Valuing Player Actions in Counter-Strike: Global Offensive
Abstract
Esports, despite its expanding interest, lacks fundamental sports analytics resources such as accessible data or proven and reproducible analytical frameworks. Even Counter-Strike: Global Offensive (CSGO), the second most popular esport, suffers from these problems. Thus, quantitative evaluation of CSGO players, a task important to teams, media, bettors and fans, is difficult. To address this, we introduce (1) a data model for CSGO with an open-source implementation; (2) a graph distance measure for defining distances in CSGO; and (3) a context-aware framework to value players' actions based on changes in their team's chances of winning. Using over 70 million in-game CSGO events, we demonstrate our framework's consistency and independence compared to existing valuation frameworks. We also provide use cases demonstrating high-impact play identification and uncertainty estimation.
Keywords
Cite
@article{arxiv.2011.01324,
title = {Valuing Player Actions in Counter-Strike: Global Offensive},
author = {Peter Xenopoulos and Harish Doraiswamy and Claudio Silva},
journal= {arXiv preprint arXiv:2011.01324},
year = {2020}
}
Comments
to be published in 2020 IEEE International Conference on Big Data