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Discussion Starter · #1 · (Edited)
This calculation is inspired by Malcolm Gladwell's Outliers, in which he uses the case of Canadian ice hockey to show the influence of birth month on players' achievement. In the league system classified by players' age, those who are born in January actually are almost one year older than those who are born in December, and therefore are stronger when they're young, encouraged at the beginning, and get more chances to play in matches. The result of these accumulated advantages is, statistically, more professional ice hockey players are born in the earlier months of a year.

(I'm not an English speaker, so forgive me if there's any language confusion)

I wonder whether we can observe similar phenomenon in professional tennis. Here's the result.

Result from ATP Rankings (15.09.2014)

Table 1a: players per birth month within all players, top 1000, top 500 and top 100

(attachment 1a)

Because each month has different number of days, I make another table to show players per day in each month
(ex. Jan= x/31, Jun= x/30. For Febuary, I divide it by 28.25.

Table 1b: players per day in each birth month

(attachment 1b)


In the third table, I divide the result in the second table by "the players per day in a year" to show the differences in each ranking range. For example, if the players per day in a month within top 1000 is x1, and then the y1 in the third table will be x1/(1000/365.25). And if the players per day in a month within top 500 is x2, and then the y2 will be x2/(500/365.25)

Table 1c: Y (I don't know how to call it)

(attachment 1c)

We can see that the "birth month effect" is less obvious in top-ranking players, but we you can clearly see the influence of birth month from top 500 downward. The number of top 1000 players born in December is only half the number in January.



How about the influence of birth month on junior players? I expect the higher influence of birth month on younger player and therefore I use the ITF Junior Ranking in this September. Here's the result.

Table 2a: junior players per birth month in different ranking ranges

(attachment 2a)

Table 2b: junior players per day in each birth month within different ranking ranges

(attachment 2b)

Table 2c: Y in junior ranking

(attachment 2c)

We can also see the trends in junior players and the effect is also weaker in higher-ranking players
And if we compare table 2c to table 1c, we can find that the birth month effect is stronger among junior players than professional players.
 

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