Quote:
Originally posted by Jeff P
I don't think the idea of increasing the data set by including 2nd or even 3rd place finishers as "winners" and considering only the horses below that position is a good idea. I say this purely from a logistical standpoint. Back away from statistics for a second and consider the way races are run in the first place.
The gate opens. One or more speed horses scramble for the lead. One speed horse gets the lead. The rest then take up positions behind the leader. They wait. Each makes a move at some point to challenge for the lead. Each challenge either succeeds or fails. That success or failure is only revealed to us when the first horse hits the wire. They load another field in the gate and the whole process is repeated.
Okay. Back to statistics. As soon as you remove the winner from the model and re-evaluate the race using only the horses below that- isn't your model now flawed because it is deviating from the way races are run? The winner that you just removed had some influence on the way the race was run. Probably a very strong one. Now remove the second place horse and re-evaluate using only the horses below that. Did the second place horse have an influence on the way the race was run? Again, very likey yes.
How valid can information obtained in this manner actually be?
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I more or less agree, but I think some real tests need to be done.
The technique might have some usefulness. For instance, if you have only a very small sample of races (say from a particular track) it might improve your predictions.
Also, it depends on the factors you are extracting from the data. General factors like speed will hold up, but certain pace factors will fall apart as you described. Or will they? Throwing the winner out may just bring an almost equally likely but different pace scenario to the fore.
I've learned that most ideas in horseracing can't be defeated with logic, only with experimentation. Most of my best discoveries came from taking something I thought would work well that wasn't working well and doing the exact opposite. (I love it when I describe to somebody something I'm doing that works great, and they explain to me why it can't work.)
