Following the Irish rugby team’s humiliating 60-0 defeat to New Zealand, an interesting question was posed on Twitter: what does a 60-0 result convert to in football/soccer?
Intrigued, I decided to gather some data from both the English premier league (this season, more data collected and future blog posts to come!) and the equivalent English league in rugby union (this season too). My solution to this question involved the following steps. Firstly, I assumed that the scoring process in both games follow parametric probability distributions. I then fitted these data to these distributions, and calculated both the distribution and quantile functions. This allowed me to see the probability of a team scoring 60 in rugby, and then convert that probability into football goals.
The scores in both games will take the form of some kind of count distribution. However, Rugby is a much higher scoring game, and it is unlikely that both of the score count processes are being generated from the same parametric distribution. To fit scores from both games to their respective distributions, I have chosen to use the gamlss package on CRAN. The advantage of the gamlss package is that it has the capability to fit a huge range of distributions.
The code below shows how I loaded these data and fit the scores for both football and rugby to a number of count related distributions. My final choice of distribution was based on a comparison of AIC values for each model. Based on these, football and rugby scores follow the Poisson-inverse Gaussian, and zero-adjusted and zero-inflated negative binomial distributions respectively. The pZANBI and qPIG functions calculate the location of a rugby score on the football score distribution.
To answer the question: a 60-0 score in rugby translates into a 7-0 score in football. Oh dear.
#### score analysis
rm(list=ls())
p1 <- read.csv("premgames.csv")
sc <- c(p1$hgoal,p1$agoal)
# sc is premier league goals
library(gamlss.dist)
library(gamlss)
# fit dists
m1a <- gamlss(sc ~ 1, family = PO)
m2a <- gamlss(sc ~ 1, family = NBI)
m3a <- gamlss(sc ~ 1, family = NBII)
m4a <- gamlss(sc ~ 1, family = PIG)
m5a <- gamlss(sc ~ 1, family = ZANBI)
m6a <- gamlss(sc ~ 1, family = ZIPIG)
m7a <- gamlss(sc ~ 1, family = SI)
# compare dists
AIC(m1a,m2a,m3a,m4a,m5a,m6a,m7a)
# m4a is the best
#load rugby data
p2 <- as.character(unlist(read.csv("rugscore.csv")))
nms <- names(table(p2))[2:47]
p3 <- p2[p2 %in% nms]
p4 <- as.numeric(as.character(p3))
#fit
m1b <- gamlss(p4 ~ 1, family = PO)
m2b <- gamlss(p4 ~ 1, family = NBI)
m3b <- gamlss(p4 ~ 1, family = NBII)
m4b <- gamlss(p4 ~ 1, family = PIG)
m5b <- gamlss(p4 ~ 1, family = ZANBI)
m6b <- gamlss(p4 ~ 1, family = ZIPIG)
m7b <- gamlss(p4 ~ 1, family = SI)
#compare
AIC(m1b,m2b,m3b,m4b,m5b,m6b,m7b)
#m5b is best
# p of 60 in rugby
s1 <- pZANBI(60, mu = exp(m5b$mu.coefficients), sigma = exp(m5b$sigma.coefficients),
nu = exp(m5b$nu.coefficients))
# convert p in rugby to soccer distribution
qPIG(s1, mu = exp(m4a$mu.coefficients), sigma = exp(m4a$sigma.coefficients))
# the same again for zero
s2 <- pZANBI(0, mu = exp(m5b$mu.coefficients), sigma = exp(m5b$sigma.coefficients),
nu = exp(m5b$nu.coefficients))
qPIG(s2, mu = exp(m4a$mu.coefficients), sigma = exp(m4a$sigma.coefficients))
#############################################################
########## output
> rm(list=ls())
> p1 <- read.csv("premgames.csv")
> sc <- c(p1$hgoal,p1$agoal)
> # sc is premier league goals
>
> library(gamlss.dist)
> library(gamlss)
>
> # fit dists
> m1a <- gamlss(sc ~ 1, family = PO)
> m2a <- gamlss(sc ~ 1, family = NBI)
> m3a <- gamlss(sc ~ 1, family = NBII)
> m4a <- gamlss(sc ~ 1, family = PIG)
> m5a <- gamlss(sc ~ 1, family = ZANBI)
> m6a <- gamlss(sc ~ 1, family = ZIPIG)
> m7a <- gamlss(sc ~ 1, family = SI)
>
> # compare dists
> AIC(m1a,m2a,m3a,m4a,m5a,m6a,m7a)
df AIC
m4a 2 2334.244
m2a 2 2334.412
m3a 2 2334.412
m6a 3 2336.244
m7a 3 2336.244
m5a 3 2336.328
m1a 1 2341.862
> # m4a is the best
>
> #load rugby data
> p2 <- as.character(unlist(read.csv("rugscore.csv")))
> nms <- names(table(p2))[2:47]
> p3 <- p2[p2 %in% nms]
> p4 <- as.numeric(as.character(p3))
>
> #fit
> m1b <- gamlss(p4 ~ 1, family = PO)
> m2b <- gamlss(p4 ~ 1, family = NBI)
> m3b <- gamlss(p4 ~ 1, family = NBII)
> m4b <- gamlss(p4 ~ 1, family = PIG)
> m5b <- gamlss(p4 ~ 1, family = ZANBI)
> m6b <- gamlss(p4 ~ 1, family = ZIPIG)
> m7b <- gamlss(p4 ~ 1, family = SI)
>
> #compare
> AIC(m1b,m2b,m3b,m4b,m5b,m6b,m7b)
df AIC
m5b 3 1721.183
m2b 2 1722.700
m3b 2 1722.700
m6b 3 1727.345
m4b 2 1732.172
m7b 3 1749.975
m1b 1 2265.146
> #m5b is best
>
> # p of 60 in rugby
> s1 <- pZANBI(60, mu = exp(m5b$mu.coefficients), sigma = exp(m5b$sigma.coefficients),
+ nu = exp(m5b$nu.coefficients))
> # convert p in rugby to soccer distribution
> qPIG(s1, mu = exp(m4a$mu.coefficients), sigma = exp(m4a$sigma.coefficients))
[1] 7
>
> # the same again for zero
> s2 <- pZANBI(0, mu = exp(m5b$mu.coefficients), sigma = exp(m5b$sigma.coefficients),
+ nu = exp(m5b$nu.coefficients))
> qPIG(s2, mu = exp(m4a$mu.coefficients), sigma = exp(m4a$sigma.coefficients))
[1] 0
DiffusePrioR 