  Game companies avoid sharing their game data with external researchers. Only
a few research groups have been granted limited access to game data so far. The
reluctance of these companies to make data publicly available limits the wide
use and development of data mining techniques and artificial intelligence
research specific to the game industry. In this work, we developed and
implemented an international competition on game data mining using commercial
game log data from one of the major game companies in South Korea: NCSOFT. Our
approach enabled researchers to develop and apply state-of-the-art data mining
techniques to game log data by making the data open. For the competition, data
were collected from Blade & Soul, an action role-playing game, from NCSOFT. The
data comprised approximately 100 GB of game logs from 10,000 players. The main
aim of the competition was to predict whether a player would churn and when the
player would churn during two periods between which the business model was
changed to a free-to-play model from a monthly subscription. The results of the
competition revealed that highly ranked competitors used deep learning, tree
boosting, and linear regression.
