{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/esports-pro-players-behavior-during-the-game","title":"eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair","arxiv_id":"1908.06402","date":"2019-08-18","proceeding":null,"authors":["Anton Smerdov","Evgeny Burnaev","Andrey Somov"],"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.","url_abs":"https://arxiv.org/abs/1908.06402v1","url_pdf":"https://arxiv.org/pdf/1908.06402v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"esports-pro-players-behavior-during-the-game","repo_url":"https://github.com/smerdov/smart_chair_client","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"fps-games","task_name":"FPS Games"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"feature-importance","task_name":"Feature Importance"},{"task_slug":"sensor-modeling","task_name":"Sensor Modeling"},{"task_slug":"skills-assessment","task_name":"Skills Assessment"},{"task_slug":"skills-evaluation","task_name":"Skills Evaluation"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[{"method_slug":"l1-regularization","method_name":"L1 Regularization"},{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}