  The video game industry has seen 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, which allows 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 this paper, we take recent video games released on
Steam and aim to discover the relation between game popularity and a game's
features that can be acquired through Steam. We approach this task by
predicting the popularity of Steam games in the early stages after their
release and we use a Bayesian approach to understand the influence of a game's
price, size, supported languages, release date, and genres on its player count.
We implement several models and discover 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 game popularity.
