Player Behavior Analysis for Predicting Player Identity Within Pairs in Esports Tournaments: A Case Study of Counter-Strike Using Binary Random Forest Classifier

Presenter Information

• Franziska Zimmer, University of TokyoFollow
• Mhd Irvan, University of TokyoFollow
• M. Nisansala Sevwandi Perera, University of TokyoFollow
• Roberta Tamponi, University of TokyoFollow
• Ryosuke Kobayashi, University of TokyoFollow
• Rie Shigetomi Yamaguchi, University of TokyoFollow

Location

Hilton Waikoloa Village, Hawaii

Event Website

https://hicss.hawaii.edu/

Start Date

7-1-2025 12:00 AM

End Date

10-1-2025 12:00 AM

Description

This research utilizes a binary random forest classifier to predict individual players based on their in-game behavior, analyzing and distinguishing within player pairs through a comprehensive set of in-game features. The analysis is based on a dataset from 119 "Counter-Strike: Global Offensive" (CS:GO) esports tournament matches. The classifier achieves a testing accuracy of up to 87%, highlighting its ability to effectively differentiate between players. A key contribution of this paper is the demonstration of the potential to predict player identities through in-game behavior data from CS:GO. This has implications for the gaming industry such as mitigating security issues. Additionally, the study pinpoints a detailed set of behavioral features that can uniquely identify players in a competitive esports setting.

Recommended Citation

Zimmer, Franziska; Irvan, Mhd; Perera, M. Nisansala Sevwandi; Tamponi, Roberta; Kobayashi, Ryosuke; and Shigetomi Yamaguchi, Rie, "Player Behavior Analysis for Predicting Player Identity Within Pairs in Esports Tournaments: A Case Study of Counter-Strike Using Binary Random Forest Classifier" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 4.
https://aisel.aisnet.org/hicss-58/in/esports/4