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https://github.com/mrdimosthenis/glicko2
The Glicko-2 rating system for Scala and Scala.js
https://github.com/mrdimosthenis/glicko2
glicko glicko-2 glicko-calculator glicko-rating glicko-ratings glicko2 glicko2-algorithm rating rating-prediction rating-system ratings scala scalajs
Last synced: 26 days ago
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The Glicko-2 rating system for Scala and Scala.js
- Host: GitHub
- URL: https://github.com/mrdimosthenis/glicko2
- Owner: mrdimosthenis
- Created: 2022-02-13T18:52:17.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2022-10-25T16:43:43.000Z (about 2 years ago)
- Last Synced: 2023-06-30T20:38:43.480Z (over 1 year ago)
- Topics: glicko, glicko-2, glicko-calculator, glicko-rating, glicko-ratings, glicko2, glicko2-algorithm, rating, rating-prediction, rating-system, ratings, scala, scalajs
- Language: Scala
- Homepage:
- Size: 12.7 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# glicko2
An implementation of the **Glicko-2** rating system for **Scala** and **Scala.js**.
## Install the library
Add the dependency in the `build.sbt` file.
```scala
libraryDependencies += "com.github.mrdimosthenis" %% "glicko2" % "1.0.1"
```
For Scala.js use the `%%%` operator instead of `%%`.
## Import the package
```scala
import dimos.glicko2._
```
## Create players
`Player` is a case class with three parameters (`rating`, `deviation`, `volatility`).
* We can create a player by defining their parameters.
```scala
val player = Player(1464, 151, 0.06)
```
* We can also create a player with the default initial parameters.
```scala
val newPlayer = Player.newEntry()
// Player(1500.0, 350.0, 0.06)
```
## Create results
`Result` is an enum with three values (`WonAgainst`, `DefeatedBy`, `TiedWith`).
To create results, we need to specify their kind, their opponent and place them inside a `Seq`.
```scala
val res = List(Result.WonAgainst(newPlayer))
```
## Calculate player params after a period of results
We can see how results affect a player.
```scala
val playerUpdated = player.afterPeriod(res)
// Player(1507.5, 145.3, 0.06)
```
## Glickman's example
Suppose a player rated `1500` competes against players rated `1400`, `1550` and `1700`, winning
the first game and losing the next two. Assume the `1500`-rated player’s rating deviation
is `200`, and his opponents’ are `30`, `100` and `300`, respectively. Assume the `1500` player has
volatility _σ_ = `0.06`, and his opponents have `0.07`, `0.08` and `0.05`, respectively.
Let's see how this example is translated and what happens to the first player when the period ends.
```scala
val p = Player(1500, 200, 0.06)
val opp1 = Player(1400, 30, 0.07)
val opp2 = Player(1550, 100, 0.08)
val opp3 = Player(1700, 300, 0.05)
val res1 = Result.WonAgainst(opp1)
val res2 = Result.DefeatedBy(opp2)
val res3 = Result.DefeatedBy(opp3)
val results = List(res1, res2, res3)
val pUpdated = p.afterPeriod(results)
// Player(1464.06, 151.52, 0.05999)
```
## Tuning
If the default values of the system do not serve us well, we can change them.
```scala
val tuning = Tuning.default(initRating = 1200, minDeviation = 30, tau = 0.75)
// Tuning(1200.0, 350.0, 30.0, 0.06, 0.75, 0.000001)
```
In that case, we need to pass `tuning` as a parameter to `newEntry` and `afterPeriod` functions.
These are the parameters of `Tuning` and their default values:
* `initRating`: 1500
* `maxDeviation`: 350,
* `minDeviation`: 0,
* `initVolatility`: 0.06
* `tau`: 0.5
* `tolerance`: 0.000001