A machine learning approach to predict the winner in StarCraft based on influence maps

We test different machine learning techniques on two different datasets of StarCraft games. The first dataset contains 4-player games in which the players are controlled by the internal game AI. The second dataset contains 2-player human games from specialized websites. We analyze the similarities and differences between both datasets and the performance of each algorithm. Finally, we perform a small experiment with expert players and conclude that our system reaches a level of precision similar to the human judges, although human judges base their predictions on a much more complex and abstract set of game features.