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Tutorials

The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.



Tutorial on
Application of Artificial Intelligence in Competitive Sport


Instructor

Raúl Arellano
Aquatics Lab, Dep. Physical Education and Sports, Faculty of Sports Science, University of Granada
Spain
 
Brief Bio
Professor Raúl Arellano Colomina has been associated with the University of Granada since the inception of the Faculty of Sport Sciences, where he currently serves as Full Professor (Catedrático de Universidad) and Director of the Aquatics Lab. His academic background includes a Doctorate in Physical Education (University of Granada, 1992) and a Master’s degree in High Performance Sports (Autonomous University of Madrid, 2001). His scientific contributions have substantially advanced aquatic sports research, particularly in swimming technique, competition analysis, propulsive force evaluation, and, more recently, the integration of artificial intelligence and deep learning in aquatic performance studies. He has authored over 100 articles in indexed journals, accumulating more than 3,300 citations and achieving an h-index of 30. His pioneering work, notably in undulatory underwater swimming, has significantly influenced international research agendas. He has directed 14 doctoral theses, all awarded the distinction of “Sobresaliente Cum Laude,” consolidating his role in shaping new generations of researchers. His engagement in elite sport contexts is remarkable: he contributed scientifically to four editions of the Olympic Games (Barcelona 1992, Sydney 2000, London 2012, and Tokyo 2020) and multiple World Swimming Championships (1993, 2003, 2013, 2019, and 2021), providing biomechanical analyses of elite swimming performances. In terms of institutional service, he has held several academic leadership positions, including Director of the Department of Physical and Sport Education, Vice Dean of Academic Organization (Vicedecano de Ordenación Académica), and Coordinator of the Master’s Degree in Research in Physical Activity and Sport. Moreover, he has actively contributed to academic quality assurance processes, serving as commission president for program accreditation at agencies such as ANECA and Madri+d. Professor Arellano’s scientific visibility extends internationally through his membership on the editorial boards of leading journals such as the Journal of Sports Sciences and the International Journal of Sport Science and Coaching, as well as through his service as an ad hoc reviewer for numerous prestigious scientific journals. Furthermore, he has directed multiple nationally competitive research projects (e.g., SWIM I, SWIM II, and SWIM III) and collaborated extensively with industry partners to bridge applied research with technological innovation.
Abstract

1. Introduction to Artificial Intelligence and its current status.
2. Artificial Intelligence tools and their application to sport.
3. Development of Deep Learning and Vibe Coding projects, use cases.

Artificial Intelligence (AI) is becoming a transformative tool in competitive sport, enabling coaches, sport scientists, and physical education professionals to analyze performance with unprecedented precision. This 1.5-hour course introduces participants to the practical use of AI in sport, with a strong emphasis on applied methodologies and accessible coding strategies.

The session begins with a brief overview of AI concepts, distinguishing classical machine learning from recent advances in deep learning and computer vision. This foundation prepares participants to understand the role of AI in video-based analysis, performance prediction, and athlete monitoring. The focus then shifts to practical implementation through vibe coding, a hands-on and simplified coding approach that allows non-experts to test and adapt AI tools in real scenarios. Using open-source libraries and structured examples, participants will explore how video data can be processed to detect technical patterns, extract kinematic variables, and provide immediate feedback for coaching decisions.

The course concludes by addressing ethical considerations, data privacy, and the importance of interdisciplinary collaboration. Participants will leave with a clear understanding of AI’s practical value, as well as concrete strategies to incorporate vibe coding into their professional or academic activities.


Keywords

Artificial Intelligence, Competitive Sport, Vibe Coding, Performance Analysis, Coaching

Aims and Learning Objectives

The course aims to introduce professionals, coaches, and students to the practical application of Artificial Intelligence (AI) in competitive sport, with a specific focus on accessible coding strategies (vibe coding) for performance analysis, athlete monitoring, and coaching support.

Learning Objectives

By the end of the session, participants will be able to:
1. Understand the fundamental concepts of AI, machine learning, and computer vision in sport contexts.
2. Apply vibe coding techniques to implement simple AI tools for video-based performance analysis.
3. Interpret outputs from AI systems (e.g., kinematic data, stroke detection, pacing trends) to support coaching decisions.
4. Evaluate the benefits and limitations of AI adoption in sport, including issues of data privacy and ethical use.
5. Integrate AI-based approaches into academic projects, research, or professional practice with minimal technical barriers.


Target Audience

PE professionals, coaches, sport science students, PhD candidates

Prerequisite Knowledge of Audience


1. General Knowledge of Sport Sciences
• Familiarity with concepts in training, performance analysis, and athlete monitoring.
• Basic understanding of biomechanics and physiology in sport contexts.
2. Digital Competence
• Comfort with standard digital tools (e.g., video analysis software, spreadsheets).
• No advanced coding skills required, but openness to hands-on demonstrations with Python or R.


Detailed Outline

1. Introduction to Artificial Intelligence and its Current Status (30 min)
•Overview of AI concepts: machine learning, deep learning, computer vision.
•Current role of AI across industries, with emphasis on sport sciences.
•Evolution from traditional performance analysis to data-driven approaches.
•Opportunities and challenges: accuracy, transparency, and ethical considerations.

2. Artificial Intelligence Tools and their Application to Sport (30 min)
•Video-based analysis: pose estimation, stroke detection, movement tracking.
•Predictive analytics: pacing strategies, load monitoring, injury risk assessment.
•Sensor and wearable integration: real-time biomechanical and physiological data.
•Practical examples from ompetitive sports.

3. Development of Deep Learning and Vibe Coding Projects: Use Cases (30 min)
•Introduction to vibe coding: simplified, hands-on coding for sport professionals.
•Demonstration of open-source Python tools for video and performance analysis.
•Case studies:
•Swimming stroke recognition.
•Training microcycle optimization.
•Real-time feedback for coaches and athletes.
•Guidelines for designing small AI projects in academic or applied contexts.


Secretariat Contacts
e-mail: icsports.secretariat@insticc.org

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