Modelling the effect of training on performance in road cycling: estimation of the Banister model parameters using field data

Abstract

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.