Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario

TL;DR

This paper introduces a smart chair platform that unobtrusively collects and analyzes data from eSports athletes during CS:GO gameplay, using machine learning to distinguish professional players from others based on their behavior.

Contribution

It presents a novel unobtrusive data collection method with a smart chair and applies machine learning to identify professional eSports athletes.

Findings

Professional players can be distinguished by their behavior on the smart chair.

Data from accelerometer and gyroscope effectively classify player skill levels.

The approach offers a new way to analyze eSports athlete behavior.

Abstract

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.