Speaking strictly for myself, I've been forced to gravitate away from what has historically been my bread and butter: UDMs designed to capitalize on speed-pace-form horses (that used to win) in the 6-1 to 10-1 range.
Not because those horses don't win at similar percentages.
And not because those horses don't show up in the pps with the occasional ridiculously high morning line.
But because the money in today's pools is SHARP (compared to even two years ago) and not enough of those horses win at high enough odds.
Here is a link to a text file that contains a cut and paste from my Data Window of a UDM that I created late last year: http://www.JCapper.com/MessageBoard/Reports/__ItFigures-a.txt
The UDM is designed to flag horses that really should be in the 7-5 to 9-5 odds range that end up going off in the 2-1 to 3-1 odds range.
Here, I am using three primary factor constraints: RANKF14=1 AND RANKF35 <= 2 AND VALF35 >= 0.1125
AND RANKF33 <= 3
Fyi, the primary factor RANKF14=1 is a Prob Expression I am using to score the most recent n number of starters for FigConsensus at the track-intsurface-dist.
And while rank=1 for this factor only has an roi of 0.8519 on my ytd Key Factors Report... which is hardly magical:
I have found that Prob Expressions help my betting avoid too much "against the grain" from a track bias standpoint.
The second factor RANKF35 is a UserFactor based on an MLR model where the output is a prob estimate between 0 and 1 after running exported JCapper data for speed-pace-form through the mlogit module in r.
Here I am basically scoring each horse for many of the same things I used to include in my speed-pace-form UDMs. Imo, speed-pace-form is still valid. But with the state of the game being what it is today you aren't likely to find a lasting edge by basing your world on a handful of rules for speed-pace-form. Imo, you are far better off using speed-pace-form as an integral part of an overall model.
And while rank=1 for this factor only has an roi of 0.8517 on my ytd Key Factors Report... which again is hardly magical:
The factor scores out a few points better than that at the tracks I am playing because the coefficients for the factors in the model are track-intsurface-dist specific because I am using SubGroups per The MLR Help Doc.
The third factor RANKF33 is a UserFactor based on an MLR model where the output is a prob estimate between 0 and 1 after running exported JCapper data for rider-trainer-railposition at the track-intsurface-dist through the mlogit module in r.
I was actually surprised to see this factor ($1.00 roi of 0.8806 on my Key Factors Report) score as well as it did.
My own cognitive bias, which gets in the way sometimes, says no way should this factor score out as well as it does.
The top three areas? Track bias from a FigConsensus standpoint. A pricing model from a speed-pace-form standpoint.
And a pricing model from rider-trainer-gate draw standpoint.
The rest of the UDM is just filtering designed to eliminate sets of horses that have obvious negative attributes.
-jp
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