2026/2027

General Description: The integrated course “Research Methodology in Sport and Performance Analysis” aims to provide students with advanced skills to manage and analyse sport-related data using digital tools and dedicated software, design and conduct research projects, from critical source selection to scientific writing and revision, and apply performance analysis methods to describe, interpret, and communicate sport performance in a rigorous manner. The course combines theoretical content with hands-on technical-practical activities (TPA/ATP) focused on operational workflows, platforms, and analytical procedures. Learning Objectives and Expected Learning Outcomes: 1. Knowledge and Understanding: ● The student will understand the fundamentals of data management, applied/statistical analysis, and research methodology; know the principles, aims, and tools of performance analysis in sport contexts. 2. Applying Knowledge and Understanding: ● The student will be able to apply data cleaning, organisation, and analysis procedures; use software and programming tools for processing and visualisation; develop a research project and produce structured scientific texts; apply performance analysis methods to real-world cases 3. Making Judgements: ● The student will be able to critically evaluate the quality, reliability, and limitations of data, methods, and results; select analytical approaches aligned with study aims; interpret evidence and formulate well-grounded conclusions 4. Communication Skills: ● The student will develop the ability to clearly communicate research and analysis outcomes to specialist and non-specialist audiences through reports, presentations, and effective visualisations; use appropriate terminology and justify methodological choices. 5. Learning Skills: ● The course will contribute to the development of autonomy in continuous updating on digital tools, software, analytical procedures, and scientific literature, transferring competencies across contexts and datasets.

There are no prerequisites

Teaching materials provided by the lecturer.

Performance Analysis
Introduction to Python programming (basic syntax, variables, control structures, functions)
Development environment setup and virtual environment management (venv)
Introduction to NumPy for numerical computing
Use of Pandas for data management and analysis
Cleaning and preprocessing sport performance datasets
Practical exercises in sport performance analysis
Sport performance analysis: use and interpretation of data to support technical

Thomas, Nelson, Silverman – Research Methods in Physical Activity
Lames – Performance Analysis in Game Sports: Concepts and Methods
Teaching materials provided by the lecturers, including slides, notebooks, practical exercises, datasets, and guided tasks.

Three hours per week

the objective of the written exam is to verify that the student has understood all the theoretical concepts seen in class

    Diego Pennino