  We suppose that performance is a random variable whose expectation is related
to training inputs, and we study four performance measures in a statistical
model that relates performance to training. Our aim is to carry out a robust
statistical analysis of the training-performance models that are used in
proprietary software to plan training, and thereby put them on a firmer
footing. The performance measures we consider are calculated using power output
and heart rate data collected in the field by road cyclists. We find that
parameter estimates in the training-performance models that we study differ
across riders and across performance measures within riders. We conclude
therefore that models and their estimates must be specific, both to the
individual and to the quality (e.g. speed or endurance) that the individual
seeks to train. While the parameter estimates we obtain may be useful for
comparing given training programmes, we show that the underlying models
themselves are not appropriate for the optimisation of a training schedule in
advance of competition.
