Are participation rates sufficient to explain gender differences in chess performance?Author(s): Michael KnappSource: Proceedings: Biological Sciences, Vol. 277, No. 1692 (7 August 2010), pp. 2269-2270Published by: The Royal SocietyStable URL: http://www.jstor.org/stable/25706451 .

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PROCEEDINGS -OF-TH)

Proc- R- Soc- B (2010) 277, 2269-2270 THE ROYAL

*yv doi:10.1098/rspb.2009.2257

SOCIETY JAJJ Published online 24 March 2010

Comment

Are participation rates sufficient to explain gender differences in chess performance?

In a recent paper in the Proceedings, Bilalic et al. (2009) argued that different participation rates of men and women represent the key factor that has to be taken into account when the comparatively small number of

women at the top level of certain intellectually demanding activities needs to be explained. Their conclusion was based on the results of an analysis of ratings of German chess players. According to Bilalic et al. (2009), 96 per cent of the observed differences in performance between the top 100 pairs of male and female players could be attributed to differential participation rates. The first purpose of this comment is to argue that their conclusion was premature and caused by an inappropriate statistical approach. The second purpose is to propose a

more adequate method of analysis and to show that par ticipation rates only explain two-thirds of the observed differences.

Bilalic et al. (2009) assumed that the ratings of German chess players are realizations of normally

distributed random variables. Then, they calculated

approximately the expected rating of the kxh best male and the kxh best female player. Fig. 2 of their

paper contained the differences of these two values for

&=1,...,100. What these authors did not mention,

however, is that their model predicts a rating of 3031 for the best male German player and a rating above 2700 for the 16 best male German players. Currently, there are only 33 players in the world with a rating above 2700 and there is no German belonging to this elite group. The highest rating ever achieved by a human player is 2851, which is significantly lower than the expected rating of 3031 predicted for the best

German player according to the model of Bilalic et al.

(2009). Therefore, this model seems inadequate to describe the upper tail of the distribution of ratings of German chess players.

I will now describe an analytical approach that does not rely on the questionable assumption of a normal distribution for the rating. Assume there are nf female

players and nm male players, and let Rk denote the rank of the kth best female player in the ordered com bined list of male and female players. Under the

assumption that gender has no effect on rating performance, it follows that the distribution of Rk is a

negative hypergeometric distribution (Johnson & Kotz

Electronic supplementary material is available at http://dx.doi.org/10. 1098/rspb.2009.2257 or via http://rspb.royalsocietypublishing.org.

1969), i.e.:

( nf \ ( nm \

\k-lj' \s-k) n{-k+\ 1 [Kk

= S)

=- -7 / m + nm \ n{ + nm-s+\

V 5-1 ) / S-l\ / ?m + Wf - 5 \

= U-1/ V nt-k )

for k < s < nm + k. The expected value is ERk = k

(wm + Wf + l)/(wf + 1) and it is straightforward to calculate the 0.05 per cent quantile r^o.ooos an^ the

99.95 per cent quantile rk^ 9995 for this distribution. If there is no gender effect on rating performance, then with probability of at least 99.9 per cent, the rank Rk of the kth best female player would be between

r&,o.ooo5 and ^,0.9995- Figure 1 compares the observed

rank rk of the kxh best female player with its expected value and with the quantiles r^o.ooos and ^,0.9995- The

discrepancy is evident. With the exception of the best female German player (even for her, the observed rank rx

- 87 is considerably above her expected rank

of Ei?!=21 but at least is within the interval

l7i5o.ooo55 ^1,0.9995] =

[15157]), the observed ranks of

the best 100th female players are above their 99.9% confidence intervals. For example, for the 100th best female player, the observed rank is 5505, whereas her expected rank is only 2116 and, assuming no

gender effect, her rank is between rx 00,o.0005 = 1510

and n00,0.9995 ? 2849 with a probability of at least

99.9 per cent.

Perfect agreement between the observed rank and

the rank expected under the assumption that gender has no effect on rating performance for the kth best female player would occur if she possessed rating fk of the round(Ei?^)th best player in the combined list

(where round(ERk) denotes rounding ERk to the near est integer). Analogously, let ER% denote the expected rank of the kth best male player and mk the rating of the round(JER%k)th best player in the combined list.

Then, dk ?

mk- fk may be considered to represent

that part of the rating difference between the kth best male and the kth best female player, which can be attributed to differential participation rates of men and women. Figure 2 compares these differences dk with the differences between the actual ratings of the best 100 female and male players. Only between

Received 9 December 2009

Accepted 22 January 2010 2269 This journal is ? 2010 The Royal Society

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2270 M. Knapp Comment. Participation rates and chess performance

6000 -z 5750 \ 5500 -j cP 5250 \

* 5000 -j drf^ 4750 i d*3 4500 i ^^P*^ 4250 4 v***^ 4000 -j

.2 4750 4 ^P^^

| 3250 i ^ | 3000 -j c 2750 -I ^ . ^

*i 2500 -j Q ^

^

2 2250 -j ^ ^ ^

2000 H cP?D ̂ - ^

1750 -j 1500 i d? ^

^ ^^^^ 1250 J

D^ ^ ^

^^^^ ^ - ^

1000 -j ^

250 -j ~~~~

~l?i?i?i?i?|?i?i?i?i?|?i?i?i?i?|?i?i?i?i?|?i?i?i?i?|?i?i?i?i?|?i?i?i?i?|?i?i?i?i?|?i?i?i?i?|?i?i?i?i?j

0 10 20 30 40 50 60 70 80 90 100 rank in women's list

Figure 1. Observed and expected rank of the best 100 female chess players. Squares represent the observed rank of the &th best

female player in the combined list of male and female players. The unbroken line gives the expected rank ERk and the two dotted lines denote the quantiles r^o.ooos anc* ^,0.9995

500 :

450 :

I 300 '-. ?^ Mt^^m

H 250 : o ^^^^

% 200 \ ?

50 :

0 ' 111111111111111111111111111111111111111111111111111

0 10 20 30 40 50 60 70 80 90 100

pair number (k)

Figure 2. The differences between the actual ratings of the

best 100 female and male German chess players and

the differences dk attributable to different participation rates. Squares, real differences; diamonds, attributable

differences.

41 and 71.1 per cent (mean value: 66.9%) of the actual

rating differences are explained by different partici pation rates of men and women, which is substantially lower than the 96 per cent obtained by Bilalic et al.

(2009). The unexplained gap between the two curves varies between 99 and 170 rating points (mean value over 100 pairs: 124.5). If two players with a rating difference of 124.5 points compete in a match over 100 games, the expected result is 67 : 33 in favour of the higher rated player. Therefore, the conclusion of Bilalic et al. (2009) that 'there is little left for biological or cultural explanations to account for', appears to be

premature.

I am grateful to Karen Hirschmann for pointing my attention to the work of Bilalic et al. (2009).

Michael Knapp* Institute for Medical Biometry, Informatics and Epidemiology,

University of Bonn, Sigmund-Frud-Str. 25, 53105 Bonn, Germany

*knapp@uni-bonn.de

REFERENCES Bilalic, M., Smallbone, K., McLeod, P. & Gobet, F. 2009

Why are (the best) women so good at chess? Participation rates and gender differences in intellectual domains.

Proc. R. Soc. B 276, 1161-1165. (doi:10.1098/rspb.

2008.1576) Johnson, N. L. & Kotz, S. 1969 Discrete distributions.

New York, NY: Wiley.

Proc. R. Soc. B (2010)

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