A neural network is basically a smart look up table. Looks for correlations and patterns between results and input. We already do this with PMC and our brains. Way I learned to use it was to look at a season or two's data and learn from the patterns. e.g. My best performances were always around a TSS of 80 or so. Much higher and I was stale. Coggan did a good job of pointing out the effect of TSB. For a RR or TT slightly negative TSB was best, slightly positive TSB for a crit. Too negative or too positive and performance suffered. Also saw some good results with a double peak in TSS. Ramp up fitness to 80-85 TSS, ease off a bit to 70 and ramp up to 80 again and I was flying.

We're all different so everybody will have different values that give optimal results and these values might change with age and experience. I know the values that will usually put me in the best position but I've also had some great results at lousy numbers and horrible results at great numbers - so accuracy will be somewhere in the 60-80% range.

Machine learning might be able to discern some other patterns and hopefully make some predictions. Big question is are the model parameters it uses of any meaning or just random ones that give the best results.

Definitely worth exploring.