Master in Sport Analytics

• Comprehensive overview of professional sports club structures and organizations.
• Key departments, roles, and processes in modern sports management.
• The transversal role of data: its impact on decision-making across sporting, medical, financial, and communication domains.
• Embracing a data-driven culture within sports organizations.
• Real-world examples of data-centric structures in various sports.

• Historical evolution of sports analytics: from observational analysis to machine learning.
• Core statistical principles and their application in sports.
• Types of sports data (tracking, eventing, biometric, GPS, etc.).
• Introduction to ETL processes and the data lifecycle in sports.
• Case studies on data-informed decision-making across club departments.

• Foundations of relational (SQL) and non-relational (NoSQL) database design and management.
• Structuring databases to efficiently store and query sports data.
• Practical introduction to SQL tailored to sports databases.
• Integrating data from multiple sources and providers (tracking, scouting, external platforms).
• Data privacy and security protocols in athlete data management.

• Hands-on introduction to R and Python for sports data analysis and modeling.
• Data cleaning, transformation, and analysis using tidyverse and pandas.
• Building predictive and descriptive models with scikit-learn and caret.
• Advanced data visualization: dashboards with ggplot2, plotly, seaborn, and interactive libraries.
• Essentials of Tableau and Power BI for rapid reporting and executive dashboards.
• Automating visual reports and creating custom outputs for clubs and federations.

• Core principles of computer vision applied to video and tracking analysis.
• Event, pose, and behavior pattern detection in sports imagery.
• Building deep learning models using TensorFlow and PyTorch.
• Real-world applications of AI in scouting, tactics, and physical performance.
• Case studies in football, basketball, tennis, and other disciplines.

• Foundations of tactical and performance analysis in team sports.
• Designing and interpreting advanced individual and team metrics.
• Identifying game patterns and tactical trends using data.
• Role- and style-specific KPIs for performance evaluation.
• Creating tactical reports and presentations for coaching staff.
• Real-world examples of tactical analysis in clubs and national teams.

• Essentials of modern scouting: data scouting, visual scouting, and hybrid models.
• Designing player profiles and tactical fit models.
• Talent evaluation through predictive modeling and scenario simulation.
• Squad building and market planning based on data insights.
• Tools for medium- and long-term decision-making in sports departments.
• Successful case studies in data-driven sports management.

• Business models in the sports industry: traditional and emerging revenue streams (ticketing, media rights, sponsorship, hospitality, digital commerce).
• Executive visualization and narrative for senior management: financial KPIs and executive dashboards.
• Data storytelling for executive leadership and marketing.
• Analytics of social media, digital platforms, and OTT services.
• Conversion funnels and data-driven activation strategies.
• Final workshop: Executive report on digital business.

• Group development of a project applied to a real-world case.
• Methodological supervision and mentoring by academic staff.
• Final evaluation and presentation.

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This master's qualifies you to manage and lead professional sports analysis and data management projects. You will be able to work in roles such as:

• Sport Scientist, carrying out scientific analyses to optimise sports performance.
• Sport Data Scientist, managing large volumes of data to extract key information for decision-making.
• Data Scout, using data analysis to identify patterns and trends to discover talent.
• Scout, specialising in scouting for players and analysing the opposition for competitive teams.
• Match analyst, breaking down tactics, strategies and statistics to support coaching staff and improve team performance.

• Match analyst.
• Sports data scientist.
• Sports data engineer.
• Tactical analyst.
• Data scout.
• Data-driven sporting director.
• Performance analyst.
• Head of analysis department.

• Sports data strategist.
• Business intelligence manager (sports sector).
• Sports marketing data analyst.
• Communication & data specialist.
• Visualization & reporting expert in sports.
• Consultant for digital transformation in sports organizations.

• AI solutions developer for sports.
• Computer vision specialist for performance.
• Product owner in sports tech companies.
• Fan engagement & digital strategy specialist.

For the access to the Master's Degree/University Specialist/University Expert it is necessary to meet one of the following requirements:

• To be in possession of a University Degree.
• If applicable, accredit professional experience of at least 6 months that is directly related to the field of the degree.
• If applicable, accredit professional internships of CFGS that are directly related to the scope of the degree.

This Master aimed at professionals in the sports industry who want to build a career in the field of match and data analysis, such as:

• Graduates with a degree in physical activity and sports sciences
• Graduates with degrees such as engineering, mathematics, big data or other fields related to the area of knowledge.
• Coaches and members of coaching teams who want to understand match play better and to develop the skills needed to enable them to improve team and player performance.
• Former players who wish to transfer their skills and experience into the field of match analysis.
• Professionals working with new technologies with a passion for sport, who wish to use their knowledge to help develop this sector.