EloRating (version 0.46.18)

correctly_predicted: correctly predicted outcomes

Description

correctly predicted outcomes

Usage

correctly_predicted(xdata, ...)
# S3 method for default
correctly_predicted(xdata, ...)
# S3 method for elo
correctly_predicted(xdata, exclude_draws = TRUE, daterange = NULL, ...)
# S3 method for fastelo
correctly_predicted(xdata, ...)
# S3 method for list
correctly_predicted(xdata, ...)
# S3 method for matrix
correctly_predicted(xdata, ...)

Value

a list with two items where the first item is the proportion of correctly predicted outcomes and the second item is the total number of interactions for which the winning probability is not 0.5 (in the case of elo or fastelo) or the total number of interactions (in case of matrix or list)

Arguments

xdata

result from elo.seq, fastelo, a square interaction matrix or a list with two items where the first is a character vector with ID names (which represents the rank order to be checked) and the second is an interaction matrix (which needs to be square and which has column and row names)

...

additional arguments depending on the class of object you supplied

exclude_draws

logical, should draws be excluded from the calculation, by default TRUE. If they are included, such interactions will be scored as incorrectly predicted.

daterange

character or Date of length two, which allows to restrict the time range to be considered for elo objects

Methods (by class)

• correctly_predicted(default): default method for logical vector

• correctly_predicted(elo): for usage with results of elo.seq

• correctly_predicted(fastelo): for usage with results of fastelo

• correctly_predicted(list): for usage with a list of order and interaction matrix

• correctly_predicted(matrix): for usage with an interaction matrix

Author

Christof Neumann

Details

If you provide results from elo.seq or fastelo, this function first extracts the number of interactions for which a winning expectation can be expressed, i.e. for all interactions for which the winning probability for either individual is different from 0.5. If the winning probability for both IDs is 0.5 then either outcome is equally likely and hence it cannot be verified whether the winning probability 'worked correctly'.

If you provide an interaction matrix, the order of columns in which it is supplied is taken as the order to be checked, i.e. this just calculates the proportion of interactions that are in upper triangle of the matrix.

If you provide a list with a rank order and an interaction matrix, the matrix will be 'reshuffled' according to the rank order and then all entries above the diagonal will be divided by the total number of interactions.

Note that there is one potential issue for the list-based method (rank order and interaction matrix supplied), which is that it can't accomodate tied ranks.

Examples

data(adv)
res <- elo.seq(winner = adv$winner, loser = adv$loser, Date = adv$Date)
correctly_predicted(res)
correctly_predicted(res, daterange = c("2010-01-10", "2010-01-20"))
# only one interaction considered because for the first no expection was
# expressed (same starting values for both contestants)
correctly_predicted(res, daterange = c("2010-01-01", "2010-01-02"))

data("devries98")
correctly_predicted(list(colnames(devries98), devries98))
# is the same as
correctly_predicted(devries98)
# reversed order
correctly_predicted(list(rev(colnames(devries98)), devries98))

mat <- matrix(ncol = 10, nrow = 10, 0)
colnames(mat) <- rownames(mat) <- letters[1:10]
mat[upper.tri(mat)] <- 101
mat[lower.tri(mat)] <- 100
# correct order
order1 <- colnames(mat)
correctly_predicted(list(order1, mat))
# not very good

# the worst possible order for that matrix:
order2 <- rev(order1)
correctly_predicted(list(order2, mat))
# not much worse than order 1...

mat <- matrix(ncol = 10, nrow = 10, 0)
colnames(mat) <- rownames(mat) <- letters[1:10]
mat[upper.tri(mat)] <- 1
mat[1, 2] <- 100
# correct ranking
order1 <- letters[1:10]
correctly_predicted(xdata = list(order1, mat))
# almost correct order
order2 <- c("b", "a", letters[3:10])
correctly_predicted(xdata = list(order2, mat))