An efficient machine learning approach for extracting eSports players’ distinguishing features and classifying their skill levels using symbolic transfer entropy and consensus nested cross validation

NOROOZI FAKHABI, Amin, HASAN, Mohammad, Ravan, Maryam, Norouzi, Elham and LAW, Ying-Ying (2024) An efficient machine learning approach for extracting eSports players’ distinguishing features and classifying their skill levels using symbolic transfer entropy and consensus nested cross validation. International Journal of Data Science and Analytics. ISSN 2364-4168 (In Press)

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Abstract or description

Discovering features that set elite players apart is of great significance for eSports coaches as it enables them to arrange a more effective training program focused on improving those features. Moreover, finding such features results in a better evaluation of eSports players’ skills, which, besides coaches, is of interest for game developers to design games automatically adaptable to the players’ expertise. Sensor data combined with machine learning have already proved effective in classifying eSports players. However, the existing methods do not provide sufficient information about features that distinguish high-skilled players. In this paper, we propose an efficient method to find these features and then use them to classify players' skill levels. We first apply a time window to extract the players' sensor data, including heart rate, hand activities, etc., before and after game events in the League of Legends game. We use the extracted segments and symbolic transfer entropy to calculate connectivity features between sensors. The most relevant features are then selected using the consensus nested cross validation method. These features, representing the harmony between body parts, are finally used to find the optimum window size and classify players' skills. The classification results demonstrate a significant improvement by achieving 90.1% accuracy. Also, connectivity features between players’ gaze positions and keyboard, mouse, and hand activities were the most distinguishing features in classifying players' skills. The proposed method in this paper can be similarly applied to sportspeople’s data and potentially revolutionize the training programs in both eSports and sports industries.

Item Type:	Article
Uncontrolled Keywords:	Machine Learning, Skill classification, eSports players, Sensor data
Faculty:	School of Digital, Technologies and Arts > Computer Science, AI and Robotics
Depositing User:	Mohammad HASAN
Date Deposited:	18 Mar 2024 12:03
Last Modified:	19 Mar 2024 04:30
URI:	https://eprints.staffs.ac.uk/id/eprint/8133

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