# Towards Understanding of eSports Athletes' Potentialities: The Sensing System for Data Collection and Analysis

arXiv: https://arxiv.org/abs/1908.06403 · HTML: https://mlsanity.com/paper/1908.06403/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques](https://mlsanity.com/paper/2107.00948/) (2021)
- [SC2EGSet: StarCraft II Esport Replay and Game-state Dataset](https://mlsanity.com/paper/2207.03428/) (2022)
- [An efficient machine learning approach for extracting eSports players distinguishing features and classifying their skill levels using symbolic transfer entropy and consensus nested cross validation](https://mlsanity.com/paper/2407.11972/) (2024)

## Abstract (arXiv)

eSports is a developing multidisciplinary research area. At present, there is a lack of relevant data collected from real eSports athletes and lack of platforms which could be used for the data collection and further analysis. In this paper, we present a sensing system for enabling the data collection from professional athletes. Also, we report on the case study about collecting and analyzing the gaze data from Monolith professional eSports team specializing in Counter-Strike: Global Offensive (CS:GO) discipline. We perform a comparative study on assessing the gaze of amateur players and professional athletes. The results of our work are vital for ensuring eSports data collection and the following analysis in the scope of scouting or assessing the eSports players and athletes.