# Esports Athletes and Players: a Comparative Study

arXiv: https://arxiv.org/abs/1812.03200 · HTML: https://mlsanity.com/paper/1812.03200/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2018.

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

- [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)
- [Explainable e-sports win prediction through Machine Learning classification in streaming](https://mlsanity.com/paper/2510.19671/) (2025)

## Abstract (arXiv)

We present a comparative study of the players' and professional players' (athletes') performance in Counter Strike: Global Offensive (CS:GO) discipline. Our study is based on ubiquitous sensing helping identify the biometric features significantly contributing to the classification of particular skills of the players. The research provides better understanding why the athletes demonstrate superior performance as compared to other players.