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Forecasting Player Behavioral Data and Simulating in-Game Events
Machine Learning · Submitted 5 Oct 2017
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Understanding player behavior is fundamental in game data science. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. | background 0.99 | 0.04 | 0.03 | hedged possibility 0.56 |
| s2 | In particular, game developers need to evaluate beforehand the impact of in-game events. | background 0.98 | 0.04 | 0.02 | asserted 0.99 |
| s3 | Simulation optimization of these events is crucial to increase player engagement and maximize monetization. | background 0.99 | 0.05 | 0.04 | asserted 1.00 |
| s4 | 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. | contribution 1.00 | 0.91 | 0.09 | asserted 0.97 |
| s5 | Our ultimate purpose is to take a step towards the data-driven development of games. | future work 0.54 | 0.16 | 0.07 | asserted 0.76 |
| s6 | 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. | result 0.80 | 0.88 | 0.50 | hedged possibility 0.92 |
| s7 | 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. | interpretation 0.91 | 0.70 | 0.28 | hedged possibility 0.80 |
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The paper
Original 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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How the literature uses it
1 arXiv paper cite this in 1 sentence
Papers citing it
Cites it as background 1 paper
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In the field of game data science, DNNs have been applied to the prediction of both churn [ref] and purchases [ref] , and also to the simulation of in-game events [ref] .
Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models · Deep multilayer perceptron · 0.96
What it cites · 2 sentences without a confident label
Uses the method or tool of 1 paper
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DBN was performed with Python 2.7.12, using Theano [ref] .
Theano: A Python framework for fast computation of mathematical expressions · Software · 1.00
Cites as background 1 paper
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Based on them, several outcomes can be forecast [ref] .
An Introductory Study on Time Series Modeling and Forecasting · Introduction · 0.95
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