The past Power profiling has always been an areathat has received great interest within cycling specific research (Leoet al., 2021; Pinot & Grappe, 2011; Quod et al., 2010; Sanders & van Erp, 2021). Although there arenumerous methodologies that fall under the umbrella of power profiling, there remainslimited consensus on testing protocols, modelling procedures and practical applications. Arguably the most usedmethodology is derivingmean maximum power outputsMMP values from training and racing. This may be, at least partially, due to the ease in which MMP values can be derived. However, this approach does have several limitations. MMP data can includesubmaximal data, unstandardized effortlengthsand changing environmental conditions (e.g.,sea level vs. altitude). Even though MMP data haveshown to be a good predictor of race typology, difficulty and performance (Leo, et al. 2021a; 2021b; Sanders and van Erp 2021), little attention is often paid to the accuracyand reliability of the power output measurement. Since the first crank spindle manufactured power meters from SRM in the 1980s many other companies came up with a similar product measuring power output via crank arms, spindles,and pedals. Despite the power meters`popularity, little information is known about the accurate calibration processto ensure power meters measurevalid and reliable. However,this is of paramount importance for all further power output data processing and analysis. Manual calibration of a power meter according to Wooles et al.(2005) is limited by most manufacturers due to their firmware policies and intellectual property rules. Instead, power meter manufacturers provide their internal "arbitrary" calibrationprocess, which reveal little information for customers.Inthe authors`opinion it would be advantageousthat power meters allow a manual adjustment of the slope and zero-offsetalongside the automatic calibration process, (e.g., via a mobile smartphone app), to ensure accurate and reliablepower output readings.
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