A flexible framework for calculating Elo ratings and resulting rankings of any two-team-per-matchup system (chess, sports leagues, 'Go', etc.). This implementation is capable of evaluating a variety of matchups, Elo rating updates, and win probabilities, all based on the basic Elo rating system.
elo package includes functions to address all kinds of Elo calculations.
Please see the vignette for examples. Note that v1.0.0 is very much not backwards-compatible.
Most functions begin with the prefix "elo.", for easy autocompletion.
Vectors or scalars of Elo scores are denoted
Vectors or scalars of wins by team A are denoted by
Vectors or scalars of win probabilities are denoted by
Vectors of team names are denoted
Widened the version dependency to R 3.3.0.
players() matrices in
elo.run() to find Elos of individual players playing at the same time.
elo.glm(), a simple function to run logistic regressions on Elo setups.
Fixed a bug in the
favored() function (used in
Exported and revamped the class structure of the specials allowed in formulas. (#30)
Allowed access to
elo.model.frame() even when the package isn't loaded. (#34)
Allowed regression to different values for each team. (#35)
Fixed a bug with initial Elos and deep copying in C++. (#25)
Added an argument to
regress() allowing users to stop regressing teams which have stopped playing. (#26)
This version is not backwards compatible!
Changed the signatures of
elo.update() to match formula interface.
elo.prob() to S3 generics, and implemented
formula methods. The default methods now include options to adjust Elos. (#3)
elo.run() no longer accepts numeric values for
elo.run() now accepts special functions
regress(). If the latter is used,
the class of the returned object becomes
"elo.run.regressed". (#11, #12, #19, #22)
$elos component of
"elo.run" objects has been completely reworked, and now uses 1-based indexing.
Because of this, the
print.elo.run() method also had to be fixed. (#16)
elo package now imports
elo.prob() now accepts vectors of team names (like
elo.run()) as input. (#6)
Documentation and the vignette have been updated.
elo.model.frame(). The output is a
data.frame with appropriately named columns.
predict.elo.run.regressed(). (#2, #19)
is.score() to test for "score-ness".
summary.elo.run(), along with helpers to calculate AUC and MSE (
Made the title more succinct.
Elaborated the description of the package.
Tweak the internal
Tweaked the README and vignette.
Submit first version of
elo to CRAN.
Issues and code can be found on GitHub: https://github.com/eheinzen/elo/