piratings (version 0.1.9)

optimize_pi_ratings: Optimize Pi Ratings

Description

This function performs grid optimization on a prespecified set of parameters to find the optimal learning rates for calculating the pi ratings for sport teams in competitive matches for a set of teams in their respective set of sport matches. The pi rating system was developed by Constantinou and Fenton Constantinou and Fenton (2013) <doi:10.1515/jqas-2012-0036>

Usage

optimize_pi_ratings(teams, outcomes, lambda_in, gamma_in, b_in, c_in)

Arguments

teams

an (n x 2) character matrix, contains unique names for the respective home and away teams in n subsequent matches

outcomes

an (n x 2) numeric matrix, contains the points that the respective home and away teams scored in n subsequent matches

lambda_in

a numerical vector, learning rate values to consider in the grid optimization, default value: seq(0, 0.1, 0.005)

gamma_in

a numerical vector, learning rate values to consider in the grid optimization, default value: seq(0, 1, 0.05)

b_in

a constant, logarithmic base, default value: 10

c_in

a constant, default value: 3

Value

a dataframe with the results of the grid optimization, the mean squared error for every combination of learning rates lambda and gamma specified in the parameter vectors

Examples

# NOT RUN {
# toy example
teams <- matrix(c("team A", "team B", "team B", "team A"), nrow = 2)
outcomes <- matrix(c(1, 3, 2, 1), nrow = 2)
optimize_pi_ratings(teams, outcomes, seq(0.05, 0.07, 0.005), seq(0.4, 0.6, 0.05))
# }