  Today's competition between the professional eSports teams is so strong that
in-depth analysis of players' performance literally crucial for creating a
powerful team. There are two main approaches to such an estimation: obtaining
features and metrics directly from the in-game data or collecting detailed
information about the player including data on his/her physical training. While
the correlation between the player's skill and in-game data has already been
covered in many papers, there are very few works related to analysis of eSports
athlete's skill through his/her physical behavior. We propose the smart chair
platform which is to collect data on the person's behavior on the chair using
an integrated accelerometer, a gyroscope and a magnetometer. We extract the
important game events to define the players' physical reactions to them. The
obtained data are used for training machine learning models in order to
distinguish between the low-skilled and high-skilled players. We extract and
figure out the key features during the game and discuss the results.
