I was asked in the comments for the R code for the ranked probability score, so instead of posting it deep down in the comments I thought I’d post it as a proper blog instead. The ranked probability score (RPS) is a measure of how similar two probability distributions are and is used as a way to evaluate the quality of a probabilistic prediction. It is an example of a proper scoring rule.

The RPS was brought to my attention in the paper *Solving the problem of inadequate scoring rules for assessing probabilistic football forecasting models* by Constantinou and Fenton. In that paper they argue that the RPS is the best measure of the quality of football predictions when the predictions are of the type where you have probabilities for the outcome (home win, draw or away win). The thing about the RPS is that it also reflects that an away win is in a sense *closer* to a draw than a home win. That means that a higher probability predicted for a draw is considered better than a higher probability for home win if the actual result is an away win.

You can also find some more details at the pena.lt/y blog.

The following R function takes two arguments. The first argument (*predictions*) is a matrix with the predictions. It should be laid out so that each row is one prediction, laid out in the proper order, where each element is a probability and each row sum to 1. The second argument (*observed*) is a numeric vector that indicates which outcome that was actually observed.

For assessing football predictions the predictions matrix would have three columns, with the probabilities for the match ordered as home, draw and away (or in the opposite order).

rankProbScore <- function(predictions, observed){ ncat <- ncol(predictions) npred <- nrow(predictions) rps <- numeric(npred) for (rr in 1:npred){ obsvec <- rep(0, ncat) obsvec[observed[rr]] <- 1 cumulative <- 0 for (i in 1:ncat){ cumulative <- cumulative + (sum(predictions[rr,1:i]) - sum(obsvec[1:i]))^2 } rps[rr] <- (1/(ncat-1))*cumulative } return(rps) }