Comments on: Birth Months of World Cup Players

By: Steve C

Steve C — Thu, 15 Jul 2010 14:51:11 +0000

What if you were to run a simple Pearson coefficient? In this case there is a -0.57 correlation the number of players born a certain month and the numerical value of the month. For hockey, the correlation is -0.86. That is, by this measure Levitt’s conclusion appears to hold.

Of course, I could be calculating things wrong or there may be a reason why looking at it this way isn’t appropriate.

By: dkane

dkane — Tue, 13 Jul 2010 21:27:28 +0000

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.

By: Ross

Ross — Mon, 12 Jul 2010 19:42:43 +0000

As Ryan says there are different cut off points in different countries, in the UK for instance it would be September. There was a study of English players done a few years ago that showed they were far more likely to be born in September than in July, although I can’t it find right now. Although this makes reference to the same trend:

“Among the 25 most capped England football players, 11 were born between September and November, while only one, Frank Lampard, was born between June and August.”

http://tinyurl.com/32m5bh6

Which bears out Levitt’s idea imho.

By: Ryan D

Ryan D — Mon, 12 Jul 2010 14:13:38 +0000

Perhaps I betray some ignorance about soccer leagues in respective countries, but in Canada the January cutoff is used. What cutoffs are used in other countries? If there are different cutoffs in different systems, you’ve made an ecological fallacy by grouping them all into the same category.

The binomial seems an appropriate way to tackle the problem. P value in test for trend of 16%? You mean P value of 0.16, presumably. Logistic regression could also tell you the significance of birth month controlling for cutoff date and fraction of the total population born each month, but presumably such data would be hard to come by.

By: dkane

dkane — Mon, 12 Jul 2010 13:33:06 +0000

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.

By: J. Goard

J. Goard — Mon, 12 Jul 2010 02:22:57 +0000

the much more likely explanation … birth date fraud in poor countries seeking an advantage in international U-17 and U-20 competitions

Am I missing something? Wouldn’t that work in the opposite direction, with kids born in the early months being made a few months younger?

By: TGGP

TGGP — Sun, 11 Jul 2010 05:21:12 +0000

A Canadian says Gladwell is wrong about hockey:
http://worthwhile.typepad.com/worthwhile_canadian_initi/2010/05/are-we-wasting-half-our-hockey-talent.html

By: David Kane

David Kane — Sat, 10 Jul 2010 23:59:38 +0000

JL: No.

DavidB: Yes. I am not bothering with the details of some months having different numbers of days, with leap years and so on. None of that arcana would make any difference to the larger point. (Figuring out precisely the correct way to handle leap years is an interesting statistical question . . .

By: DavidB

DavidB — Sat, 10 Jul 2010 16:57:07 +0000

The ‘expected value’ of 61 looks suspiciously like 736 just divided by 12!

By: JL

JL — Fri, 09 Jul 2010 18:39:34 +0000

Does this calculation take into account the fact that more babies are born in some months and not only because some months have several more days than others?

By: dkane

dkane — Fri, 09 Jul 2010 13:37:39 +0000

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)