Description
Python implementation of WengLin Bayesian ranking, a better, licensefree alternative to TrueSkill
This is a port of the amazing openskill.js package.
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They vary from L1 to L5 with "L5" being the highest.
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README
Python implementation of WengLin Bayesian ranking, a better, licensefree alternative to TrueSkill
This is a port of the amazing openskill.js package.
Installation
pip install openskill
Usage
>>> from openskill import Rating, rate
>>> a1 = Rating()
>>> a1
Rating(mu=25, sigma=8.333333333333334)
>>> a2 = Rating(mu=32.444, sigma=5.123)
>>> a2
Rating(mu=32.444, sigma=5.123)
>>> b1 = Rating(43.381, 2.421)
>>> b1
Rating(mu=43.381, sigma=2.421)
>>> b2 = Rating(mu=25.188, sigma=6.211)
>>> b2
Rating(mu=25.188, sigma=6.211)
If a1
and a2
are on a team, and wins against a team of b1
and b2
, send this into rate:
>>> [[x1, x2], [y1, y2]] = rate([[a1, a2], [b1, b2]])
>>> x1, x2, y1, y2
(Rating(mu=28.669648436582808, sigma=8.071520788025197), Rating(mu=33.83086971107981, sigma=5.062772998705765), Rating(mu=43.071274808241974, sigma=2.4166900452721256), Rating(mu=23.149503312339064, sigma=6.1378606973362135))
You can also create Rating
objects by importing create_rating
:
>>> from openskill import create_rating
>>> x1 = [28.669648436582808, 8.071520788025197]
>>> x1 = create_rating(x1)
>>> x1
Rating(mu=28.669648436582808, sigma=8.071520788025197)
Ranks
When displaying a rating, or sorting a list of ratings, you can use ordinal
:
>>> from openskill import ordinal
>>> ordinal([43.07, 2.42])
35.81
By default, this returns mu  3 * sigma
, showing a rating for which there's a 99.7% likelihood the player's true rating is higher, so with early games, a player's ordinal rating will usually go up and could go up even if that player loses.
Artificial Ranks
If your teams are listed in one order but your ranking is in a different order, for convenience you can specify a ranks option, such as:
>>> a1 = b1 = c1 = d1 = Rating()
>>> result = [[a2], [b2], [c2], [d2]] = rate([[a1], [b1], [c1], [d1]], rank=[4, 1, 3, 2])
>>> result
[[Rating(mu=20.96265504062538, sigma=8.083731307186588)], [Rating(mu=27.795084971874736, sigma=8.263160757613477)], [Rating(mu=24.68943500312503, sigma=8.083731307186588)], [Rating(mu=26.552824984374855, sigma=8.179213704945203)]]
It's assumed that the lower ranks are better (wins), while higher ranks are worse (losses). You can provide a score instead, where lower is worse and higher is better. These can just be raw scores from the game, if you want.
Ties should have either equivalent rank or score.
>>> a1 = b1 = c1 = d1 = Rating()
>>> result = [[a2], [b2], [c2], [d2]] = rate([[a1], [b1], [c1], [d1]], score=[37, 19, 37, 42])
>>> result
[[Rating(mu=24.68943500312503, sigma=8.179213704945203)], [Rating(mu=22.826045021875203, sigma=8.179213704945203)], [Rating(mu=24.68943500312503, sigma=8.179213704945203)], [Rating(mu=27.795084971874736, sigma=8.263160757613477)]]
Predicting Winners
You can compare two or more teams to get the probabilities of each team winning.
>>> from openskill import predict_win
>>> a1 = Rating()
>>> a2 = Rating(mu=33.564, sigma=1.123)
>>> predictions = predict_win(teams=[[a1], [a2]])
>>> predictions
[0.45110901512761536, 0.5488909848723846]
>>> sum(predictions)
1.0
Predicting Draws
You can compare two or more teams to get the probabilities of the match drawing.
>>> from openskill import predict_draw
>>> a1 = Rating()
>>> a2 = Rating(mu=33.564, sigma=1.123)
>>> prediction = predict_draw(teams=[[a1], [a2]])
>>> prediction
0.09025541153402594
Choosing Models
The default model is PlackettLuce
. You can import alternate models from openskill.models
like so:
>>> from openskill.models import BradleyTerryFull
>>> a1 = b1 = c1 = d1 = Rating()
>>> rate([[a1], [b1], [c1], [d1]], rank=[4, 1, 3, 2], model=BradleyTerryFull)
[[Rating(mu=17.09430584957905, sigma=7.5012190693964005)], [Rating(mu=32.90569415042095, sigma=7.5012190693964005)], [Rating(mu=22.36476861652635, sigma=7.5012190693964005)], [Rating(mu=27.63523138347365, sigma=7.5012190693964005)]]
Available Models
BradleyTerryFull
: Full Pairing for BradleyTerryBradleyTerryPart
: Partial Pairing for BradleyTerryPlackettLuce
: Generalized BradleyTerryThurstoneMostellerFull
: Full Pairing for ThurstoneMostellerThurstoneMostellerPart
: Partial Pairing for ThurstoneMosteller
Which Model Do I Want?
 BradleyTerry rating models follow a logistic distribution over a player's skill, similar to Glicko.
 ThurstoneMosteller rating models follow a gaussian distribution, similar to TrueSkill. Gaussian CDF/PDF functions differ in implementation from system to system (they're all just chebyshev approximations anyway). The accuracy of this model isn't usually as great either, but tuning this with an alternative gamma function can improve the accuracy if you really want to get into it.
 Full pairing should have more accurate ratings over partial pairing, however in high k games (like a 100+ person marathon race), BradleyTerry and ThurstoneMosteller models need to do a calculation of joint probability which involves is a k1 dimensional integration, which is computationally expensive. Use partial pairing in this case, where players only change based on their neighbors.
 PlackettLuce (default) is a generalized BradleyTerry model for k โฅ 3 teams. It scales best.
Advanced Usage
You can learn more about how to configure this library to suit your custom needs in the project documentation.
Implementations in other Languages
*Note that all licence references and agreements mentioned in the openskill.py README section above
are relevant to that project's source code only.