About this Research Topic
In an increasingly digitalized world, computing resources, namely Artificial Intelligence (AI), particularly Machine Learning (ML) and integrated advanced Statistics are entering and changing sports training. As a consequence, these techniques may have a tremendous impact in the way coaches and athletes view their missions and roles, and influence recruiting and the technical and tactical aspects of different sports. This is the case not only for professional, high-performance settings, but also for talent development, youth specialization and coach education at every level of intervention. The need for a critical cooperation between sports and computer sciences seems urgent and mandatory for all agents involved in the process, enhancing the cross-fertilization between the rich body of knowledge existent in sport and the novel possibilities open by AI.
The present project aims to map the current situation and encourage reflexive thought among those implicated in sport sciences and computer sciences, grounded in must-needed interdisciplinary approaches. The impact of data-driven approaches, from training parameters (physiologic, functional or technical) as from competition results, at individual and team level, on athletes’ preparation and competition parameters needs to be critically assessed, At the same time, the influence of advanced analytics on the choice and orientation of both young talents and high-performance athletes increases the complexity of the whole process, viewed as a non-linear path of development. A particular attention is given to the modelling of the specialization process and its predictive power on adult performance. A holistic perspective, relating the individual and collective biological, psychological and social characteristics of those involved in competition, should contribute to promising avenues of research in the field of sports training.
We welcome different types of contributions like reviews, mini-reviews, theoretical and applied research, showing how Artificial Intelligence, particularly Machine Learning, Nature-Inspired AI and Advanced Statistics, e.g., Bayesian Statistics, can contribute to different topics in sports including, but not limited to:
- Game Analytics and Prediction
- Injury Prevention and Rehabilitation
- Cognitive Training and Mental Preparation
- Talent Identification and Prediction
- Sports Biomechanics
- Ethical and Legal Issues for AI in Sports.
The present project aims to map the current situation and encourage reflexive thought among those implicated in sport sciences and computer sciences, grounded in must-needed interdisciplinary approaches. The impact of data-driven approaches, from training parameters (physiologic, functional or technical) as from competition results, at individual and team level, on athletes’ preparation and competition parameters needs to be critically assessed, At the same time, the influence of advanced analytics on the choice and orientation of both young talents and high-performance athletes increases the complexity of the whole process, viewed as a non-linear path of development. A particular attention is given to the modelling of the specialization process and its predictive power on adult performance. A holistic perspective, relating the individual and collective biological, psychological and social characteristics of those involved in competition, should contribute to promising avenues of research in the field of sports training.
We welcome different types of contributions like reviews, mini-reviews, theoretical and applied research, showing how Artificial Intelligence, particularly Machine Learning, Nature-Inspired AI and Advanced Statistics, e.g., Bayesian Statistics, can contribute to different topics in sports including, but not limited to:
- Game Analytics and Prediction
- Injury Prevention and Rehabilitation
- Cognitive Training and Mental Preparation
- Talent Identification and Prediction
- Sports Biomechanics
- Ethical and Legal Issues for AI in Sports.
Keywords: AI, ML, Training, Ethics, Biomechanics, Analytics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
