The course provides the theoretical and applied foundations of human movement The course examines the growing role of data analytics and Artificial Intelligence in the transformation of the sports system. Data-driven approaches enable improved performance analysis, support training processes, prevent injuries, identify talent, and contribute to decision-making within sports organizations. A first area of the module focuses on data analytics and modeling methods applied to sport, including data collection and management, the definition of performance metrics, and the development of predictive models and decision-support tools based on Artificial Intelligence and machine learning techniques. The course also introduces methodological aspects of sports performance analysis, distinguishing between observable and measurable factors and addressing the management and interpretation of data in the context of performance, including applications to both sport and parasport. Another part of the module considers sport and physical activity as drivers of health, wellbeing, and social innovation. Starting from the FISU Healthy Campus framework, the course analyses the relationships between sport practice, health, inclusion, and social and cultural contexts, also considering the contribution of non-competitive sport practices from a salutogenic perspective. Finally, the course introduces elements of economics and management of sports organizations, with reference to the characteristics of the sports ecosystem and to the relationships between sporting results, economic and financial sustainability, and sporting events. The course adopts an interdisciplinary approach and combines theoretical lectures, case studies, and applied activities aimed at developing competencies in sports data analysis and in understanding the technological, organizational, and social implications of innovation in sport.

At the end of the course, students will be able to: • demonstrate knowledge of the main concepts and methods of data analytics and Artificial Intelligence applied to sport; • identify relevant data to be analyzed and understand how such data can be collected (sensor selection, sensing strategy and acquisition infrastructure, data collection procedure, signal and data cleaning and processing, including precision, timing and storage constraints); • understand existing frameworks for data analytics and AI-based solution design; apply data-driven approaches to support sports management, performance analysis, and decision-making; • design and develop AI solutions in specific sport contexts; • interpret and communicate analytical results effectively; • use practical tools and methods through hands-on and project-based activities; • critically assess the economic, social, organizational, and ethical implications of innovation in sport; • understand the role of sport and physical activity in promoting physical, mental, and social wellbeing from a holistic perspective; • recognize the potential of sport practices, including non-competitive activities, in fostering social inclusion and active communities; • understand how data and indicators can support the analysis of relationships between sport practice, wellbeing, and social contexts; • understand and interpret differences between sport and parasport and evaluate factors influencing sports performance; • understand the main characteristics of the sports ecosystem and the basic principles of management and economic sustainability of sports organizations.

No prerequisites are required.

The course introduces the main concepts, methods, and tools of data analytics and Artificial Intelligence applied to sport, with particular reference to performance analysis, sports management, and strategic decision-making. It addresses the collection, analysis, interpretation, and visualization of sport-related data, as well as the design and development of AI-based solutions in specific sport contexts. Key topics include data selection and acquisition procedures, performance monitoring, tactical and strategic analysis, injury prevention, talent identification, and the use of data-driven approaches to support decision-making in both individual and team sports. Particular attention is devoted to the application of machine learning methods and Artificial Intelligence techniques for the modeling, prediction, and optimization of sports performance and organizational processes. The course also introduces methodological aspects related to sports performance analysis, including the distinction between observable and measurable factors and the interpretation of data collected in sport contexts. These aspects are discussed with reference to both sport and parasport, highlighting the role of data in understanding and evaluating performance in different sporting environments. In addition, the course considers sport and physical activity as drivers of health, wellbeing, and social innovation. In this perspective, the role of sport in promoting active lifestyles, social inclusion, and community development is analyzed, also in relation to initiatives such as the FISU Healthy Campus framework. The broader economic, organizational, social, and ethical dimensions of innovation in sport are also addressed. In particular, the course introduces the main characteristics of the sports ecosystem and discusses the relationships between sporting performance, economic sustainability, and the management of sport organizations and events. Teaching activities include hands-on sessions based on real or realistic sport datasets, practical exercises, and case studies aimed at developing technical skills in data analysis, AI methods, and result interpretation. A project work, carried out in small groups, will enable students to apply the acquired knowledge to a concrete sport-related problem, while also strengthening transversal skills such as teamwork, problem solving, critical thinking, and effective communication.

• Theoretical lectures • Project work (group-based projects in which students identify sport-related challenges and design data-driven and AI-based solutions) • Case study analysis • Seminars with industry experts • Practical activities and discussion of scientific articles • Guided class discussions and peer discussions

Reading materials, including scientific articles, technical reports, and other supporting study materials, will be provided and indicated by the instructors during the course.

Slides; Dispense; Esercizi; Materiale multimediale ;

Modalita di esame: Accertamento (esame senza voto); Elaborato progettuale individuale; Elaborato progettuale in gruppo;

Exam: Check; Individual project; Group project;

... Assessment will take into account the students’ ability to analyze sport-related data, interpret analytical results, and connect theoretical concepts with practical applications in the context of sports performance, management, or innovation. Participation in project activities, case study discussions, and other course activities may also contribute to the overall evaluation.

Gli studenti e le studentesse con disabilita o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unita Special Needs, al fine di permettere al/la docente la declinazione piu idonea in riferimento alla specifica tipologia di esame.