# eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair

## TL;DR

This paper introduces a smart chair platform to analyze eSports players' physical reactions during gameplay, using sensor data and machine learning to differentiate skill levels based on physical behavior.

## Contribution

The study presents a novel approach to assess eSports players' skill through physical behavior analysis using a smart chair with sensors, complementing traditional in-game data analysis.

## Key findings

- Key physical features correlate with player skill levels.
- Machine learning models can distinguish between low- and high-skilled players.
- Sensor data during game events reveal significant behavioral patterns.

## Abstract

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.