eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair
This addresses the need for in-depth performance analysis in professional eSports teams, though it is incremental as it builds on limited prior work on physical behavior analysis.
The study tackled the problem of analyzing eSports players' skill by examining their physical behavior during game events using a smart chair with sensors, achieving the ability to distinguish between low-skilled and high-skilled players through machine learning models.
Today's competition between the professional eSports teams is so strong that in-depth analysis of players' performance literally crucial for creating a powerful team. There are two main approaches to such an estimation: obtaining features and metrics directly from the in-game data or collecting detailed information about the player including data on his/her physical training. While the correlation between the player's skill and in-game data has already been covered in many papers, there are very few works related to analysis of eSports athlete's skill through his/her physical behavior. We propose the smart chair platform which is to collect data on the person's behavior on the chair using an integrated accelerometer, a gyroscope and a magnetometer. We extract the important game events to define the players' physical reactions to them. The obtained data are used for training machine learning models in order to distinguish between the low-skilled and high-skilled players. We extract and figure out the key features during the game and discuss the results.