Multiplayer online video games in the past decade have become one of the world's most popular gaming formats. Analyzing player data can provide insights into player behavior, such as how long players continue to play or where they stop playing. This information can help developers to come up with solutions to retain their players. We have compared binary classification algorithms on different time frames of player history data along with level and guild from World of Warcraft – a previously popular massively multiplayer online role-playing game (MMORPG), to assess the probability of subscription renewal. Our findings suggest that random forest and decision tree algorithms give the highest accuracy in all cases (more than 90 %). This was achieved using level and guild as parameters along with a combination of active days and total playing time taken from different time frames in the past month along.