Engineering Esports Excellence: Data, AI, and High-Performance Human-Computer Interaction in Gaming

Competitive gaming is an interdisciplinary field that combines sports science, artificial intelligence, data engineering, and human–computer interaction to understand and enhance player performance. As esports becomes increasingly professionalised, performance is shaped by the interaction of cognitive skills, motor control, team coordination, technology use, and health-related factors. Current research is investigating how gameplay data, physiological signals, behavioural measures, and contextual information can be integrated to assess performance and guide training. However, important questions remain about the validity and comparability of performance metrics, the transferability of laboratory findings to professional environments, and the extent to which data-driven systems genuinely improve competition outcomes. Recent advances in machine learning, multimodal sensing, eye tracking, wearable technology, virtual and extended reality, and intelligent coaching systems have created new opportunities for real-time assessment and personalised feedback. At the same time, concerns regarding fatigue, stress, sleep, injury risk, privacy, explainability, accessibility, and responsible technology adoption remain insufficiently addressed. A more integrated and human-centred approach is therefore needed to connect technical innovation with player wellbeing, competitive performance, and sustainable esports practice.This Research Topic aims to advance the scientific and engineering foundations of high-performance esports by examining how data, artificial intelligence, and human–computer interaction can be used to measure, model, explain, and improve individual and team performance. It seeks to identify reliable indicators of expertise, develop valid and interpretable methods for performance analysis, and evaluate intelligent systems that support training, decision-making, coaching, and competition. Contributions may investigate how cognitive, physiological, behavioural, biomechanical, and gameplay variables interact across different contexts and levels of expertise. The Research Topic also aims to assess the effectiveness, ergonomics, fairness, and real-world applicability of emerging technologies. Particular emphasis will be placed on research that bridges laboratory experimentation, simulation, training environments, and professional esports practice.To gather further insights into the design, evaluation, and application of data-driven and human-centred technologies for high-performance esports, we welcome articles addressing, but not limited to, the following themes:1. Performance analytics, modelling, prediction, and benchmarking in esports2. Artificial intelligence and machine learning for training, competition, and strategic analysis3. Multimodal sensing, wearable technologies, eye tracking, and physiological monitoring4. Cognitive performance, attention, decision-making, perception, and motor control5. Fatigue, stress, sleep, recovery, workload, and readiness management6. Injury prevention, ergonomics, posture, equipment design, and musculoskeletal health7. Human–computer interaction systems for performance measurement and training, including interface and control design, sensory feedback, and immersive training environments8. Virtual, augmented, and extended reality for esports training and performance assessment9. Team communication, collaboration, coordination, leadership, and organisational performance10. Personalised coaching, adaptive systems, recommender systems, and intelligent feedback11. Data quality, validation, reproducibility, explainability, privacy, and responsible artificial intelligence12. Translational research connecting laboratory findings with coaches, teams, players, and professional practice13. Comparative studies across games, platforms, player populations, skill levels, and competitive contexts14. Open datasets, analytical frameworks, software, methods, and reproducible research practicesRelevant article types: Original Research; Systematic Review; Review; Mini Review; Method articles; Data Report; Brief Research Report; Corrections; Community Case Study; Technology and Code; Editorial. Topic editors: Andrzej Białecki, Jan Gajewski, Oskar Szymon Słowik Source: https://www.frontiersin.org/research-topics/84772/engineering-esports-excellence-data-ai-and-high-performance-human-computer-interaction-in-gaming