Using Match Reference Values of External Load to Monitor Performance and Fatigue in Football: Are We Looking the Right Way?

Monitoring external load is critical for informed decision-making processes across sports. In football, external load is routinely monitored during training sessions and competitive matches. Matches are typically given particular emphasis, as they are generally associated with the highest external load and the most pronounced fatigue responses within a microcycle. Consequently, coaches frequently use match-derived external load values as reference points for prescribing training loads throughout this period. However, despite the apparent specificity of this approach and the unique characteristics of match external load, recent research suggests that coaching staffs may be inadequately quantifying the actual external load produced by players during matches, as well as the associated fatigue responses. This limitation is largely attributable to the substantial match-to-match variability observed in external load outcomes, which may lead to misinterpretation of the true load produced by players. Therefore, a normalized approach to external load monitoring, whereby locomotor external loads are expressed to each player's highest observed load, has been recommended as a more player-specific method for quantifying and interpreting external load in football. This opinion article highlights how the use of match load as a reference for training prescription may lead to inaccurate estimations of the external load imposed on football players. Furthermore, it outlines the potential consequences of match-load-based prescription for microcycle load management, which may ultimately result in suboptimal or ineffective training stimuli in the long term. Finally, we propose a conceptual framework in which external locomotor load metrics are expressed to players' characteristic- and/or exposure-based reference values. This framework is intended to support a more player-specific interpretation of external load but requires empirical validation before being used as a prescriptive decision-making model.