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Mining efficient training patterns of non-professional cyclists

The recent emergence of the so called online social fitness open up new scenarios for fascinating challenges in the field of data science. Through these platforms, users can collect, monitor and share with friends their sport performance, with interesting details about heartrate, watt consumption and calories burned. The availability of this data, collected among a large number of users, gives us the possibility to explore new data mining applications. In the current work, we present the results of a study conducted on a sample of 29, 284 cyclists downloaded via APIs from the social fitness platform Strava.com. We defined two basic metrics: a measure of training effort, that is how much a cyclist struggled during the workout; and a measure of training performance indicating the results achieved during the training. Although the average effort is weakly correlated with the average performance, by deeply investigating workouts time evolution and cyclists’ training characteristics interesting findings came out. We found that athletes that better improve their performance follow precise training patterns usually referred as overcompensation theory, with alternation of stress peaks and rest periods. Studies and experiments related to such theory, up to now, have always been conducted by sports doctors on a few dozen professionals athletes. To the best of our knowledge, our study is the first corroboration on large scale of this theory.