  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
