John Smith wrote: > What about the following approach: > > I bin the data in terms of the ELO ratings difference, e.g -100 to > -90,-90 to -80 etc
I dislike binning numbers that are essentially on a continuous scale. I think methods designed to treat the ELO ratings as continuous will be more powerful statistically than methods based on binning. But for the sake of my understanding of your proposal, let's go with bins. > Within each bin I separate winners from losers to make two 'sub-bins' > I average the win/loss head-to head ratio in each of these sub-bins. Oooh, average of ratios. Another not-so-good idea. Better to compute a ratio of the total number of wins divided by the total number of games of everyone in the bin. > I now have a series of figures, two for each bin, one is the average > previous win% against that opponent where player 1 wins the match and > the other for where player 1 loses the match. > > Some kind of difference measaure then between the two 'series' would > indicate how much separate effect there is? Now you're coming close to stating an hypothesis, without actually stating one. Of course, figuring out what the distribution of this "binned-ratio-difference of series" statistic could be a difficult problem. But I am also lost as to what you are trying to do here, I can't follow the bins/wins/losses/draws/head-to-head combinations. Perhaps you could provide an example with just 3 players? -- Paige Miller Eastman Kodak Company paige dot miller at kodak dot com http://www.kodak.com "It's nothing until I call it!" -- Bill Klem, NL Umpire "When you get the choice to sit it out or dance, I hope you dance" -- Lee Ann Womack . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
