At 10:04 AM 2/27/2004, Peter Flom wrote:

2) Lacking such ratings of style, you might try to derive some.
Assuming that these 600 players are top grandmasters, then you MIGHT be
able to do something with multidimensional scaling.  What this would
require is getting a bunch of people to rate how 'similar' different
players are.  The trouble is, with 600 people to rate, you'd need a lot
of raters, and they'd have to be knowldegable enough to give sensible
ratings of the players.   Perhaps this is doable; you might try
contacting a bunch of very knowldgable players and offering them money
to do the job.

The other inherent problem here is that IF in fact these 600 are all topnotch ... there will not be THAT much variation amongst them on a variety of factors ... winning records ... etc. This relative lack of differentiation amongst the players will make it hard for ANY method to uncover differential facets or dimensions of their behavior ...






HTH

Peter




Peter L. Flom, PhD Assistant Director, Statistics and Data Analysis Core Center for Drug Use and HIV Research National Development and Research Institutes 71 W. 23rd St www.peterflom.com New York, NY 10010 (212) 845-4485 (voice) (917) 438-0894 (fax)



>>> [EMAIL PROTECTED] 2/27/2004 4:26:52 AM >>>
Thanks for the responses guys.

The ELO system does indeed give reasonably accurate estimates of the
probability of winning. A simple logit model based on the difference
in ELO rating will give a reasonable estimate of the probabilities of
players winning.

I suppose what I am asking, in a sense, is could this prediction be
improved by somehow accounting for the head-to-head record of the two
participants in addition to the ELO rating difference.

A corollorary to that is obviously whether the head-to-head effect is
significant of itself.

I am not a statistician (I'm an engineer) but I am numerate and am
prepared to study the appropriate area. However I don't know what that
area is!

What would a suitable null hypothesis be?


The motivation for the question by the way comes from the idea that styles of play may suit certain opponents, e.g. my style of play might be relatively ineffective against many players but very effective against a small number of players, some of whom may be significantly better (in the sense they are ranked higher) than me.

I have a large database of matches (about 13,000 for about 600
players) where I have the ratings and the outcomes of the games. What
I am looking for is some statistical analysis I can do to begin
addressing the above question (although obviously looking at
head-to-head rather than the rather intangible style issue)
.
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Dennis Roberts
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http://www.personal.psu.edu/users/d/m/dmr/droberts.htm



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