Authors:
David Bednárek
;
Martin Krulis
;
Jakub Yaghob
and
Filip Zavoral
Affiliation:
Charles University, Czech Republic
Keyword(s):
e-sport, Data Analysis, Data Integration, Data Quality, Player Rating.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaboration and e-Services
;
Data Engineering
;
Data Integrity
;
Data Management and Quality
;
Data Management for Analytics
;
Databases and Data Security
;
e-Business
;
Enterprise Information Systems
;
Information and Systems Security
;
Information Integration
;
Information Quality
;
Integration/Interoperability
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Organizational Concepts and Best Practices
;
Symbolic Systems
Abstract:
Electronic sports or pro gaming have become very popular in this millenium and the increased value of this
new industry is attracting investors with various interests. One of these interest is game betting, which requires
player and team rating, game result predictions, and fraud detection techniques. In our work, we focus on
preprocessing data of Counter-Strike: Global Offensive game in order to employ subsequent data analysis
methods for quantifying player performance. The data preprocessing is difficult since the data format is
complex and undocumented, the data quality of available sources is low, and there is no direct way how to
match players from the recorded files with players listed on public boards such as HLTV website. We have
summarized our experience from the data preprocessing and provide a way how to establish a player matching
based on their metadata.