robert99 
08192007 04:14 PM 
Impact Factor versus true probability
What is the definitions of the impact factor calculation introduced by Quirin?
Are there any other alternative definitions that are better?
How do these IFs compare to "true" win probabilities of a horse racing factor or can they be adjusted to do so and how?
What i am getting at, say, for example of last time out winners (LTOW) is that in some races there will be several LTOWs but in some races none at all. That needs correcting for, to find a "true" probability of the LTOW factor.

098poi 
08192007 04:23 PM 

GameTheory 
08192007 04:49 PM 
Quote:
Originally Posted by robert99
What is the definitions of the impact factor calculation introduced by Quirin?
Are there any other alternative definitions that are better?
How do these IFs compare to "true" win probabilities of a horse racing factor or can they be adjusted to do so and how?
What i am getting at, say, for example of last time out winners (LTOW) is that in some races there will be several LTOWs but in some races none at all. That needs correcting for, to find a "true" probability of the LTOW factor.

Well, there is no such thing as a "true" probability, but putting that aside the only way to adjust for the different scenarios precisely is to consider each scenario separately, i.e. create an IV/probability distribution independently for each different scenario you're interested in. So separate races where there is exactly one LTOW, compute the frequency, then do two, etc. Of course you need huge initial samples because your some of your subsets are going to be so small as to be meaningless otherwise...

Overlay 
08192007 05:06 PM 
Aside from the oddsrelated aspect that Gordon Pine mentioned, Mike Nunamaker also adjusted the impact values in his studies for the field sizes of the races in his sample. However, I don't know how you could take a set of impact values calculated according to Quirin's definition, and recalculate them afterthefact to fit either Pine's or Nunamaker's method, without knowing odds data and field sizes for each of the individual horses or races in the sample (which are often not available, short of starting from scratch with your own calculations from original past performances). To me, Quirin's method suits the purpose well enough, as long as a sufficient sample size is used.
The true strength of the impact value for a particular factor or set of factors (and the effect on individual winning probability) can only be calculated or considered in the context of how all the other horses in the race stack up with regard to the same factor(s).

robert99 
08192007 07:05 PM 
098poi,
Thanks for the Gordon Pine link.
A/E is definitely a useful index for seeing if a factor is over or underbet but it does not tell you anything about win probability for that factor. Using the price of a horse brings in all sorts of other handicapping factors which together sets its price. So presuming it is the single factor being investigated that does all that is very misleading.
Game Theory,
As you might suspect I am trying to avoid the incremental approach. I would guess it is a compound IV that needs to be found, as it is a combination of factors that may make the race winner, rather than just one single factor. The closer you look the more confused it gets.
Overlay,
Thanks for info.
What you are saying is that a large sample used for an IV calculation converges onto a "truish" probability. That knowledge, +/ 5%, is what I am after. Does the price really effect the chance of winning, or does the chance of winning effect the price?
Has anyone got any relevant information on Fred Davis's views, who possibly introduced the IV method in his 1974 book "Percentages and Probabilities"

Overlay 
08192007 07:34 PM 
Quote:
Originally Posted by robert99
Does the price really effect the chance of winning, or does the chance of winning effect the price?

I, too, have difficulty with the concept (or with my understanding of it) of the odds themselves affecting a horse's winning chances, although Gordon Pine used it as the basis for his A/E gauge. To me, the odds are a dependent variable driven by the public's interpretation of the whole body of information that is available about a given field. And the public, although remarkably accurate in the aggregate, can be wrong on a regular basis about the odds that it assigns to individual horses in individual races. Of course, horses at lower odds win at a greater rate than horses at higher odds, but (again, in my opinion) it's their superior performance attributes that both produce that winning result, and that also account for the fact that they go off at low odds.

GameTheory 
08192007 07:37 PM 
Overlay, how do you handle factors that simply don't apply to all horses in a race? 2nd race off layoff, 1st off claim, trainer angles that only apply to one horse, etc...

Overlay 
08192007 07:47 PM 
The only factors I would use as standalone angles like that (that would not apply to all horses in a field) are those that would be sufficiently strong that they would statistically qualify as independent variables (such as the type Quirin discussed in Winning at the Races), that by themselves account for the winning percentages they produce, apart from any other variables or factors that might be present in the horse's record. Even then, though, I think there has to be consideration of the angle's winning percentage in light of the horse's odds, rather than just betting any particular angle blindly.

Quote:
Originally Posted by GameTheory
Overlay, how do you handle factors that simply don't apply to all horses in a race? 2nd race off layoff, 1st off claim, trainer angles that only apply to one horse, etc...

If your research indicates that horses that fit a particular condition run 3 points better than if they did not you can make that adjustment to thier expected speed rating.
Alternatively if you determine that a situation improves a horse's winning chances by 5% you can adjust the win probability and renormalize.
I do some of each.

GameTheory 
08192007 09:13 PM 
Quote:
Originally Posted by Overlay
The only factors I would use as standalone angles like that (that would not apply to all horses in a field) are those that would be sufficiently strong that they would statistically qualify as independent variables (such as the type Quirin discussed in Winning at the Races), that by themselves account for the winning percentages they produce, apart from any other variables or factors that might be present in the horse's record. Even then, though, I think there has to be consideration of the angle's winning percentage in light of the horse's odds, rather than just betting any particular angle blindly.

I didn't say anything about betting them blindly, or even betting them at all. They could be negative angles. I was just wondering how you might adjust your oddsline for inherently "nonrelative" factors where you can't compare horses within a race on that factor other than to say this horse has it and this horse doesn't...

Kelso 
08202007 01:24 AM 
Quote:
Originally Posted by 098poi

===========================
From the article:
What if you knew that horses that won their last start went off at odds lower than average? In fact, they do. That would mean that the horses in this group might be winning a lot because of their low odds. Their winning the last race might not have anything to do with it.
==============================
He didn't really mean to say this ... did he? (The remainder of the article doesn't reveal the answer.)

Overlay 
08202007 07:26 AM 
Quote:
Originally Posted by GameTheory
I didn't say anything about betting them blindly, or even betting them at all. They could be negative angles. I was just wondering how you might adjust your oddsline for inherently "nonrelative" factors where you can't compare horses within a race on that factor other than to say this horse has it and this horse doesn't...

(Sorry, I wasn't meaning to imply anything negative about anybody's particular betting style.) The factors (and there are only three of them) that I apply to individual horses (rather than to the full field) are strong positive ones (the type of independent variables that I previously described) that fall outside the scope of the factors that I'm using to rank the whole field on. When I've arrived at a composite value ratio for all the horses in the field using my "fullfield" factors, if any horses in the field then qualify for the individual factors I employ, I multiply only the composite value of those horses by the value associated with the individual positive factor(s) for which they qualify, and use the new composite value for those horses as the basis for calculating their fair odds.

robert99 
08202007 09:02 AM 
Quote:
Originally Posted by sjk
If your research indicates that horses that fit a particular condition run 3 points better than if they did not you can make that adjustment to thier expected speed rating.
Alternatively if you determine that a situation improves a horse's winning chances by 5% you can adjust the win probability and renormalize.
I do some of each.

sjk,
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?

I would think that winner last time out is far from independent of the speed figure data and would be a poor candidate for the type of adjustment mentioned in my post.
On the other hand a horse that was claimed by a 20% winning trainer away from a 5% winning trainer might well be a candidate for an adjustment and the fact that he was claimed is clearly independent of the PP information.
If a horse has the speed to make a clear lead on the other runners that is also information that is at least partly independent of his PPs viewed in a vacuum and might also inspire an adjustment.
I guess I am agreeing with you that a careful understanding the interrelations of the various factors (both from a conceptual and from an analytical perspective) is pretty important to getting it right.

Jeff P 
08202007 03:32 PM 
Nice thread so far. :ThmbUp:
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?

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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