University of Twente Student Theses


Using in-game data to give insights in the performance of eSporters

Nijland, R.G. (2020) Using in-game data to give insights in the performance of eSporters.

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Abstract:eSports is becoming an increasingly important sector within sports and gaming. With tournament prizes over the millions and millions of fans watching it, it is more popular now than ever. However, where there is a lot of research about traditional sports, there is less to no research investigating the factors that influence the performance of eSporters. Due to this literature gap, eSporters are unable to make educated decisions about their performance management. As eSports’ performance is improving constantly for success and high stakes, performance management research is crucial. In this research, a machine learning methodology for obtaining data and understanding the game EA SPORTS™ FIFA 20, an upcoming game within the eSports, has been developed using controller input and a Convolutional Neural Network (CNN). This has been done to answer the main research question of this report: “Which FIFA in-game data has a relation to the in-game performance of eSporters?”. A combination of the Convolutional Neural Network and the controller input, together with the end screen data concluded to give a proper indication of the eSporter’s performance. These three levels of data obtained in this research give insights in the eSporters’ performance, as they have been visualised to guide the eSporters in evaluating their missteps within their gameplay in order to improve their performance. This project is the basis for new research opportunities in domains like Data Science, Data Visualisation, in-game strategy and tactics extraction, and how to deliver the feedback to the eSporters.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Creative Technology BSc (50447)
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