This paper proposes a method to help understanding the influence of a game design on player retention. Using Far Cry R 4 data, we illustrate how playtime measures can be used to identify time periods where players are more likely to stop playing. First, we show that a benchmark can easily be performed for every game available on Steam using publicly available data. Then, we introduce how survival analysis can help to model the influence of game variables on player retention. Game environment and player characteristics change over time and tracking systems already store those changes. But existing model which deals with time varying covariate cannot scale on huge datasets produced by video game monitoring. That is why we propose a model that can both deal with time varying covariates and is well suited for big datasets. As a given game variable can have a changing effect over time, we also include time-varying coefficients in our model. We used this survival analysis model to quantify the effect of Far Cry 4 weapons usage on player retention.

In this paper, we study the link between di culty and player's motivation in two games developed by Ubiso ®: Rayman®Legends and Tom Clancy's e Division®. We describe a method to estimate players' di culty over time and link it's time varying e ect with players retention. Results con rm ow and self-e cacy theory. Also, for the rst hours of playtime, results di er between the two games. We explain that discrepancy with regard to a ribution theory : in Rayman Legends, failure can be mainly a ributed to the player skills, while in Tom Clancy's e Division, avatar's strength plays a fundamental role and can always be relatively quickly improved.