Feature Extraction for StarCraft II League Prediction

Abstract

In a player-versus-player game such as StarCraft II, it is important to match players with others with similar skills. Studies modeling player skills were conducted, with 47.3% and 61.3% performance. In order to improve the performance, we collected 46,398 replays and compared features extracted from six sections of replays. Through the comparison of the six datasets we created, we propose a method for extracting features from a single replay. Two algorithms, k-Nearest Neighbors and Random Forest, which are most commonly used in related studies, are compared. Our research showed a outperforming accuracy of 75.3% compared to previous works. Although no direct comparison has been made with the current system, we conclude that our research can replace the placement games of five rounds.