Nice thread so far.
postet by robert99-
Quote:
To me that seems the crux of the problem.
If you take last time out winners they have, in UK, a "positive" IV but a "negative" A/E. The public can all see that the horse won last time out and its price falls (possibly too low) but its apparent chance of winning, compared to the field, has apparently risen, based purely on long term averaging study of winners last time out.
Any odds line adjustments could cancel out or make matters worse if the factor used was not the actual cause of a better chance. If the factor is not an independent one, it might be double counted or worse if you allow for it. So a winner last time out in a poor race, one that has been rested too long after win, one that got injured in winning that race, one racing at a different distance or ground condition is not so good as other last time out winners. You are then back to studying the actual horse in detail and possibly the many interacting IVs, A/Es which now seem not so helpful as at first sight they could be.
So, that is really what I am asking here, is there anything new that has made them more useful in practice?
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In practice what I tend to do is this: Run a large sample for a given factor and take note of both the impact value and roi. After you've run enough samples for many different factors (I've done tens of thousands over the years) you'll gain a little knowledge about what to expect from impact values and roi. When you run a sample on a good factor it will stick out like a sore thumb.
Realistically it will have two attributes:
1. The factor is predictive. It will have a nice clean curve for impact values where horses that are strong in the factor being looked at relative to their fields will win more races than horses that are weak in that factor. If you run enough samples you'll notice that a LOT of factors meet this criteria. What they fail to meet is the next attribute which is:
2. The factor is ignored by the betting public. This is what I refer to as a hidden positive. The roi for horses that are strong in the factor being looked at will not only be much higher than horses that are weak in that factor - but -
the roi for horses that are strong in the factor being looked at will be noticeably higher than the roi for top horses in other factors that you have looked at.
This last part is key. When a hidden positive is present over a significant sample, it's a very strong indicator that you are looking at a factor that is
crying out to somehow be used in your overall mix.
I find that if you use hidden positives your assessments will be superior to assessments made by the vast majority of your competition. This should be a no brainer. But amazingly enough, it took me decades of trial and error to finally reach this conclusion.
If you fail to use hidden positives in your mix then you are just spinning your wheels and doing the same thing everybody else is doing. The result will be that you will face a very tough uphill climb in trying to gain a true edge over your competition.
That's what I have found to be true in practice.
-jp
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