Esports Athletes and Players: a Comparative Study
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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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Dec 2018.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.
3 papers.
- 2022SC2EGSet: StarCraft II Esport Replay and Game-state Datasetcited by 3
- 2024An efficient machine learning approach for extracting eSports players distinguishing features and classifying their skill levels using symbolic transfer entropy and consensus nested cross validationcited by 1
- 2025Explainable e-sports win prediction through Machine Learning classification in streamingcited by 0
- 2025Explainable e-sports win prediction through Machine Learning classification in streaming22 Oct 2025
- 2024An efficient machine learning approach for extracting eSports players distinguishing features and classifying their skill levels using symbolic transfer entropy and consensus nested cross validation8 May 2024
- 2022SC2EGSet: StarCraft II Esport Replay and Game-state Dataset7 Jul 2022