MASTER’S DEGREE IN FOOTBALL ACADEMY ANALYTICS

The course examines how the organizational structures of youth clubs influence the implementation of data analysis systems. It studies the interactions between departments (technical, medical, physical, psychological) and their information management.

Based on real cases, students will identify key factors for integrating data systems into development processes. They will also learn to map information flows and tackle the challenges of digital transformation in traditional sports organizations.

1. Organizational structures in youth football
2. Mapping information flows in development environments
3. The data analyst within the development structure
4. Sports methodology and its connection with technical systems
5. Change management in traditional sports environments
6. Specific limitations and opportunities
7. Case studies of elite organizations

The course introduces the fundamentals of Big Data applied to youth football, analyzing the unique characteristics of academies and the potential of their data sources.

Students will understand how to transform heterogeneous data into useful decision-making tools and will explore key technologies and emerging trends as a foundation for later technical modules.

1. Conceptual fundamentals of Big Data in sports environments
2. The data ecosystem in youth football
3. Technological infrastructures for academies
4. Application opportunities by specific areas
5. Data governance in development environments
6. From Big Data to Smart Data in grassroots football
7. Success stories and best practices

The course covers the design of digital transformation strategies for football academies, addressing the assessment of digital maturity, the creation of scalable data architectures, and organizational change management.

Students will learn to develop strategic roadmaps considering impact and feasibility, and to apply project management methodologies adapted to sports contexts with limited resources.

1. Diagnosis and evaluation of digital maturity
2. Design of data architectures for academies
3. Strategic planning of digital transformation
4. Project management methodologies in sports environments
5. Definition and measurement of digital transformation KPIs
6. Change management in sports organizations
7. Practical cases of digital transformation in academies

The course addresses the alignment of information systems with each club’s sports methodology, translating technical-tactical concepts into coherent data structures.

Students will learn to design models that reflect the club’s development identity, with KPIs adapted by category, ensuring that technology reinforces consistency between training, competition, and individual development.

1. Translating sports methodologies into data models
2. Design of specific taxonomies and ontologies
3. Development KPIs by category and position
4. Architectures for performance tracking
5. Validation of methodological consistency with data
6. Data integration for overall evaluation
7. Practical cases of methodological alignment

The course explores video analysis technologies and methodologies applied to youth football, focusing on converting visual information into useful metrics.

Students will learn to manage the entire analysis process, optimize workflows with limited resources, and integrate data with sports management systems.

1. Advanced video analysis technologies in development environments
2. Design of coding systems for youth football
3. Adapted technical-tactical analysis methodologies
4. From qualitative analysis to quantifiable data
5. Optimization of processing workflows
6. Integration of video analysis into data ecosystems
7. Effective communication of findings

The course focuses on the design of Business Intelligence systems for youth football, emphasizing effective data visualization and user-centered design.

Students will learn to create intuitive visualizations with professional tools, integrate diverse data sources, and customize dashboards for different profiles within the development environment.

1. Fundamentals of sports data visualization
2. User-centered design for development environments
3. Professional BI tools in sports contexts
4. Advanced design of development dashboards
5. Automation of reports and alerts
6. Specialized visualizations for youth football
7. Practical implementation cases

The course covers advanced data engineering techniques in youth football, focusing on automated and scalable solutions for capturing, processing, and storing information.

Students will learn to use Python and MySQL to build efficient data architectures, adapted to the characteristics of the youth environment and optimized for resources.

1. Python fundamentals for sports analysis
2. Advanced data acquisition techniques
3. Design and optimization of MySQL databases
4. Development of ETL pipelines for development data
5. Sports data quality and validation
6. Integration of heterogeneous data sources
7. Implementation of autonomous systems

The course applies Artificial Intelligence and Machine Learning techniques to youth football, developing predictive models and advanced metrics to evaluate performance and potential.

Students will learn to implement techniques adapted to youth data and to create indicators that go beyond traditional statistics, capturing complex aspects of the game.

1. Fundamentals of ML applied to sports development
2. Predictive models for potential evaluation
3. Classification and clustering systems
4. Advanced metrics for youth football
5. Deep Learning applied to technical-tactical analysis
6. Implementation of recommendation systems
7. Model evaluation and interpretation

The course transforms traditional scouting through advanced data-based systems, integrating quantitative and qualitative information to enhance talent acquisition.

Students will learn to design complete scouting processes, define target profiles, evaluate talent in different contexts, and optimize resources through data-driven prioritization.

1. Digital transformation of youth scouting
2. Design of multidimensional evaluation frameworks
3. Comparative metrics across competitions
4. Data-driven optimization and prioritization
5. Integration of video analysis into scouting processes
6. Technological tools for talent acquis

The course focuses on advanced systems to evaluate and project internal talent toward professional football, using data-driven methodologies.

Students will learn to design predictive models that integrate performance and potential, and to identify the optimal timing for promotion, optimizing the transition to higher categories.

1. Comprehensive internal evaluation frameworks
2. Predictive models of growth and development
3. Comparative analysis with professional requirements
4. Readiness indicators for promotion
5. Optimization of promotion timing
6. Prioritization and strategic talent management
7. Case studies and retrospective validation

The course focuses on comprehensive systems for holistic player monitoring, integrating technical, physical, medical, psychological, and academic data.

Students will learn to design 360° monitoring models that support individualized plans and coordinated decisions, optimizing the development process through the integration of heterogeneous information.

1. Fundamentals of comprehensive development in youth football
2. Data architectures for interdepartmental integration
3. Physical monitoring and injury prevention
4. Cognitive and psychological development
5. Comprehensive academic-sport monitoring
6. Data-driven individualization
7. Implementation of continuous monitoring systems

The Final Project allows students to practically apply the knowledge acquired, developing an innovative data-driven solution for youth football.

Students will tackle all phases of the project, from identifying the challenge to evaluating results, working with sports organizations and applying professional methodologies.