MSc IN BIG DATA
APPLIED TO
SCOUTING
IN FOOTBALL

Big Data has opened a cross-cutting opportunity for its optimal application in sports, particularly in football.
It is therefore a useful tool to strengthen the development of the sporting direction. It is very difficult to understand one of the most important departments in a football club without the professional use of data.
Ultimately, data is a fundamental pillar that helps optimize processes, identify talent in any competition, and minimize the role of luck.

1. Big Data and football
2. Structure, organization, and planning of a football sporting direction
3. The basic pillars of a sporting direction
4. Raw and refined scouting
5. Player profiles
6. Negotiation
7. Adaptation
8. Data and the reduction of luck factor
9. Youth academy

The figure of the scout is a key component without which a sporting direction cannot function. However, it is the sporting director who must set the guidelines and steps to follow in order to achieve harmony that yields meaningful results in the scouting process. The scout follows the directives of the sporting director and proposes solutions to enhance the established strategy and identify potential players that fit the implemented style of play. Ultimately, the scout is responsible for creating both visual and written reports to present to the sporting director.

• The scout in the sporting direction.
• Scout’s responsibilities.
• Scout’s working methodology.
• Real-world cases of a scout’s role in professional football.

Football cannot be understood without data, as it is the best complement for decision-making. Data providers such as Wyscout, Opta, and Instat are essential for both scouts and analysts within a football club to begin executing their analyses. They are the starting point, and this module focuses on selecting the appropriate performance indicators based on set objectives and how to export them. In addition, the clear differences between providers will be addressed, along with their internal structures and the advanced capabilities they offer.

• Introduction to data providers
• Opta
• Scout74
• Instat
• Wyscout
• MediaCoach
• StatsBomb
• Statistics platforms

La explosión del Big Data en el mundo actual alcanza y debe alcanzar al plano micro. El scout no solamente ha de quedarse en el conocimiento de qué proveedores de datos existen en la industria y el cómo funcionan, sino también en su contenido más técnico. Un análisis o proceso de seguimiento carece de sentido si las métricas o indicadores del rendimiento a aplicar no se conocen con total certeza. Es fundamental saber qué miden y qué alcance tienen cada una de las métricas que se pueden aplicar en el análisis del rendimiento de un jugador, tanto en fase ofensiva como en fase defensiva.

1. La importancia del dato en el plano micro.
2. Métricas ofensivas: concepto e interpretación.
3. Métricas defensivas: concepto e interpretación.
4. Construcción y enfoque de métricas avanzadas.
5. Las métricas de evaluación del portero.
6. Caso real de aplicación de métricas de un scout.

The sports management department requires interactive dashboards that offer objective and personalized visualizations. The scout plays an essential role in developing these tools for the technical secretariat. Data visualizations allow for more impactful decision-making, save time, and automate information gathering. This way, selectable players and their standout metrics can be identified based on defined filters, in addition to generating individual reports and statistical relationships tailored to each position.

1. Introduction to different tools for micro data analysis.
2. Introduction to Tableau: elements, resources, and chart types.
3. Evolutionary and cumulative reports of selected players in Tableau.
4. Introduction to Microsoft Power BI.
5. Visualization of game events at the micro level in Power BI using data from Opta, Instat, Wyscout, and Mediacoach.

Individual performance analysis must include both an objective and a subjective part. To visually showcase the player’s technical-tactical skills, it is essential to master tools such as NacSport or Metrica Sports. This module focuses on individual videos that support game patterns detected in the data. Additionally, it is fundamental to consider the player’s team environment and playing model, as ignoring these can lead to incomplete performance interpretations.

1. Introduction to video analysis.
2. Methodology to optimize and enhance the tools.
3. Matrices and transformation of eventing into useful data.
4. Real cases of video analysis.
5. Building the support material for the written report.

The form and structure of the individual report must follow fixed patterns in order to convey and conclude with maximum accuracy the characteristics of the player being studied. The presentation of data together with the subjective sections and their corresponding visual support must be combined in the most professional way possible. Therefore, in this module, you will learn how to formalize individual reports and share them with the sporting management.

1. Definition of individual report within a professional football sporting management.
2. Theoretical foundations applicable to the editing of technical reports.
3. Report models to be produced and their characteristics.
4. The inclusion of data in technical reports.
5. Real cases of reports together with their interactive dashboards.

The youth academy is the cornerstone of every project in a club. The future of the teams depends on having a good structure and methodology, both in the coaching staff and in the scouting departments. The role of the scout is key in the youth categories and can even become the cornerstone of the club’s future projection. Detecting not only talent but also players who can build a career at the club and develop a sense of belonging is essential. In this entire process, technology is not as established as in professional football, and therefore, the human eye and knowledge of Smart Data will help minimize errors in tracking players to be incorporated into the youth scouting department.

1. Mission and importance of the youth academy.
2. Philosophy and values of the youth academy.
3. Methodology in the youth scouting department.
4. Design of a youth monitoring model.
5. Individualized attention in the comprehensive training process.
6. The keen eye and Smart Data.
7. Data collection systems in the youth academy.
8. Real cases of scouting reports.

The importance of analytics is now a reality in the world of sports. The automation of processes and the development of algorithms will allow better decision-making to achieve maximum benefit. With the programming languages R and Python, we will learn to use the most important languages in Data Science at each of its stages: information extraction, data cleaning and preprocessing, visualization of results, and their application to use cases in the world of football.

1. Introduction and basic programming with R.
2. Introduction and basic programming with Python.
3. Data cleaning, preprocessing, and visualization with R and Python.
4. Data analysis applications with Python.
5. Introduction to player similarity.
6. Real cases of scouting reports.

Machine Learning is a computational technique whereby the system is able to learn models based on examples rather than instructions. In the world of sports, we are in an early phase; however, there are many applied use cases in sports where the use of algorithms that detect patterns in the initial data will make a difference.

1. Introduction to Machine Learning
2. Types of machine learning algorithms
3. Use cases in the world of football

The Master’s Final Project will consist of choosing one or several of the topics covered throughout the course, with the recommendation to follow a gradual and progressive line similar to that of the Master’s program.