Road bike training: Analysing training data - how to interpret your training data and learn from mistakes

Tony Martin was anything but satisfied with his performances at the season highlights, the Tour de France and the individual time trial at the Olympic Games in Rio. He spoke of wanting to analyse this very carefully to find out why he was unable to match his usual performance. Was it due to the changed riding position in the time trial or the fact that he had lost three kilograms? Or had he also changed his training? For a professional, small details can mean the difference between victory and defeat.

For amateur athletes, who do not utilise their potential to the same extent as competitive athletes, fundamental questions arise at the end of the season: Was my training structured in a sensible way? And effective?

Training and big data

Scientists at the University of Pisa have investigated precisely this question. However, they were less interested in individual fates than in amateur athletes as a whole. They scrutinised data sets from 30,000 users of the internet platform Strava* to find out how amateur athletes plan their season and which strategy they are most likely to use for success. After a pre-selection process, 2,000 cyclists remained who trained regularly enough from winter 2012 to spring 2013 to be able to analyse their records.

In order to evaluate training effort and performance progress, the researchers analysed heart rate data and used it to calculate the "training stress score": this method assesses how long the athletes were active in the various training zones, or intensity ranges, which were derived from the users' documented maximum and minimum values. The training stress score is a measure of the overall sporting load.

You can find the entire article on these topics as a PDF in the download area below:

• Which strategy works?
• Measure load
• The ten most common training mistakes
• How to plan your season

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