  eSports is the rapidly developing multidisciplinary domain. However, research
and experimentation in eSports are in the infancy. In this work, we propose a
smart chair platform - an unobtrusive approach to the collection of data on the
eSports athletes and data further processing with machine learning methods. The
use case scenario involves three groups of players: `cyber athletes' (Monolith
team), semi-professional players and newbies all playing CS:GO discipline. In
particular, we collect data from the accelerometer and gyroscope integrated in
the chair and apply machine learning algorithms for the data analysis. Our
results demonstrate that the professional athletes can be identified by their
behaviour on the chair while playing the game.
