* I want to obtain maximum performance out of a process with many variables, many of which cannot be controlled.
* I cannot run thousands of experiments, so it'd be nice if I could run hundreds of experiments and
	+ vary many controllable parameters
	+ collect data on many parameters indicating performance
	+ 'correct,' as much as possible, for those parameters I couldn't control
	+ Tease out the 'best' values for those things I can control, and start all over again

It feels like this would be called data mining, where you're going through tons of data which doesn't immediately appear to relate, but does show correlation after some effort.

So... Where do I start looking at algorithms, concepts, theory of this sort of thing? Even related terms for purposes of search would be useful.

Background: I like to do ultra-marathon cycling, and keep logs of each ride. I'd like to keep more data, and after hundreds of rides be able to pull out information about how I perform.

However, everything varies - routes, environment (temp, pres., hum., sun load, wind, precip., etc), fuel, attitude, weight, water load, etc, etc, etc. I can control a few things, but running the same route 20 times to test out a new fuel regime would just be depressing, and take years to perform all the experiments that I'd like to do. I can, however, record all these things and more(telemetry on bicycle FTW).