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:
elo_3.0.2.9000.tar.gz
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elo_3.0.2.9000.tgz(r-4.4-x86_64)elo_3.0.2.9000.tgz(r-4.4-arm64)elo_3.0.2.9000.tgz(r-4.3-x86_64)elo_3.0.2.9000.tgz(r-4.3-arm64)
elo_3.0.2.9000.tar.gz(r-4.5-noble)elo_3.0.2.9000.tar.gz(r-4.4-noble)
elo_3.0.2.9000.tgz(r-4.4-emscripten)elo_3.0.2.9000.tgz(r-4.3-emscripten)
elo.pdf |elo.html✨
elo/json (API)
NEWS
| # 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
- tournament - 'tournament': Mock data for examples
- tournament.multiteam - 'tournament.multiteam': Mock data for examples
eloelo-ratinglogistic-regressionmarkov-chainmarkov-modelrankingsports-analytics
Last updated 1 years agofrom:1ce2bba9fa. Checks:OK: 4 NOTE: 5. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 09 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 09 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 09 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 09 2024 |
| R-4.4-mac-x86_64 | NOTE | Nov 09 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 09 2024 |
| R-4.3-win-x86_64 | OK | Nov 09 2024 |
| R-4.3-mac-x86_64 | OK | Nov 09 2024 |
| R-4.3-mac-aarch64 | OK | Nov 09 2024 |
Exports:adjustbrierelo.calcelo.colleyelo.glmelo.markovchainelo.model.frameelo.probelo.runelo.run.multiteamelo.updateelo.winpctfavoredfinal.elosgroupis.scorekmovmsemultiteamneutralplayersrank.teamsregressscore
Adjusting for Players in the Elo Framework
Rendered fromplayers.Rmdusingknitr::rmarkdownon Nov 09 2024.Last update: 2023-09-19
Started: 2023-09-19
Calculating Running Elo Updates
Rendered fromrunning_elos.Rmdusingknitr::rmarkdownon Nov 09 2024.Last update: 2020-10-21
Started: 2020-10-21
Comparison Methods
Rendered fromcomparison_methods.Rmdusingknitr::rmarkdownon Nov 09 2024.Last update: 2023-08-22
Started: 2020-10-21
Introduction to Elo Rankings and the 'elo' Package
Rendered fromintro.Rmdusingknitr::rmarkdownon Nov 09 2024.Last update: 2020-10-21
Started: 2020-10-21
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Calculate AUC on an 'elo.run' object | auc.elo auc.elo.colley auc.elo.glm auc.elo.markovchain auc.elo.run auc.elo.running auc.elo.winpct |
| The Elo Package | elo-package elo |
| Post-update Elo values | elo.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' functions | elo.model.frame |
| Create a "margin of victory" column | elo.mov mov |
| Calculate the mean square error | brier elo.mse mse mse.elo.colley mse.elo.glm mse.elo.markovchain mse.elo.run mse.elo.running mse.elo.winpct |
| Elo probability | elo.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 updates | elo.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 win | favored favored.default favored.elo favored.elo.colley favored.elo.glm favored.elo.markovchain favored.elo.run favored.elo.running favored.elo.winpct |
| Extract model values | fitted.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 therein | adjust formula.specials group k multiteam neutral players regress |
| Make Predictions on an 'elo' Object | predict.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 teams | rank.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' Object | summary.elo summary.elo.colley summary.elo.glm summary.elo.markovchain summary.elo.run summary.elo.winpct |
| 'tournament': Mock data for examples | tournament |
| 'tournament.multiteam': Mock data for examples | tournament.multiteam |
