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.
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Saint Petersburg State University of Aerospace Instrumentation（圣彼得堡国立航空航天仪器大学）
eSports is the rapidly developing multidisciplinary domain. However, research and experimentation in eSports are in the infancy. In this work, we propose a smart chair platform - an unobtrusive approach to the collection of data on the eSports athletes and data further processing with machine learning methods. The use case scenario involves three groups of players: `cyber athletes' (Monolith team), semi-professional players and newbies all playing CS:GO discipline. In particular, we collect data from the accelerometer and gyroscope integrated in the chair and apply machine learning algorithms …
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）
The emerging progress of eSports lacks the tools for ensuring high-quality analytics and training in Pro and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the game chair data from Pro and amateur players. The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network. We have investigated that attention mechanism improves the …
eSports is a developing multidisciplinary research area. At present, there is a lack of relevant data collected from real eSports athletes and lack of platforms which could be used for the data collection and further analysis. In this paper, we present a sensing system for enabling the data collection from professional athletes. Also, we report on the case study about collecting and analyzing the gaze data from Monolith professional eSports team specializing in Counter-Strike: Global Offensive (CS:GO) discipline. We perform a comparative study on assessing the gaze of amateur players and profe…
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
Staffordshire University（斯塔福德郡大学）；New York Institute of Technology（纽约理工学院）；Azad University（阿扎德大学）
eSports industry has greatly progressed within the last decade in terms of audience and fund rising, broadcasting, networking and hardware. Since the number and quality of professional team has evolved too, there is a reasonable need in improving skills and training process of professional eSports athletes. In this work, we demonstrate a system able to collect heterogeneous data (physiological, environmental, video, telemetry) and guarantying synchronization with 10 ms accuracy. In particular, we demonstrate how to synchronize various sensors and ensure post synchronization, i.e. logged video,…
We present a comparative study of the players' and professional players' (athletes') performance in Counter Strike: Global Offensive (CS:GO) discipline. Our study is based on ubiquitous sensing helping identify the biometric features significantly contributing to the classification of particular skills of the players. The research provides better understanding why the athletes demonstrate superior performance as compared to other players.
The role of AI in esports is shifting from leveraging games as a testbed for improving AI algorithms to addressing the needs of the esports players such as enhancing their gaming experience, esports skills, and providing coaching. For AI to be able to effectively address such needs in esports, AI agents require a theory of mind, that is, the ability to infer players' tactics and intents. To that end, in this position paper, we argue for human-in-the-loop approaches for the discovery and computational embedding of the theory of mind within behavioral models of esports players. We discuss that s…
While esports organizations are increasingly adopting practices of conventional sports teams, such as dedicated analysts and data-driven decision-making, video-based game review is still the primary mode of game analysis. In conventional sports, advances in data collection have introduced systems that allow for sketch-based querying of game situations. However, due to data limitations, as well as differences in the sport itself, esports has seen a dearth of such systems. In this paper, we leverage player tracking data for Counter-Strike: Global Offensive (CSGO) to develop ggViz, a visual analy…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；German Research Centre for Artificial Intelligence（德国人工智能研究中心）
Proper training and analytics in eSports require accurately collected and annotated data. Most eSports research focuses exclusively on in-game data analysis, and there is a lack of prior work involving eSports athletes' psychophysiological data. In this paper, we present a dataset collected from professional and amateur teams in 22 matches in League of Legends video game with more than 40 hours of recordings. Recorded data include the players' physiological activity, e.g. movements, pulse, saccades, obtained from various sensors, self-reported aftermatch survey, and in-game data. An important …
The world of competitive Esports and video gaming has seen and continues to experience steady growth in popularity and complexity. Correspondingly, more research on the topic is being published, ranging from social network analyses to the benchmarking of advanced artificial intelligence systems in playing against humans. In this paper, we present ongoing work on an intelligent agent recommendation engine that suggests actions to players in order to maximise success and enjoyment, both in the space of in-game choices, as well as decisions made around play session timing in the broader context. …
Using highly interactive systems like computer games requires a lot of visual activity and eye movements. Eye movements are best characterized by visual fixation - periods of time when the eyes stay relatively still over an object. We analyzed the distributions of fixation duration of professional athletes, amateur and newbie players. We show that the analysis of fixation durations can be used to deduce the skill level in computer game players. Highly skilled gaming performance is characterized by more variability in fixation durations and by bimodal fixation duration distributions suggesting …
In the emerging field of esports research, there is an increasing demand for quantitative results that can be used by players, coaches and analysts to make decisions and present meaningful commentary for spectators. We present FirstPersonScience, a software application intended to fill this need in the esports community by allowing scientists to design carefully controlled experiments and capture accurate results in the First Person Shooter esports genre. An experiment designer can control a variety of parameters including target motion, weapon configuration, 3D scene, frame rate, and latency.…
How does the team formation relates to team performance in professional video game playing? This study examined one aspect of group dynamics - team switching - and aims to answer how changing a team affects individual and collective performance in eSports tournaments. In this study we test the hypothesis that switching teams can be detrimental to individual and team performance both in short term and in a long run. We collected data from professional tournaments of a popular first-person shooter game {\itshape Counter-Strike: Global Offensive (CS:GO)} and perform two natural experiments. We fo…
This study aimed to evaluate the effects of possible physical design features of self-service kiosks (SSK), side and back partitions and chairs, on workload and task performance of older users during a typical SSK task. The study comparatively evaluated eight physical SSK design alternatives, and younger and older participants performed a menu ordering task using each physical design alternative. Older participants showed a large variation in task performance across the design alternatives indicating stronger impacts of the physical design features. In particular, sitting significantly reduced…
On Edge in the Dental Chair: Designing VR Support for Moments of Dental Anxiety
School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen（香港中文大学深圳人工智能学院）；Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong（香港大学牙医学院应用口腔科学与社区牙科护理部）；Beijing Institute of Collaborative Innovation（北京协同创新研究院）；School of Design, Hunan University（湖南大学设计学院）；Shenzhen Clinical College of Stomatology, School of Stomatology, Southern Medical University（南方医科大学口腔医学院深圳临床学院）；Department of Periodontology, Shenzhen Stomatology Hospital (Pingshan) of Southern Medical University（南方医科大学深圳口腔医院（坪山）牙周科）；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学深圳科学与工程学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学深圳数据科学学院）；Computational Media and Arts, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）计算媒体与艺术）
