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Game companies avoid sharing their game data with external researchers. Onlya few research groups have been granted limited access to game data so far. Thereluctance of these companies to make data publicly available limits the wideuse and development of data mining techniques and artificial intelligenceresearch specific to the game industry. In this work, we developed andimplemented an international competition on game data mining using commercialgame log data from one of the major game companies in South Korea: NCSOFT. Ourapproach enabled researchers to develop and apply state-of-the-art data miningtechniques to game log data by making the data open. For the competition, datawere collected from Blade & Soul, an action role-playing game, from NCSOFT. Thedata comprised approximately 100 GB of game logs from 10,000 players. The mainaim of the competition was to predict whether a player would churn and when theplayer would churn during two periods between which the business model waschanged to a free-to-play model from a monthly subscription. The results of thecompetition revealed that highly ranked competitors used deep learning, treeboosting, and linear regression.

Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data