Javascript implementation of Weng-Lin Rating, using Bradley Terry model as described at https://jmlr.csail.mit.edu/papers/volume12/weng11a/weng11a.pdf

Installation

Add wenglin to your list of dependencies in package.json:

npm install --save wenglin  

Importing wenglin module

Use CommonJS's require

const {    
  Rater,    
  Team,    
  Player 
} = require('wenglin');  

Quick example

const rater = Rater();
const WINNER = 1; 
const LOSER = 0; 

const team_1 = new Team({players: [new Player()], score: WINNER});
const team_2 = new Team({players: [new Player()], score: LOSER});

const result = rater(team_1, team_2)

Modules

Player skill is represented by two parameters: mu and sigma
Mu is the actual skill of the player and sigma is its standard deviation.
Default values for mu and sigma are 25. and 25 / 3.

Rater is the main rating processor. It is initialized with BETA (sigma / 2) parameter by default so it can me omitted.

const rater = new Rater();
const rater = new Rater(BETA);

Once rater is initialized, it can be invoked with all teams as separated arguments:

rater(team_1, team_2, team_3, ...);

Team: Team represents a set of players, each with its skill data and a score. Score scale does not matter. It is compared between teams pairs and considered win, draw or loss.

const team_1 = new Team({players: [player_1, player_2, ...], score: 0});
const team_1 = new Team({players: [player_x, player_y, ...], score: 1});

Player: This class stores a player's skill, mu and sigma. Player class is initialized with default mu and sigma but can also be overwritten.

> const player = new Player();
> const player = new Player({mu: 25, sigma: 25 / 3,});

Player skill can be obtained calling its skill method

> const player = new Player({mu: 25, sigma: 25 / 3, ref: my_db_player});
> player.skill()
{ mu: 25, sigma_sq: 69.44444444444446, sigma: 8.333333333333334 }

Player can also store a reference to an object of your choice to keep track of players in your implementation and ease its usage after Rater results are returned.

> const my_db_player = {id: 'Player id'};
> const player = new Player({mu: 25, sigma: 25 / 3, ref: my_db_player});
> player.ref();
{ id: 'Player id' } 

Full usage example

Following example represents a match between tree teams of two players each. Team 1 loses the match and teams 2 and 3 win with a draw.

> const {    
  Rater,    
  Team,    
  Player
} = require('wenglin');  
> const team_1 = new Team({players: [new Player(), new Player()], score: 60});  
> const team_2 = new Team({players: [new Player(), new Player()], score: 80});  
> const team_3 = new Team({players: [new Player(), new Player()], score: 80});  
> const BETA = 4.16;
> Rater(BETA)(team_1, team_2, team_3);  

[  
  Player {  _mu: 21.071628993408070, _sigma: 8.018753738744802, _sigma_sq: 64.30041152263375, _ref: undefined},  
  Player {  _mu: 21.071628993408070, _sigma: 8.018753738744802, _sigma_sq: 64.30041152263375, _ref: undefined},  
  Player {  _mu: 26.964185503295965, _sigma: 8.018753738744802, _sigma_sq: 64.30041152263375, _ref: undefined},  
  Player {  _mu: 26.964185503295965, _sigma: 8.018753738744802, _sigma_sq: 64.30041152263375, _ref: undefined},  
  Player {  _mu: 26.964185503295965, _sigma: 8.018753738744802, _sigma_sq: 64.30041152263375, _ref: undefined},  
  Player {  _mu: 26.964185503295965, _sigma: 8.018753738744802, _sigma_sq: 64.30041152263375, _ref: undefined}  
]

Results

Rater output is an array of Player objects with the new rating and in the same input order. Original players are kept intact. After results are returned by rater, you can iterate over result items to obtain each reference and data at your side.

> const BETA = 4;
> const result = Rater(BETA)(team_1, team_2, ...);  
> player_1_skill = result.map(it => it.skill())  
[  
 {mu: 23.035814496704035, sigma: 8.17755635771097, sigma_sq: 66.8724279835391},  
 {mu: 23.035814496704035, sigma: 8.17755635771097, sigma_sq: 66.8724279835391},  
 {mu: 26.964185503295965, sigma: 8.17755635771097, sigma_sq: 66.8724279835391},  
 {mu: 26.964185503295965, sigma: 8.17755635771097, sigma_sq: 66.8724279835391}  
]