Implements the Glicko rating system for estimating the relative skill level of players in two-player games such as chess. It extends the Elo method by including a deviation parameter for each player, representing uncertainty on the rating.
glicko(x, status = NULL, init = c(2200,300), gamma = 0, cval = 15,
history = FALSE, sort = TRUE, rdmax = 350, …)A data frame containing four variables: (1) a numeric vector denoting the time period in which the game took place (2) a numeric or character identifier for player one (3) a numeric or character identifier for player two and (4) the result of the game expressed as a number, typically equal to one for a player one win, zero for a player two win and one half for a draw.
A data frame with the current status of the
system. If not NULL, this needs to be a data frame
in the form of the ratings component of the returned
list, containing variables named Player, Rating,
Deviation, and optionally Games, Win,
Draw, Loss and Lag, which are set to zero
if not given.
The rating vector at which to initialize a new player
not appearing in status. Must be a vector of length two
giving the initial rating and initial deviation respectively.
If different initializations for different players are
required, this can be done using status. The initial
deviation cannot be greater than rdmax.
A player one advantage parameter; either a single
value or a numeric vector equal to the number of rows in
x. Positive values favour player one, while negative
values favour player two. This could represent the advantage
of playing at home, or the advantage of playing white for chess.
Note that this is not passed to predict.rating,
which has its own gamma parameter.
The c parameter, which controls the increase in the player deviations across time. Must be a single non-negative number.
If TRUE returns the entire history for each
period in the component history of the returned list.
If TRUE sort the results by rating (highest
to lowest). If FALSE sort the results by player.
The maximum value allowed for the rating deviation.
Not used.
A list object of class "rating" with the following
components
A data frame of the results at the end of the
final time period. The variables are self explanatory except
for Lag, which represents the number of time periods
since the player last played a game. This is equal to zero
for players who played in the latest time period, and is
also zero for players who have not yet played any games.
A three dimensional array, or NULL if
history is FALSE. The row dimension is the
players, the column dimension is the time periods.
The third dimension gives different parameters.
The player one advantage parameter.
The c parameter.
The character string "Glicko".
The Glicko rating system is a method for evaluating the skill
of players. It is more complex than Elo but typically yields
better predictions.
Default values are roughly optimized for the chess data analyzed
in the file doc/ChessRatings.pdf, using the binomial deviance
criterion. A player one advantage parameter has been added to
the original definition in the reference. A player one advantage
parameter is also used for prediction purposes in
predict.rating.
In this implementation, rating deviances increase at the
beginning of the updating period, and decrease at the end.
This is slightly different from the Glicko-2 implementation,
where deviance increases for active players may occur at the end
of the previous period. In both implementations there will be
an initial increase for existing but previously inactive players.
Glickman, M.E. (1999) Parameter estimation in large dynamic paired comparison experiments. J. R. Stat. Soc. Ser. C: Applied Statistics, 48(3), 377-394.
# NOT RUN {
afl <- aflodds[,c(2,3,4,7)]
robj <- glicko(afl)
robj
robj <- glicko(afl[afl$Week==1,])
for(i in 2:max(afl$Week)) robj <- glicko(afl[afl$Week==i,], robj$ratings)
robj
# }
Run the code above in your browser using DataLab