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The elo Package

The elo package includes functions to address all kinds of Elo calculations.

  • elo.prob(): calculate probabilities based on Elo scores

  • elo.update(): calculate Elo updates

  • elo.calc(): calculate post-update Elo values

  • elo.run() and elo.run2(): calculate "running" Elo values for a series of matches

It also includes comparable models for accuracy (auc, MSE) benchmarking:

  • elo.glm() which fits a logistic regression model

  • elo.markovchain() which fits a Markov chain model

  • elo.colley() for a method based on the Colley matrix.

  • elo.winpct() which fits a model based on win percentage

Please see the vignette for examples.

Naming Schema

Most functions begin with the prefix "elo.", for easy autocompletion.

  • Vectors or scalars of Elo scores are denoted "elo.A" or "elo.B".

  • Vectors or scalars of wins by team A are denoted by "wins.A".

  • Vectors or scalars of win probabilities are denoted by "p.A".

  • Vectors of team names are denoted "team.A" or "team.B".

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Version

Install

install.packages('elo')

Monthly Downloads

449

Version

2.1.1

License

GPL (>= 2)

Issues

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Maintainer

Ethan Heinzen

Last Published

January 14th, 2020

Functions in elo (2.1.1)

elo.markovchain

elo.markovchain
elo.mse

Calculate the mean square error
elo

The Elo Package
elo.colley

elo.colley
elo.model.frame

Interpret formulas in elo functions
elo.prob

Elo functions
elo.glm

elo.glm
elo.mov

Create a "margin of victory" column
favored.elo

Classify teams that are favored to win
fitted.elo

Extract model values
elo.update

Elo functions
elo.winpct

elo.winpct
predict.elo

Make Predictions on an elo Object
tournament

tournament: Mock data for examples
players

Details on elo formulas and the specials therein
summary.elo

Summarize an elo Object
elo.run

elo.run
elo.run.helpers

Helper functions for elo.run
score

Create a 1/0/0.5 win "indicator"
rank.teams

Rank teams
auc.elo

Calculate AUC on an elo.run object
elo.calc

Elo functions