Forecasting Player Behavioral Data and Simulating in-Game Events

TL;DR

This paper explores forecasting methods for player behavior in video games, comparing traditional and deep learning techniques to predict in-game variables and optimize event simulations for increased engagement and revenue.

Contribution

It provides an experimental comparison of forecasting methods, highlighting deep learning's potential for versatile and effective prediction in game data science.

Findings

Deep learning shows promising results despite traditional methods performing better.

Deep learning models are versatile for different dynamic behaviors in game data.

Forecasting accuracy varies, but deep learning offers a general approach.

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.