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Proper training and analytics in eSports require accurately collected andannotated data. Most eSports research focuses exclusively on in-game dataanalysis, and there is a lack of prior work involving eSports athletes'psychophysiological data. In this paper, we present a dataset collected fromprofessional and amateur teams in 22 matches in League of Legends video gamewith more than 40 hours of recordings. Recorded data include the players'physiological activity, e.g. movements, pulse, saccades, obtained from varioussensors, self-reported aftermatch survey, and in-game data. An importantfeature of the dataset is simultaneous data collection from five players, whichfacilitates the analysis of sensor data on a team level. Upon the collection ofdataset we carried out its validation. In particular, we demonstrate thatstress and concentration levels for professional players are less correlated,meaning more independent playstyle. Also, we show that the absence of teamcommunication does not affect the professional players as much as amateur ones.To investigate other possible use cases of the dataset, we have trainedclassical machine learning algorithms for skill prediction and playerre-identification using 3-minute sessions of sensor data. Best models achieved0.856 and 0.521 (0.10 for a chance level) accuracy scores on a validation setfor skill prediction and player re-id problems, respectively. The dataset isavailable at https://github.com/smerdov/eSports Sensors Dataset.

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset