Predicting the Popularity of Games on Steam

TL;DR

This paper investigates how various features of Steam games, such as genre and release timing, influence their early popularity using Bayesian models, providing insights into factors that drive game success.

Contribution

It introduces a Bayesian hierarchical model to predict Steam game popularity based on features like genre, release date, and price, highlighting the impact of release timing and genre.

Findings

Genre-based models outperform other approaches.

Early month releases tend to be more popular.

Certain genres are strongly correlated with higher player counts.

Abstract

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.