Glicko2 sport players' rating algorithm for the JVM. Details on ELO and Glicko systems can be found at ELO Wikipedia, Glicko Wikipedia, or Glicko-2 Example. This project is used for computing ELO ratings in the squash players ranking system, for example in Waterfront and Fareham Leisure Centre leagues.

    <dependency>
        <groupId>com.github.forwardloop</groupId>
        <artifactId>glicko2s_2.12</artifactId>
        <version>0.9.4</version>
    </dependency>

    compile 'com.github.forwardloop:glicko2s_2.12:0.9.4'

    libraryDependencies += "com.github.forwardloop" %% "glicko2s" % "0.9.4"

Compute new rating for a player based on a sequence of match results with other players:

     import static forwardloop.glicko2s.Glicko2J.newPlayerRating;
     import forwardloop.glicko2s.Glicko2;
     import scala.Tuple2;
     import java.util.Arrays;
     import java.util.List;

     Glicko2 player = newPlayerRating();
     Glicko2 opponent1 = newPlayerRating();
     Glicko2 opponent2 = newPlayerRating();
    
     Tuple2<Glicko2, Result> match1 = new Tuple2(opponent1, Glicko2J.Win);
     Tuple2<Glicko2, Result> match2 = new Tuple2(opponent2, Glicko2J.Loss);
     Tuple2<Glicko2, Result> match3 = new Tuple2(opponent1, Glicko2J.Win);
    
     List<Tuple2<Glicko2, Result>> results = Arrays.asList(match1, match2, match3);
     Glicko2 newRating = Glicko2J.calculateNewRating(player, results);

The project is cross-compiled for Scala 2.11 and 2.12.

    import forwardloop.glicko2s.{Loss, Win, Glicko2}
    
    val player, opponent1, opponent2 = new Glicko2
    val results = Seq(
         (opponent1, Win), 
         (opponent2, Loss), 
         (opponent1, Win))
    val newRating = player.calculateNewRating(results)

The rating, rating deviation and volatility parameters will change as follows:

    //player.toGlicko1:    rating: 1500, deviation: 350.00, volatility: 0.060000
    //newRating.toGlicko1: rating: 1600, deviation: 227.74, volatility: 0.059998

The simple implementation of the EloResult trait provided allows three outcomes: win, draw or loss with weights 1.0, 0.5, 0.0, respectively. This can be fine tuned to differentiate between outcomes like 3:0 and 3:2, to better reflect true players' level in ELO computations. An example implementation for racquet sports can be found here