Year: 2020
Type: conference-paper
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.
Institution: New York University
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
Funder: National Science Foundation
Awards: CPS: Frontier: SONYC: A Cyber-Physical System for Monitoring, Analysis and Mitigation of Urban Noise Pollution, MRI: Development of Experiential Supercomputing: Developing a Transdisciplinary Research and Innovation Holodeck, MRI: Acquisition of an Infrastructure for Prototyping Next-Generation Algorithms for Large-Scale Visualization, Data Processing and Analysis, MRI: Development of Reconfigurable Environmental Intelligence Platform, AitF: FULL: Collaborative Research: Provably Efficient GPU Algorithms, CRI: II-New: An Infrastructure of Display Devices to Study Visual Analytics Beyond the Desktop
OpenAlex ID: W3096326666