Package: elo 3.0.2.9000

elo: Ranking Teams by Elo Rating and Comparable Methods

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. It also includes methods to benchmark performance, including logistic regression and Markov chain models.

Authors:Ethan Heinzen [aut, cre]

elo_3.0.2.9000.tar.gz
elo_3.0.2.9000.zip(r-4.7)elo_3.0.2.9000.zip(r-4.6)elo_3.0.2.9000.zip(r-4.5)
elo_3.0.2.9000.tgz(r-4.6-x86_64)elo_3.0.2.9000.tgz(r-4.6-arm64)elo_3.0.2.9000.tgz(r-4.5-x86_64)elo_3.0.2.9000.tgz(r-4.5-arm64)
elo_3.0.2.9000.tar.gz(r-4.7-arm64)elo_3.0.2.9000.tar.gz(r-4.7-x86_64)elo_3.0.2.9000.tar.gz(r-4.6-arm64)elo_3.0.2.9000.tar.gz(r-4.6-x86_64)
elo_3.0.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
elo/json (API)

# Install 'elo' in R:
install.packages('elo', repos = c('https://eheinzen.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/eheinzen/elo/issues

Pkgdown/docs site:https://eheinzen.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

eloelo-ratinglogistic-regressionmarkov-chainmarkov-modelrankingsports-analyticscpp

7.09 score 38 stars 162 scripts 403 downloads 25 exports 2 dependencies

Last updated from:1ce2bba9fa. Checks:11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE143
linux-devel-x86_64NOTE126
source / vignettesOK240
linux-release-arm64NOTE169
linux-release-x86_64NOTE136
macos-release-arm64NOTE126
macos-release-x86_64NOTE270
macos-oldrel-arm64NOTE117
macos-oldrel-x86_64NOTE255
windows-develNOTE120
windows-releaseNOTE113
windows-oldrelNOTE119
wasm-releaseOK113

Exports:adjustbrierelo.calcelo.colleyelo.glmelo.markovchainelo.model.frameelo.probelo.runelo.run.multiteamelo.updateelo.winpctfavoredfinal.elosgroupis.scorekmovmsemultiteamneutralplayersrank.teamsregressscore

Dependencies:pROCRcpp

Adjusting for Players in the Elo Framework
Another Application
Rendered fromplayers.Rmdusingknitr::rmarkdown

Last update: 2023-09-19
Started: 2023-09-19

Comparison Methods
Comparison Models | Win/Loss Logistic Regression | Logistic Regression | Markov Chain | A note about LRMC | Colley Matrix Method | Modeling Margin of Victory Instead of Wins
Rendered fromcomparison_methods.Rmdusingknitr::rmarkdown

Last update: 2023-08-22
Started: 2020-10-21

Calculating Running Elo Updates
The elo.run() function | With two variable Elos | With a fixed-Elo opponent | Regress Elos back to the mean | Group matches | elo.run.multiteam() | Helper functions | Making Predictions | Advanced: custom probability and updates | Final Thoughts
Rendered fromrunning_elos.Rmdusingknitr::rmarkdown

Last update: 2020-10-21
Started: 2020-10-21

Introduction to Elo Rankings and the 'elo' Package
Introduction to Elo Rankings | The elo Package | Naming Schema | Basic Functions | Formula Interface | Final Thoughts
Rendered fromintro.Rmdusingknitr::rmarkdown

Last update: 2020-10-21
Started: 2020-10-21

Readme and manuals

Help Manual

Help pageTopics
Calculate AUC on an 'elo.run' objectauc.elo auc.elo.colley auc.elo.glm auc.elo.markovchain auc.elo.run auc.elo.running auc.elo.winpct
The Elo Packageelo-package elo
Post-update Elo valueselo.calc elo.calc.default elo.calc.formula
Compute a Colley matrix model for a matchup.elo.colley
Compute a (usually logistic) regression model for a series of matches.elo.glm
Compute a Markov chain model for a series of matches.elo.markovchain
Interpret formulas in 'elo' functionselo.model.frame
Create a "margin of victory" columnelo.mov mov
Calculate the mean square errorbrier elo.mse mse mse.elo.colley mse.elo.glm mse.elo.markovchain mse.elo.run mse.elo.running mse.elo.winpct
Elo probabilityelo.prob elo.prob.default elo.prob.elo.multiteam.matrix elo.prob.formula
Calculate running Elos for a series of matches.elo.run
Helper functions for 'elo.run'as.data.frame.elo.run as.matrix.elo.run as.matrix.elo.run.regressed elo.run.helpers final.elos final.elos.elo.run final.elos.elo.run.regressed
Calculate running Elos for a series of multi-team matches.elo.run.multiteam
Elo updateselo.update elo.update.default elo.update.formula
Compute a (usually logistic) regression based on win percentage for a series of matches.elo.winpct
Classify teams that are favored to winfavored favored.default favored.elo favored.elo.colley favored.elo.glm favored.elo.markovchain favored.elo.run favored.elo.running favored.elo.winpct
Extract model valuesfitted.elo fitted.elo.colley fitted.elo.glm fitted.elo.markovchain fitted.elo.run fitted.elo.running fitted.elo.winpct residuals.elo.run
Details on 'elo' formulas and the specials thereinadjust formula.specials group k multiteam neutral players regress
Make Predictions on an 'elo' Objectpredict.elo predict.elo.colley predict.elo.glm predict.elo.markovchain predict.elo.run predict.elo.run.multiteam predict.elo.run.regressed predict.elo.running predict.elo.winpct
Rank teamsrank.teams rank.teams.elo.colley rank.teams.elo.glm rank.teams.elo.markovchain rank.teams.elo.run rank.teams.elo.run.regressed rank.teams.elo.winpct
Create a 1/0/0.5 win "indicator"is.score score
Summarize an 'elo' Objectsummary.elo summary.elo.colley summary.elo.glm summary.elo.markovchain summary.elo.run summary.elo.winpct
'tournament': Mock data for examplestournament
'tournament.multiteam': Mock data for examplestournament.multiteam