"Engine matters": a first large scale data driven study on cyclists' performance

Cintia P., Pappalardo L., Pedreschi D.

Data Mining  62H30  H 2.8 Database Applications. Data mining 

The recent emergence of the so called online social fitness constitutes a good proxy to study the patterns underlying success in sport. Through these platforms, users can collect, monitor and share with friends their sport performance, diet, and even burned calories, giving an unprecedented opportunity to answer very fascinating questions: What are the main factors that shape sport performance? What are the characteristics that distinguish successful sportsmen? Can we characterize the role of social influence on fitness behavior? 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. Analyzing the relationship between these two metrics, an interesting result immediately emerges: at a global level, there is no correlation between effort and performance. This means that, in general, the performance is not simply a function of training: two athletes with the same level of training have different performance. However, by deeply investigating workouts time evolution and cyclists' training characteristics, 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, mainly confirming that "engine matters", but tuning is fundamental.

Source: Workshop on Data Mining Case Studies and Practice Prize, pp. 147–153, Dallas, US, 7 December 2013

Publisher: IEEE, New York, USA

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