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.

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The emerging progress of video gaming and eSports lacks the tools for ensuring high-quality analytics and training in professional and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the smart chair data from professional and amateur players. The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network. We have investigated an attention mechanism improves the generalization of the network and provides a straightforward feature importance as well. The best model achieves Area Under the Receiver Operating Characteristic Curve (ROC AUC) score 0.73 in predicting whether a player will perform better or worse in the next 240 seconds based on in-game metrics. The prediction of the performance of a particular player is realized although their data are not utilized in the training set. The proposed solution has a number of promising applications for professional eSports teams and amateur players, such as a learning tool or performance monitoring system.