Technological advances of the last decades have seen a vast increase in data availability in general but also in particular in the domain of sport science. Given the amount of data produced in cycling and other endurance sports, various models have been developed for (among other aspects) predicting performances based on historical data. While the complexity of data analysis tasks might not have changed (or even has decreased), the possibilities have drastically increased over the last years. This is again also due to the increasing amount of data collected. With the emergence of low-cost, energy-efficient GPS head units for cycling and the ability to store and share data online, the amount of data produced by athletes has increased drastically. Recent years have seen a big increase in available health-/sports-related data due to (among other factors) the introduction of 24/7 monitoring devices. Not only is managing and making use of these datasets challenging for non-technicians, but also the increase in data availability raised the complexity of some novel models (e.g., "deep learning") thus also elevating possibilities in analysis, which cannot be assumed to be understandable. While all this possibly equips sport scientists, researchers, coaches, and also athletes with many possibilities (e.g., monitoring, modelling, ...) the question to be addressed is: are current and future sport scientists prepared for this task?
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