Valuing Player Actions in Counter-Strike: Global Offensive

TL;DR

This paper introduces a new framework for valuing player actions in Counter-Strike: Global Offensive, leveraging a data model, graph distance measure, and context-aware analysis to improve sports analytics in esports.

Contribution

It presents an open-source data model, a novel graph distance measure, and a context-aware framework for evaluating player actions in CSGO.

Findings

Framework is consistent and independent of existing methods.

Effective in identifying high-impact plays.

Provides uncertainty estimation for player valuation.

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.