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

ABSTRACT: Proper training and analytics in eSports require accurately collected and
annotated data. Most eSports research focuses exclusively on in-game data
analysis, and there is a lack of prior work involving eSports athletes'
psychophysiological data. In this paper, we present a dataset collected from
professional and amateur teams in 22 matches in League of Legends video game
with more than 40 hours of recordings. Recorded data include the players'
physiological activity, e.g. movements, pulse, saccades, obtained from various
sensors, self-reported aftermatch survey, and in-game data. An important
feature of the dataset is simultaneous data collection from five players, which
facilitates the analysis of sensor data on a team level. Upon the collection of
dataset we carried out its validation. In particular, we demonstrate that
stress and concentration levels for professional players are less correlated,
meaning more independent playstyle. Also, we show that the absence of team
communication does not affect the professional players as much as amateur ones.
To investigate other possible use cases of the dataset, we have trained
classical machine learning algorithms for skill prediction and player
re-identification using 3-minute sessions of sensor data. Best models achieved
0.856 and 0.521 (0.10 for a chance level) accuracy scores on a validation set
for skill prediction and player re-id problems, respectively. The dataset is
available at https://github.com/smerdov/eSports Sensors Dataset.
Proper training and analytics in eSports require accurately collected and
annotated data. Most eSports research focuses exclusively on in-game data
analysis, and there is a lack of prior work involving eSports athletes'
psychophysiological data. In this paper, we present a dataset collected from
professional and amateur teams in 22 matches in League of Legends video game
with more than 40 hours of recordings. Recorded data include the players'
physiological activity, e.g. movements, pulse, saccades, obtained from various
sensors, self-reported aftermatch survey, and in-game data. An important
feature of the dataset is simultaneous data collection from five players, which
facilitates the analysis of sensor data on a team level. Upon the collection of
dataset we carried out its validation. In particular, we demonstrate that
stress and concentration levels for professional players are less correlated,
meaning more independent playstyle. Also, we show that the absence of team
communication does not affect the professional players as much as amateur ones.
To investigate other possible use cases of the dataset, we have trained
classical machine learning algorithms for skill prediction and player
re-identification using 3-minute sessions of sensor data. Best models achieved
0.856 and 0.521 (0.10 for a chance level) accuracy scores on a validation set
for skill prediction and player re-id problems, respectively. The dataset is
available at https://github.com/smerdov/eSports Sensors Dataset.
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset
http://arxiv.org/abs/2011.00958v2