Predicting the Popularity of Games on Steam

The video-game industry has seen a rapid growth over the last decade. Thousands of video-games are released and played by millions of people every year, creating a large community of players. Steam is a leading gaming platform and social networking site allowing its users to purchase and store games. A by-product of Steam is a large database of information about games, players, and gaming behavior. In the paper, we investigate the relation between the game popularity and features that can be acquired through Steam. We predict the popularity of Steam games in the early stages after their release and we use a Bayesian approach to determine the impact of a game price, size, supported languages, release date, and genres on its player count. We implement several models and show that a genre-based hierarchical approach achieves the best performance. We further analyze the model and interpret its coefficients which indicate that games released at the beginning of the month and games of certain genres correlate with the game popularity.