MSc IN BIG DATA
APPLIED TO
SCOUTING
IN FOOTBALL

Big Data has opened a transversal door for its optimal application in sports, specifically in football. For this reason, it is a useful tool to reinforce the structure of sports management. It is very difficult to understand one of the most important departments in a football club without the professional application of data. Ultimately, data is a fundamental pillar that helps optimize processes, detect talent in any competition, and minimize the factor of luck.

1. Big Data and football.
2. Structure, organization, and planning of football sports management.
3. The basic pillars of sports management.
4. Raw and net tracking.
5. Profiles.
6. Negotiation.
7. Adaptation.
8. Data and the reduction of luck’s factor.
9. The youth academy.

The scout is a key component without which a sports management system cannot function. However, it is the sports director who must set the guidelines and steps to follow in order to create harmony that yields promising results in the scouting process. The scout follows the instructions of the sports director and provides solutions to enhance the established strategy and identify potential players that fit the implemented playing style. Ultimately, it will be the scout who compiles visual and written reports to present to the sports director.

• The scout in sports management.

• Scout’s responsibilities.

• Scout’s working methodology.

• Real-life 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 analysis. They serve as the starting point, and this module focuses on selecting the appropriate performance indicators based on the set objectives and their exportation. Additionally, the module will highlight the differences between providers and showcase their internal structures, as well as all the advanced possibilities they offer.

Introduction to data providers.
Opta
Scout74
Instat
Wyscout
MediaCoach
StatsBomb
Statistics portals.

The explosion of Big Data in today’s world reaches and should reach the micro level. The scout must not only understand what data providers exist in the industry and how they function, but also delve into their more technical content. An analysis or tracking process is meaningless if the performance metrics or indicators to be applied are not fully understood. It is crucial to know what each metric measures and its scope when analyzing a player’s performance, both in the offensive and defensive phases.

• The importance of data at the micro level.

• Offensive metrics: concept and interpretation.

• Defensive metrics: concept and interpretation.

• Construction and focus of advanced metrics.

• Goalkeeper evaluation metrics.

• Real-life case study of a scout’s application of metrics.

The importance of analytics is already a reality in the sports world. The automation of processes and the development of algorithms will allow for 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, and result visualization, and their application to use cases in the world of football.

Introduction and basic programming with R.
Introduction and basic programming with Python.
Data acquisition, cleaning, preprocessing, and visualization with R and Python.
Applications of data analysis with Python.
Introduction to player similarity.

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

• Introduction to Machine Learning
• Types of machine learning algorithms
• Use cases in the world of football

The sporting management cannot be understood without the presence of interactive dashboards that ensure objective and personalized visualizations for the sports director. The scout plays a key role in the process of creating customized tools for the technical secretariat. Data visualizations allow for a high impact on decisions, optimize time, and automate the entire process of capturing and displaying information. In a single view, the scout can identify which player(s) are selectable and the metrics in which they excel, based on a series of pre-set filters and parameters. Additionally, through visualization tools, different statistical and descriptive relationships are built, allowing the performance indicators to be coherently adjusted by position, as well as designing in-depth individual reports.

• Introduction to different tools for micro-level data analysis.

• Introduction to Tableau: elements, resources, and types of graphs.

• Evolutionary and cumulative reports of selected players in Tableau.

• Introduction to Microsoft Power BI.

• Visualization of game events at the micro level in Power BI with data from Opta, Instat, Wyscout, and Mediacoach.

The objective aspect of individual player performance analysis is key, but the subjective viewpoint is equally important. To visually justify the player’s skills, abilities, and technical-tactical qualities, a deep understanding of tools such as NacSport, Eric Sports, and Metrica Sports becomes essential. The focus of this module is on creating individual player video profiles, as it is crucial that these videos justify the game patterns derived from the data, both at the macro and micro levels. One of the often overlooked aspects, which must be carefully considered, is the environment in which the player is analyzed. Without understanding the team’s game model, a significant amount of information is lost. Therefore, the position of the team within its competitive environment is another pillar that must be studied in individual performance analysis.

• Introduction to NacSport, Eric Sport, and Metrica Sports

• Methodology to optimize and enhance the tools

• Eventing matrixes and transformation into data

• Real cases of video analysis

• The team’s position in its competitive environment

• Construction of support for the written report

The format and structure of the individual report must follow fixed patterns to convey and conclude with maximum precision the characteristics of the player being studied. The presentation of data, along with 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 sports management team.

Contents:

• Definition of an individual report within a professional football sports management team.

• Theoretical fundamentals applicable to the editing of technical reports.

• Report models to be created and their characteristics.

• The inclusion of data in technical reports.

• Real cases of reports along with their interactive dashboards.

The academy is the cornerstone of any project in a club. The future of teams depends on having a solid structure and methodology, both in terms of coaches and recruitment scouts. The scout figure is crucial in youth categories and can even be the key to the future development of the club. Detecting not only talent but also players who can build a career within the club and foster a sense of belonging is an essential factor. In this process, technology is not as established as in professional football, so the human eye and knowledge of Smart Data will help minimize errors when tracking players to incorporate into the academy’s technical staff.

• Mission and importance of the academy.

• Philosophy and values of the academy.

• Methodology in the academy recruitment department.

• Design of an academy monitoring model.

• Personalized attention in the process of comprehensive training.

• The trained eye and Smart Data.

• Data collection systems in the academy.

• Real-life examples of recruitment reports.

The Master’s Final Project will involve selecting one or more of the topics covered throughout the course, with a recommendation to follow a gradual and progressive approach similar to that of the Master’s program.

Objectives:

• Internalize what has been learned and developed in the ten modules, demonstrating an understanding of the proposed process.
• Understand data as a complement and not as an intruder to achieve maximum optimization in decision-making.
• Get to know the structure, resources, and functionality of each of the main data providers in the football industry.
• Learn the general and advanced metrics offered by different data providers, both in the offensive and defensive phases.
• Learn how to design and execute algorithms applied to scouting in R and Python, as well as their interpretability and the extraction of conclusions related to talent detection.
• Understand how Tableau and Microsoft Power BI work, as well as all the resources they provide to build custom dashboards and evolutionary and cumulative reports.
• Understand the scouting processes in youth academies and the application of Smart Data for their optimization.