uncertainty value,
// the uncertainty function returns an Option<f64>.
assert_eq!(new_one.uncertainty().unwrap().round(), 118.0);

Contributing

Contributions of any kind are always welcome!

Found a bug or have a feature request? Submit a new issue.
Alternatively, open a pull request if you want to add features or fix bugs.
Leaving other feedback is of course also appreciated.

Thanks to everyone who takes their time to contribute.

License

This project is licensed under either the MIT License, or the Apache License, Version 2.0.

Modules

dwz
The DWZ (Deutsche Wertungszahl) algorithm used in the german chess leagues alongside Elo.
DWZ continues to be enhanced over the years, while having similar scores to Elo.
egf
The EGF (European Go Federation) rating system is a variation of the Elo rating system, adapted for playing Go.
Used for calculating Go player ratings in Europe since 1998.
elo
The Elo algorithm, the most widespread rating system and the gold-standard in chess and other games.
Used in the official FIDE chess ratings, many online games, and the basis of even more rating systems.
fifa
The FIFA Men’s rating algorithm, officially called The FIFA/Coca-Cola World Ranking.
Used to rank men’s national football (or soccer) teams in FIFA-recognised competitions since 2018.
glicko
The Glicko algorithm, developed by Mark Glickman as an improvement on Elo.
It is still being used in some games in favour Glicko-2, such as Pokémon Showdown, Chess.com and Quake Live.
glicko2
The Glicko-2 algorithm, an improvement on Glicko and widely used in online games, like Counter Strike: Global Offensive, Team Fortress 2, Splatoon 2 or Lichess.
glicko_boost
The Glicko-Boost rating algorithm, an improvement on the Glicko rating system designed specifically for chess.
Allows for player advantages, designed for a chess outcome prediction competition.
ingo
The Ingo algorithm, the predecessor of DWZ and one of the first rating algorithms invented in 1947.
Sometimes still used in Xiangqi (“Chinese Chess”).
sticko
This is the Stephenson rating algorithm, nicknamed “Sticko” due to it being an improvement on the Glicko rating algorithm.
Allows for player advantages, and the winner of a chess outcome prediction competition.
trueskill
The TrueSkill rating algorithm, developed by Microsoft for Halo 3.
Used in the Halo games, the Forza Games, Tom Clancy’s: Rainbow Six Siege, and most Xbox Live games.
uscf
The USCF (US Chess Federation) Rating Algorithm, developed by Mark Glickman as an improvement on Elo.
Used to rate US Chess events in favour of Elo, and continues to be enhanced over the years.
weng_lin
A bayesian approximation method for online ranking. Similar to TrueSkill, but based on a logistical distribution.
Used in games such as Rocket League.

Structs

MultiTeamOutcome
Outcome for a free-for-all match or a match that involves more than two teams.

Enums

Outcomes
The possible outcomes for a match: Win, Draw, Loss.

Traits

MultiTeamRatingSystem
Rating system for more than two teams.
Rating
Measure of player’s skill.
RatingPeriodSystem
Rating system for rating periods.
RatingSystem
Rating system for 1v1 matches.
TeamRatingSystem
Rating system for two teams.