Basic Elo Calculations

elo.prob()

Elo probability

elo.update()

elo.calc()

Post-update Elo values

Running Elo Calculations

elo.run()

Calculate running Elos for a series of matches.

elo.run.multiteam()

Calculate running Elos for a series of multi-team matches.

Formula Specials

players() multiteam() k() adjust() regress() group() neutral()

Details on elo formulas and the specials therein

mov()

Create a "margin of victory" column

score() is.score()

Create a 1/0/0.5 win "indicator"

Comparison Methods

elo.glm()

Compute a (usually logistic) regression model for a series of matches.

elo.winpct()

Compute a (usually logistic) regression based on win percentage for a series of matches.

elo.markovchain()

Compute a Markov chain model for a series of matches.

elo.colley()

Compute a Colley matrix model for a matchup.

Model Assessment

auc(<elo.run>) auc(<elo.glm>) auc(<elo.running>) auc(<elo.markovchain>) auc(<elo.winpct>) auc(<elo.colley>)

Calculate AUC on an elo.run object

mse() brier()

Calculate the mean square error

favored()

Classify teams that are favored to win

rank.teams()

Rank teams

summary(<elo.run>) summary(<elo.glm>) summary(<elo.markovchain>) summary(<elo.colley>) summary(<elo.winpct>)

Summarize an elo Object

fitted(<elo.run>) residuals(<elo.run>) fitted(<elo.running>) fitted(<elo.glm>) fitted(<elo.markovchain>) fitted(<elo.winpct>) fitted(<elo.colley>)

Extract model values

predict(<elo.run>) predict(<elo.run.regressed>) predict(<elo.run.multiteam>) predict(<elo.glm>) predict(<elo.running>) predict(<elo.markovchain>) predict(<elo.colley>) predict(<elo.winpct>)

Make Predictions on an elo Object

as.matrix(<elo.run>) as.matrix(<elo.run.regressed>) as.data.frame(<elo.run>) final.elos()

Helper functions for elo.run

tournament

tournament: Mock data for examples

tournament.multiteam

tournament.multiteam: Mock data for examples

elo.model.frame()

Interpret formulas in elo functions

elo

The Elo Package