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
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.
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”.