Valuing Player Actions in Counter-Strike: Global Offensive

• URL: http://arxiv.org/abs/2011.01324v2
• Date: Wed, 4 Nov 2020 18:35:51 GMT
• Title: Valuing Player Actions in Counter-Strike: Global Offensive
• Authors: Peter Xenopoulos, Harish Doraiswamy, Claudio Silva
• Abstract summary: Using over 70 million in-game CSGO events, we demonstrate our framework's consistency and independence. We also provide use cases demonstrating high-impact play identification and uncertainty estimation.
• Score: 4.621805808537653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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