June 13, 2014
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(This article was first published on DiffusePrioR » R, and kindly contributed to R-bloggers)

If history can tell us anything about the World Cup, it’s that the host nation has an advantage of all other teams. Evidence of this was presented last night as the referee in the Brazil-Croatia match unjustly ruled in Brazil’s favour on several occasions. But what it is the statistical evidence of a host advantage?

To look at this, I downloaded these data from the Guardian’s website. With these, I ran a very simple probit model that regressed the probability of winning the world cup on whether the country was the host and also if the county was not the host but located in the same continent (I merge North and South America for this exercise). Obviously, this is quite a basic analysis, so I hope to build on these data as the tournament progresses and maybe and the 2010 data, and look at more sophisticated models.

> probitmfx(formula=winners ~ continent + hosts, data=wc)
Call:
probitmfx(formula = winners ~ continent + hosts, data = wc)

Marginal Effects:
dF/dx Std. Err.      z   P>|z|
continent 0.064425  0.027018 2.3845 0.01710 *
hosts     0.315378  0.121175 2.6027 0.00925 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

dF/dx is for discrete change for the following variables:

[1] "continent" "hosts"


The results are as we would expect. I am using the excellent mfx package to interpret the probit coefficients. Being the host nation increases the probability of being victorious by nearly 32%. So, going by historical trends, Brazil have a huge advantage for this world cup. If we look at countries in the same continent (think Argentina for this world cup) we see that there is a small advantage here, just over 6%.

Whether these results are robust to additional control variables and in the inclusion of fixed effects alongside heterogeneous time-varying effects is something I hope to probe.