elo

Ranking Teams by Elo Rating and Comparable Methods


Keywords
cran, elo, elo-rating, logistic-regression, markov-chain, markov-model, r, r-package, ranking, sports-analytics
Licenses
CNRI-Python-GPL-Compatible/CNRI-Python-GPL-Compatible

Documentation

The elo Package

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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.run.multiteam(): 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 vignettes 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".