walkermac, firstly appreciate the reply. Along with beton's post its the kind of detail and differing view that I'm after.

At this point in time the size of the dataset I'm using wouldn't be sufficient to draw any significant conclusions with regards to how to weight specific factors based on distance, track, conditions etc - however thats not to say i'm discarding this train of thought as its something i'll utilize in the future, it's just not something I can implement now.

It sounds like Binomial Logistic Regression is the path I need to investigate. As you say it would work best with truly independent variables but I think it would be useful as a guide or even for comparative sake to my current approach. The issue with horse racing is you can get into debate's about what is truly independent - i.e. is number of wins a good independent stat to utilize or is that then dependent on number of career starts which then means is % Strike rate a better factor, so on and so forth.

I'll do some googling and mess around with the numbers tonight and see how I go. Having a more solid mathematical approach and background to the factor weightings rather than my current gut feel / POT & SR approach is certainly what I'm keen on.