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Forecasting Player Behavioral Data and Simulating In-Game Events

  • Anna Guitart
  • Pei Pei Chen
  • Paul Bertens
  • África Periáñez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)

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.

Keywords

Social games Time series Forecasting Sequential analysis Deep learning ARIMA models Gradient boosting 

Notes

Acknowledgments

We thank Sovannrith Lay for helping to gather the data and Javier Grande for his careful review of the manuscript.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anna Guitart
    • 1
  • Pei Pei Chen
    • 1
  • Paul Bertens
    • 1
  • África Periáñez
    • 1
  1. 1.Yokozuna Data unitSilicon StudioShibuya-kuJapan

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