Apply rating algorithm
rating_run(
method,
data,
formula,
r = numeric(0),
rd = numeric(0),
sigma = numeric(0),
init_r = numeric(0),
init_rd = numeric(0),
init_sigma = numeric(0),
lambda = numeric(0),
share = numeric(0),
weight = numeric(0),
kappa = numeric(0),
tau = numeric(0)
)one of c("glicko", "glicko2", "bbt", "dbl")
data.frame which contains columns specified in formula, and
optional columns defined by lambda, weight.
formula which specifies the model. RHS Allows only player
rating parameter and it should be specified in following manner:
rank | id ~ player(name).
rank player position in event.
id event identifier in which pairwise comparison is assessed.
player(name) name of the contestant. In this case player(name)
helps algorithm point name of the column where player names are stored.
Users can also specify formula in in different way:
rank | id ~ player(name|team). Which means that players are playing in teams,
and results are observed for teams not for players. For more see vignette.
named vector of initial players ratings estimates. If not specified
then r will be created automatically for parameters specified in formula
with initial value init_r.
rd named vector of initial rating deviation estimates. If not specified
then rd will be created automatically for parameters specified in formula
with initial value init_rd.
(only for glicko2) named vector of initial players ratings
estimates. If not specified then sigma will be created automatically for
parameters specified in formula with initial value init_sigma.
initial values for r if not provided.
Default (glicko = 1500, glicko2 = 1500, bbt = 25,
dbl = 0)
initial values for rd if not provided.
Default (glicko = 350, glicko2 = 350, bbt = 25/3, dbl = 1)
initial values for sigma if not provided.
Default = 0.5
name of the column in data containing lambda values or one
constant value (eg. lambda = colname or lambda = 0.5).
Lambda impact prior variance, and uncertainty of the matchup result. The
higher lambda, the higher prior variance and more uncertain result of the
matchup. Higher lambda flattens chances of winning.
name of the column in data containing player share in team
efforts. It's used to first calculate combined rating of the team and
then redistribute ratings update back to players level. Warning - it should
be used only if formula is specified with players nested within teams (player(player|team)).
name of the column in data containing weights values or
one constant (eg. weight = colname or weight = 0.5).
Weights increasing (weight > 1) or decreasing (weight < 1) update change.
Higher weight increasing impact of event result on rating estimate.
controls rd shrinkage not to be greater than rd*(1 - kappa).
kappa=1 means that rd will not be decreased.
The system constant. Which constrains the change in volatility over
time. Reasonable choices are between 0.3 and 1.2 (default = 0.5), though
the system should be tested to decide which value results in greatest
predictive accuracy. Smaller values of tau prevent the volatility measures
from changing by large amounts, which in turn prevent enormous changes in
ratings based on very improbable results. If the application of Glicko-2 is
expected to involve extremely improbable collections of game outcomes, then
tau should be set to a small value, even as small as, say, tau= 0.