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arXiv · 1710.01931

Forecasting Player Behavioral Data and Simulating in-Game Events

Anna Guitart·Pei Pei Chen·Paul Bertens·África Periáñez
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Abstract

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors.

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Anna Guitart, Pei Pei Chen, Paul Bertens, África Periáñez. 2017-10-05. Forecasting Player Behavioral Data and Simulating in-Game Events. https://doi.org/10.1007/978-3-030-03402-3_19

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