  The emerging progress of eSports lacks the tools for ensuring high-quality
analytics and training in Pro 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 game chair data from Pro
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 that attention mechanism improves the generalization of the
network and provides the straightforward feature importance as well. The best
model achieves ROC AUC score 0.73. The prediction of the performance of
particular player is realized although his data are not utilized in the
training set. The proposed solution has a number of promising applications for
Pro eSports teams and amateur players, such as a learning tool or a performance
monitoring system.
