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

TL;DR

This paper introduces an efficient machine learning method that uses symbolic transfer entropy and consensus nested cross validation to identify key features and accurately classify eSports players' skill levels based on sensor data.

Contribution

It proposes a novel approach combining symbolic transfer entropy and consensus nested cross validation for feature selection and skill classification in eSports.

Findings

Achieved 90.1% classification accuracy.

Identified gaze and hand activity connectivity as key features.

Method applicable to sports training and player evaluation.

Abstract

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 newly developed 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 data and potentially revolutionize the training programs in both eSports and sports industries