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Originally Posted by lansdale
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
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The example ratings are final ratings, after the weights have been applied. Many of the factors involved come directly from the raw data, jockey win and ITM percentages, trainer win and ITM percentages, horse win and ITM percentages, horse age and weight, horse power rating, horse pace and speed figures, etc.. There are a few other ratings that don't come directly from the raw data. There are 4 categories/sets of weightings, which is pretty standard to most weighted factor methods. But, there are some user preferences for several factors, and paceline selection preferences for the paceline related factors.
This method is not currently part of my Black Box, this is initially going to be a separate method, testable in batch processing mode, against any number of past races, which should help determine which factors are more important and which ones are not, also what the weightings for the final factor sets should be.
I'm looking ahead with this odds line thing, because we all know that, regardless of the accuracy of one's method, profit comes from win probability versus average price, otherwise known as "value". Sure, I could just produce the weightings method and let the user have at it, but the method will have more value to the user if there is a logical value metric included. So, I'm jumping the gun a bit via this thread, mostly because I know this portion is going to take the most time, and I want to get started now, rather than wait until the rest of the method is complete.
To answer your question, the horses, below the top 3, are
not penalized at all in this method. All horses are going to receive equal treatment and live or die according to their data, and the factors and weightings each user decides to implement for each of the 4 race type categories of factor/preferences/weight settings.
