Hi Duritz,
When you gather Win Times from track to track,each tracks average will be in isolation, cause you will have better dogs racing at one place and not the other so when the dogs from the lesser track race at the better track they will have been over compensated in time adjustment and will perform worse than expected.
You need to record those dogs that have then won at both tracks and then you will find the difference in Class from track to track.

An example would be say The two Victorian city clubs the time differences in avg times would be around .22 which is very similiar to the track record from both tracks this is because the same dogs race there from meeting to meeting, my actual Class difference is .03 with The Meadows having the better types so the difference is around .188.

Regarding 1st Sectionals these definately are most important, and of all times these are the easiest to be compared and quantified from track to track, all thats needed is the distance from boxes to the first measuring point.

Because most of my processes are automated, when i have dragged out the Formlines for each dog in Todays race they are ranked then on 1st Sectional data, now based on that ranking say its in the top two i then find the avg finish time for that dog whenever it has raced in the first2, if there is insufficent data i create synthetic data based on other dogs performances from my database, then i can move to my next step in the process.

A second method if you don't have 1st sectional data, depending if you have true and meaningful track adjustments. Once you have standardised the past formlines you then isolate the formlines based on distances ie, under 400 >400<500, and so on. Then find the average for each time bin, this can/will provide a pretty good idea of who the 1st Sectional contenders will be in Todays race.