Rating competitors

I am looking for hints on how to estimate ratings for competitors
in an ongoing pairwise competition using R... my particular area of
interest being the game of Go, but the idea of identifying ratings
(on a continuous scale) rather than relative rankings seems easily
generalized to other competitions so I thought someone might be
studying something related already.

I presume the rating of a competitor would be best modeled as a random
variate on the rating scale, and an encounter between two
competitors would be represented by a binary result.  Logistic regression
seems promising, but I am at a loss how to represent the model since
the pairings are arbitrary and not necessarily repeated often.

I have read about some approaches to estimating ratings for Go,
but they seem to involve optimization using assumed distributions
rather than model fitting which characterizes analysis in R.

Does any of this sound familiar? Suggestions for reading, anyone?

I am looking for hints on how to estimate ratings for competitors
in an ongoing pairwise competition using R... my particular area of
interest being the game of Go, but the idea of identifying ratings
(on a continuous scale) rather than relative rankings seems easily
generalized to other competitions so I thought someone might be
studying something related already.

I presume the rating of a competitor would be best modeled as a random
variate on the rating scale, and an encounter between two
competitors would be represented by a binary result.  Logistic regression
seems promising, but I am at a loss how to represent the model since
the pairings are arbitrary and not necessarily repeated often.

I have read about some approaches to estimating ratings for Go,
but they seem to involve optimization using assumed distributions
rather than model fitting which characterizes analysis in R.

Does any of this sound familiar? Suggestions for reading, anyone?

-- 
---------------------------------------------------------------------------
Jeff Newmiller                        The     .....       .....  Go Live...
DCN:<jdnewmil at dcn.davis.ca.us>        Basics: ##.#.       ##.#.  Live Go...
                                      Live:   OO#.. Dead: OO#..  Playing
Research Engineer (Solar/Batteries            O.O#.       #.O#.  with
/Software/Embedded Controllers)               .OO#.       .OO#.  rocks...1k

I am looking for hints on how to estimate ratings for competitors
in an ongoing pairwise competition using R... my particular area of
interest being the game of Go, but the idea of identifying ratings
(on a continuous scale) rather than relative rankings seems easily
generalized to other competitions so I thought someone might be
studying something related already.

I presume the rating of a competitor would be best modeled as a random
variate on the rating scale, and an encounter between two
competitors would be represented by a binary result.  Logistic  
regression
seems promising, but I am at a loss how to represent the model since
the pairings are arbitrary and not necessarily repeated often.

     Have you considered Bradley-Terry models?   RSiteSearch("bradley", 
"functions") just returned 31 hits for me.
     Hope this helps.      Spencer Graves

Jeff Newmiller wrote:

I am looking for hints on how to estimate ratings for competitors
in an ongoing pairwise competition using R... my particular area of
interest being the game of Go, but the idea of identifying ratings
(on a continuous scale) rather than relative rankings seems easily
generalized to other competitions so I thought someone might be
studying something related already.

I presume the rating of a competitor would be best modeled as a random
variate on the rating scale, and an encounter between two
competitors would be represented by a binary result.  Logistic regression
seems promising, but I am at a loss how to represent the model since
the pairings are arbitrary and not necessarily repeated often.

I have read about some approaches to estimating ratings for Go,
but they seem to involve optimization using assumed distributions
rather than model fitting which characterizes analysis in R.

Does any of this sound familiar? Suggestions for reading, anyone?