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Originally Posted by Jeff P
You'd be surprised. Factors stay hidden from the public for a reason. Mostly because either the factor itself is under the radar or the public refuses to believe that factor to have any real relevance. We just had a couple of lengthy threads arguing the validity of speed in workouts. In that thread I showed how to create a simple model using workout speed as the basis for profitable play. Speed in workouts isn't stand alone profitable so it was something I was willing to post about in order to illustrate how hidden positives can be used to good effect. I know of a couple of other factors (which I'm not willing to post about here) that have been stand alone flat bet profitable since I first came across them about 15 years ago.
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Even if they don't realize it, anyone making a profit is by definition betting on hidden positives. That's where overlays come from. (Just as anybody who makes a profit is therefore by definition betting on overlays, even if they don't believe in overlays.)
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The really interesting thing to me is that most people (and please understand I'm not picking on you) make assumptions about what I do. I get a sense from phone calls and emails a lot of people think that I use stand alone factors in a vacuum... that as soon as I discover a promising new factor that meets the criteria I posted that I'll start betting that factor as a stand alone.
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Personally, I love using single factors (or combos of esoteric factors) in a vacuum, although I use oddsline-based approaches as well. You talked about workouts. I posted a long while back about how I had made a flat-bet profit model using just info from workouts with no other factors. No one believed me because workouts aren't "accurate" and even if they were you've got to consider other factors. You've just GOT to. (Those who insist that "inaccurate" = "useless for making money" really don't understand the game they are playing, IMO.) Certain trainer and pedigree plays have huge ROIs and to look at other factors just seems to dilute them because they make the most when they don't seem to have anything else going for them. I find it actually isn't that hard to come up with flat-bet profitable angles that are based on weird things as long as you can live with low win percentages (10-15%).
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I've invested a ton of my own man hours into the never ending quest to develop better and better odds lines. No matter how accurate an odds line I make in terms of predicting race winners... and as mentioned I am able to do that with a very high degree of accuracy.... one fact remains: Whenever I simulate flat win betting EVERY horse that goes to post at odds higher than that indicated as a theoretical break even point by the odds lines I create I lose significant money.
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I got to the point where on could make an oddsline better than the public (slightly) on any measure you'd care to use to determine that. But they didn't provide many betting opportunities, so I gave that up and now make them differently. They do provide a profit at the theorhetical break-even point and above, but only if I stick to the "contenders". I've never been able to make a full oddsline and have the low probability "overlays" make money. (Although when using spot plays I often bet outrageous long shots and win.) When betting from an oddsline, I restrict play to those where I assign a probability of above average for the field, which is always (1/field size). i.e. in a 5 horse race I need at least a horse assigned 20%, but in a 10 horse race 10% is ok.
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1. I suck at making odds lines.
2. The pools are pretty efficient by nature.
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I have found that making oddslines is one of the great mysteries of life. Jeff, you might want to try the following. If you've got a model, that model is made up of factors that you use as inputs, and then the model spits out a probability value, or something you convert to one. Now, some factors are presumably "more important" to the model than others -- it gives them more weight. And also presumably, you test your models on out-of-sample data and see how they do. Now, take one your "important" factors, and when running a test replace the true values for that factor with random values (in the correct range for that factor), or just scramble the values for that factor in each race (have horses swap values). Compare your randomized test with the true test. As long as you don't use the public odds as one of the random factors, and as long as your model isn't giving a hugely disproportionate weight to that factor (I'm presuming your model doesn't have any one factor that totally dominates), there is a great chance you'll find the randomized tests done in this way perform BETTER than using the actual factor values MORE than half the time. How important is your factor now? Welcome to data mining bias...
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In practice I find that when I create spot plays keyed off of combinations of hidden positive angles and run those against the odds line theoretical break even point in play or pass decision making... THAT approach consistently wins money.
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I would really like for you to elaborate on this -- "keyed off" of how? And the break-even point is determined how? Are you saying you are using your oddsline as-is, but only considering horses for betting that pass some additional factor filter?
