Hi

Thank you all for the interesting discussion on the way to get a class fig (rating) it has given me plenty of food for thought ,

Regards
Ingust

But say they run true to form every fifth race or so you will also get a winner at much better odds - people hate to back inconsistent horses.

Although the difference is not double from wet to dry tracks there is definitely a difference between Good and Heavy. From number crunching 12 years races I got a steady rise in the equivalent distance a horse gets beaten by in worse and worse going.

KV

KV I have gotten same from more recent number crunching, however that doesn't mean the effect of weight is any different on wet tracks, it just means that margins are more spread on wet tracks. Reason? Less horses in a field handle it, not because weight means more.

I think you think I'm thinking the same way as you but I don't think I am. Note in my quote the word equivalent as opposed to something less meaningful like average. The figures I obtained were in a fairly intense setup which didn't just take the lengths from the winner against track going in isolation. It was a sort of best fit affair using many other variables with successful handicapping being the measuring stick. I know this all sounds very wishy washy but it's pretty hard to explain without producing a white paper on the whole event.
Bottom line, I believe there is some difference in margin from the winner between the same two horses running on a dry and a wet track disregarding other factors like track preference and the like.

KV

Hi ,

Paul Segar has a bit to say about wet track wgts he has a sliding wgt scale , the wgts averaging from 4kgs on the low wgt to 5kg onthe top wgts with the wgts in between adjusted accordingly ,
ie:- 47kg on a dry track goes out to 51kg on a heavy track ,

regards

ingust

Base
 

Hi Dr. Ron

Which Base figure would you use?.

Here is what I do to some extent

Before selecting a Base figure the raw data needs to be massaged somewhat before we put it through its paces.
The first step is to eliminate Outliers from the data.(the following is a simple method or one could use Grubb's method of identifying outliers.).

Using excel and its inbuilt functions will assit

Step 1
using the function Quartile in excel
@sum((quartile 3) - (quartile 1)) * 1.5

Step 2
@sum((quartile 1 )+ answer from Step1

Any score/time that is outside of the the above needs to be deleted from the data
Now find the Mean and Stdev of the data this will become the Base figure.

With the help of the Base figure we need to develop a Performance envelope.
Each performance line of each runner needs to be assigned a Z score.Using these Z scores one needs to find the Max and Min Z scores for a range of typical Variables using the individual runners performance lines or if there is not enough data for a runner then you need to use the whole Database figures in preference to the individual runners Max/Min Z scores.

X*Y/sqrt(Z)
X=Min Z score
Y=LTD Stdev
Z= number of starts for this variable or LTD number of Starts

Subtract this score from your Base figure this becomes your Lower Performance figure, now do the same but substitute the Min Z score with the Max and now you have the upper Performance figure.

Its important to find Max/Min Z scores from your database for a whole range of variables and then apply them when the data is thin and most should be Class specific.

Thanks for the detailed response woof, while I'm no excel guru, i think I've got the gist of what you're saying and will test it out and see what sort of figures it comes up with.

Don Scott
 

Hi ingust,

Scotts ratings have been updated to current race classes
in his "Winning in the 90's". I just go a copy from a second hand book store. I suppose they are the latest update on a system that will never be updated again.

Michal

winning in the 90s
 

thank you for your interest Michal , i have a copy of that book , But is anyone still using these figures with success ?

Regards
Ingust