Reference

Basic Elo Calculations
elo.prob()	Elo probability
elo.update()	Elo updates
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
Other helpful utilities
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