Profiling and Identifying Smurfs or Boosters on Dota 2 Using K-Means and IQR

Abstract

Dota 2 is one popular multiplayer online battle arena game, and it holds the grandest e-sports tournament in the world—The International. However, smurfs and boosters are plaguing the game, causing a continuous decline in the player count. Smurfs are skilled players who stomp less experienced players, while boosters are paid to improve players’ rank. At this stage, the developers have brought updates on smurf detection based on players’ complaints, where smurf accounts are likely to be prevented from entering the game. This article proposes a smurf or booster detection among the players by profiling and identifying them based on statistical differences in features. Initially, we created a dataset with player data collected from the OpenDota API. Then, K-means was used to group and profile the players. Subsequently, the interquartile range method was applied to the high-performing players to identify the smurfs or boosters. We then invited three Dota 2 game experts to review the resulting profiles. A 95% accuracy score was achieved using majority voting. The methodology proposed in this article can be implemented in the Dota 2 to detect smurfs or boosters automatically. The findings in this article shall contribute to prolonging the game's life span.