Training Load Responses Modelling in Elite Sports: How to Deal with Generalisation?

Abstract This study aims to provide a transferable methodology in the context of sport performance modelling, with a special focus to the generalisation of models. Data were collected from seven elite Short track speed skaters over a three months training period. In order to account for training load accumulation over sessions, cumulative responses to training were modelled by impulse, serial and bi-exponential responses functions. The variable dose-response (DR) model was compared to elastic net (ENET), principal component regression (PCR) and random forest (RF) models, while using cross-validation within a time-series framework. ENET, PCR and RF models were fitted either individually (MI) or on the whole group of athletes (MG). Root mean square error criterion was used to assess performances of models. ENET and PCR models provided a significant greater generalisation ability than the DR model (p = 0.012, p < 0.001, p = 0.005 and p < 0.001 for ENETI, ENETG, PCRI and PCRG, respectively). Only ENETI, ENETG and RFI were significantly more accurate in prediction than DR (p = 0.020, p < 0.001 and p = 0.043, respectively). In conclusion, ENET achieved greater generalisation and predictive accuracy performances. Thus, building and evaluating models within a generalisation enhancing procedure is a prerequisite for any predictive modelling.