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Originally Posted by raybo
Ok you stats gurus, if I have a rating derived from multiple factors, each weighted according to significance, how do I get from that final rating to a projected win probability/percentage?
Let's start with the following final ratings for a race:
#1 -- 72.65
#2 -- 101.06
#3 -- 104.32
#4 -- 87.72
#5 -- 89.90
#6 -- 10.20
#7 -- 75.89
#8 -- 79.96
#9 - 105.25
#10 - 77.95
#11 - 73.84
#12 - 69.82
How do I get from those ratings to a calculated/projected win probability/percentage? I have always thought that you just divide each rating by the sum of all the ratings, but can't seem to find anything related to this type of calculation on the web, and I haven't tried to create a line in a long time, so I'm a bit rusty.
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Hi Raybo,
I'm a little confused from what you've said in this thread whether this is a list of variable weights or a list of horses whose output is projected according to such weights. I'm guessing the latter.
If so, what the range of this data seem to resemble to me, since you've mentioned that your method is in the black, is the $net of a given field based on a few simple factors, which might explain the clustering. Also, since you've mentioned 'top 3' ranking as a part of your method, possibly you're penalizing horses who fall out of this grouping- would be consistent with this result. Since your description of your method implies that this is what you have sought to maximize, it would seem to make sense.
If it's not possible that this is what you've done, you already know this. But if it is, I would suggest just moving the decimal point two figures to the left and testing this against a database (you mentioned you're a client of J. Platt), and see how it stands up against a reasonably large sample. BTW, since the mean of even this small sample is 79, which would mean a return of .79 vs. all horses, which is quite close to what I believe is the mean return of all horses by the betting public, this may be quite accurate.
Cheers,
lansdale
