Player Modeling using Behavioral Signals in Competitive Online Games

TL;DR

This study develops behavioral player models from online game data to improve match-making accuracy, outperforming traditional rating systems by capturing diverse aspects of player behavior.

Contribution

It introduces a novel approach of modeling players using behavioral signals, demonstrating improved rank prediction over existing rating systems.

Findings

Behavioral models outperform mainstream rating systems.

Certain features predict ranks accurately across all player groups.

Different behavioral aspects are crucial for effective player modeling.

Abstract

Competitive online games use rating systems to match players with similar skills to ensure a satisfying experience for players. In this paper, we focus on the importance of addressing different aspects of playing behavior when modeling players for creating match-ups. To this end, we engineer several behavioral features from a dataset of over 75,000 battle royale matches and create player models based on the retrieved features. We then use the created models to predict ranks for different groups of players in the data. The predicted ranks are compared to those of three popular rating systems. Our results show the superiority of simple behavioral models over mainstream rating systems. Some behavioral features provided accurate predictions for all groups of players while others proved useful for certain groups of players. The results of this study highlight the necessity of considering different aspects of the player's behavior such as goals, strategy, and expertise when making assignments.