I don't have a problem so much with having myriad statistics and picking the right ones, but not knowing which stats to generate in the first place from my data

I don't have a problem so much with having myriad statistics and picking the right ones, but not knowing which stats to generate in the first place from my database of results. For example, I assume that a dogs past performance must be some indicator of its chances in the next race, but how do I account for the chances of dogs who didn't complete their last race? What weighting is the last race worth, compared to the ones before (perhaps it had a bad race, but on the whole is running well). I just don't know how to optimise for those sort of things. I have a rough idea that some combination of genetic programming and GA could help - it would be an interesting challenge to builds software that knew how to apply a selection of mathematical functions to my data, and then breed the results like a GA. But it's tricky, I'd have thought. I've been treating the ML classifiers and learners as something of a black box, perhaps a more rigorous approach is required.