Here is the code for replicating the above analysis: x = read.csv("players.txt") x$dob = as.Date(x$Date.of.Birth, format = "%d/%m/%Y") x$birth.month = ordered(months(dob), levels = month.name) jpeg() plot(table(x$birth.month), main = "Birth Month of World Cup Players", xlab = "Month", ylab = "Number of Players", axes = FALSE) axis(1, at = 1:12, labels=month.abb) axis(2, at = seq(10, 80, 10), labels = seq(10, 80, 10)) dev.off() chisq.test(table(x$birth.month)) binom.test(x = 72, n = 736, p = 1/12)

A kid born in December would have his birthday moved to January (a month later) so that he was eligible for the U-17 tournament, or whatever. You make older players appear younger by moving their birthday later in time, especially from one side of the cut-off to the other.

The main point of this post is that Levitt is wrong about the birth month distribution among World Cup players. Once we accept that, we can explore the reasons why he is wrong. One possible reason is that different countries have different cut off dates. Perhaps. Even within a single country, multiple cut off dates may be used because different leagues have different rules. (This is certainly true in the US.) But, for me, the more parsimonious explanation is that birth month does not matter. Until anyone provides any evidence that it does, for adult professionals, not 13 year olds, then I will stick with Occam.