Player Skill Modeling in Starcraft II

Abstract

Starcraft II is a popular real-time strategy (RTS) game, in which players compete with each other online. Based on their performance, the players are ranked in one of seven leagues. In our research, we aim at constructing a player model that is capable of predicting the league in which a player competes, using observations of their in-game behavior. Based on cognitive research and our knowledge of the game, we extracted from 1297 game replays a number of features that describe skill. After a preliminary test, we selected the SMO classifier to construct a player model, which achieved a weighted accuracy of 47.3% (SD = 2.2). This constitutes a significant improvement over the weighted baseline of 25.5% (SD = 1.1). We tested from what moment in the game it is possible to predict a player’s skill, which we found is after about 2.5 minutes of gameplay, i.e., even before the players have confronted each other within the game. We conclude that our model can predict a player’s skill early in the game.