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We suppose that performance is a random variable whose expectation is relatedto training inputs, and we study four performance measures in a statisticalmodel that relates performance to training. Our aim is to carry out a robuststatistical analysis of the training-performance models that are used inproprietary software to plan training, and thereby put them on a firmerfooting. The performance measures we consider are calculated using power outputand heart rate data collected in the field by road cyclists. We find thatparameter estimates in the training-performance models that we study differacross riders and across performance measures within riders. We concludetherefore that models and their estimates must be specific, both to theindividual and to the quality (e.g. speed or endurance) that the individualseeks to train. While the parameter estimates we obtain may be useful forcomparing given training programmes, we show that the underlying modelsthemselves are not appropriate for the optimisation of a training schedule inadvance of competition.

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