Because we were the pioneers in creating  the first MSC’s Degree in Sports Big Data, in Spanish, in the world, and we are also the only ones certified by the most prestigious Sports University: UCAM.

Because it is a MSc designed by and for professionals, or for all those who one day want to dedicate themselves to this. With a methodology that tries to adjust and take into account the professional and personal reality of each student, making it as easy as possible to follow the program: Tutoring system, 24/7 community through discussion forums and questions in platform, WhatsApp, live tutoring, work groups and a huge academic team at your service… essential to make all the processes you need throughout your training as easy as possible. You will never Walk alone 😉

Because, in the 8 editions held to date, they have trusted, have been trained and continue to be trained with us, students, professionals, analysts and members of the staff of the main football clubs in the world, both nationally:

Real Madrid, Atlético de Madrid, FC Barcelona, Sevilla FC, Real Betis Balompié, Levante UD, Deportivo Alavés FC (men and women), UD Las Palmas, Villarreal, Granada, Celta, Athletic Club, Cádiz, Valencia, Elche UD, Eibar, RCD Mallorca, SD Huesca (women), UD Leganes, Sporting de Gijón, Zaragoza, Albacete, Rayo Vallecano (women), Madrid CFF (women), Spanish National Team…

and internationally:

Paris Saint Germain, Leeds United, Tottenham Hotspur FC, Wesh Ham, Watford, Sporting Clube de Portugal, FC Oporto, River Plate, Boca Juniors, Newells Old Boys, Independiente del Valle, Atlético Peñarol, Chinese National Team, Internacional de Portoalegre, Club América, América de Cali, Cruzados de Chile, Club Celaya, Guangzhou Evergrande, Vélez, Nancy …

And also from Basketball:

Mombus Obradoiro, Valencia Basket, Seleccion Absoluta argentina Basket, Seleccion absoluta china Basket, Perfumerias Avenida…

And more top-level sports:

Spanish Athletics Federation, Logroño Handball, Veszprem Handball, Alcobendas Rugby, Spanish Field Hockey Team (both female and male), UPCN Voley, …

And the best of all is what we are creating together: Sports Data Campus and our students, teachers and collaborators, the largest Family of specialists in the application of Big Data and Advanced Analytics to Sport: working in innovation, leading the way and professionalizing the figure of the sports data analyst, a key profile in the present and in the future.

Because you will have at your disposal the best MasterClasses, which you can follow live or recorded, to enjoy and learn first-hand from the greatest exponents of the sports data industry and sports in general… and that will make you unique. We also periodically organize events of national and international relevance, such as the Football Data International Forum (Wanda Metropolitano, January 2020), or the recent Sports Data Forum (Ramón Sánchez Pizjuán, February 2021 and 2022).

Among the professionals you will be able to see in our classes and events are:

Ramón Rodríguez Verdejo “Monchi” (General Sports Manager Sevilla FC)

Víctor Orta (Director of Football Leeds United)

Nicolas Evans (Head of Football Research & Standards in FIFA)

Chechu Fernández (KAM Spain in Stats Perform)

Roberto López del Campo (Mediacoach Project Coordinator & Sports Research Area at Laliga)

Miguel Almeida Ferreira (Scout Sporting Club de Portugal)

Pablo Sanzol (Director of the Analysis Department at SD Eibar)

Jóse Rodríguez (Data and Performance Analyst in Aston Villa FC)

Fredi Martin (Assistant to the Chinese National Soccer Team)

Mikel Gandarias (Analyst RCD Mallorca)

Cristóbal Fuentes (Physical trainer at Levante CF)

 

And many more…

Because all our training programs are carried out in collaboration with the leading and most representative companies in the sector:

Data providers/managers:

La Liga, Besoccer, Opta, Stats Perform, Mediacoach, Besoccer Pro, Instat, Wyscout, Nacsport, Hudl, Catapult, Oliver, Eric Sports…

Organizations and Federations:

MARCA, FIFA, UCAM, IBM, Excellence Innova, Big Data International Campus, Instituto Nacional del fútbol (INAF), Neodata Group, AFEDECYL (Asociación de federaciones deportivas de Castilla y León)…

Sports analytics companies:

Driblab, Bepro11, SciSports, Madribble, Humanox, NBN23, Proneosports, Your Fisrt Sport, SiFut, Graphex, Underdata, Métrica Sports,  Bi-sports, Scout Basketball, Basket Solutions, Atenea, Analítica Sports, Big Data Sports, SBG…

Sport clubs

Real Sociedad SAD, Levante UD, Sporting de Lisboa (Portugal), Real Betis Balompié, Middlesbrough FC (Inglaterra), Real Racing Club, Real Oviedo, UD Las Palmas, Sporting de Gijón, Cultural y Deportiva Leonesa, Club Atlético Celaya (México), Real Club Pilar (Argentina)…

Because our faculty is made up of worldclass professionals from all disciplines (Data Scientist, Sports Scientist, Mathematicians, Statisticians, Artificial Intelligence Experts, Physical Trainers, Coaches of professional teams, …), and they work side by side with professionals who work in clubs and top level sports entities, to offer you a totally updated and high quality training.

Because we have a commitment with our students to give them maximum visibility to the work they do and share, both in Networks (Linkedin and Twitter) and in our Blog of Marca.com (Spain) and Record (Portugal), demonstrating everything they learn and are able to do throughout the Master, being our best prescribers.

In this Master you really learn, and this is something you can see first-hand by visiting our Linkedin profiles, for example, where we share what our students make available to the community.

Because our Final Master Projects have a real applicability, and both faculty and students, clubs and sports entities, add synergies so that the result of the Project is 100% usable. In fact, many of today’s successful companies have emerged in our community thanks to these synergies.

Because we have a commitment to entrepreneurship, since, thanks to our philosophy of «Sports Data Campus Family«, we favor the birth of some of the main new companies in the Sports Data Industry during the Master: Atenea (company that provides data services to AFA, in all its Argentine leagues), UnderData, SIFUT, …

Because once you enroll with us your classroom will always be open, even if you have completed the master, so you can keep up with the latest news, we can keep inviting you to new MasterClasses that we will be doing, and you can continue to benefit from all the advantages of belonging to the Sports Data Campus. When you are part of a family, you belong to that family forever 🙂

Because all of this, which may sound expensive is not, thanks to our Scholarship and Student Aid Program, from which you can benefit directly. When requesting information, don’t forget to ask about available scholarships, payment facilities, interest-free financing and all the advantages we have for you.

And, because we are never satisfied, because we are highly competitive and we believe that being the best forces us to take continuous improvement processes as a matter of course. This non-conformism can be seen in our Events (Sports Data Forum), open and free OpenClass, in our training offer, in our Social Media profiles, in our YouTube Channel… but, above all, in the pride of giving the best of ourselves to train the best professionals.

Description

Football cannot be understood without data, since it is the best complement to make decisions. Data providers such as Wyscout, Opta and Instat are imperative so that both scouts and analysts within a football club can start running their analyses. They are the starting point and in this module the focus is on the selection of the appropriate performance indicators according to the objective set and its export. Likewise, the obvious differences between the suppliers will be collected and the internal structure will be shown, as well as all the advanced possibilities that they allow.

Program of the subject

1. Introduction to Data Providers
2. Opta
3. Scout7
4. Instat
5. Wyscout
6. Mediacoach
7. StatsBomb
8. Statistics portals

General objectives 

• • Know the structure, resources and operation of each of the main data providers in the football industry
  • Define the performance indicators to be extracted or exported based on specific pre-set objectives
  • Exploit the possibilities offered by data providers to enhance the subsequent processing of data
  • Focus the data provider to choose according to the needs to be met

     

Competences, aptitudes and skills that the student must acquire

• • Know the potential of each of the data providers in the market, as well as the advantages and disadvantages of them
  • Learn how to manage advanced resources and options from each of the data providers
  • Filter performance indicators and other factors inherent to the macro and micro plane in order to better focus the subsequent analysis
  • Learn to export the data of the different suppliers and to treat the information in the document obtained
  • Know various interesting Open Data statistical portals to complement the studies to be executed

Description

Data analysis is a breakthrough in sport. Many clubs and sports entities already rely on these new methodologies for their day to day. Nowadays the volume of data obtained from training and competition is an opportunity to obtain valuable information from them as long as you know how to translate correctly into an analysis of them.

The objective of this subject is to know how to shape this data to obtain a product that gives information to understand globally the behavior of the player individually or as a team to improve performance.

Program of the subject

1. Historical evolution and contextualization of the use of data in football
2. Offensive metrics: concept and interpretation
3. Defensive metrics concept and interpretation
4. Building and focusing on advanced metrics
5. Goalkeeper evaluation metrics
6. Real case of applying metrics

General objectives

• • Know the origin of the use of data in a systematic way in football.
  • Understand and master the observation of the game and the athlete from the perspective of the data.
  • Filter and model the collected data to extract relevant information for the coach.
  • Identify and understand different individual and collective performance evaluation metrics.
  • Recognize the evolution of this professional activity in parallel to technological evolution.
  • Analyze the individual performance (technical and tactical) of an athlete from the data.
  • Analyze the collective performance of a team based on data.
  • Analyze the performance of the rival from the data.
  • Select relevant metrics to identify potentially interesting players to hire

Competences, aptitudes and skills that the student must acquire

• The student will be able to analyze the data to perform individual, collective and rival analysis.
• The student will be able to write reports on different individual, collective and rival analyses.
• The student will be able to effectively present the information obtained from the analysis.
• The student will know how to select and summarize the information to show individual, collective and rival performance indicators to the technical bodies of football teams.

Description

The adequate support to generate Big Data processes has usually depended on Data Lakes that allow storage and access to data through relational databases or without this condition such as NoSQL.

The relational structures that make up the sentences of SGBDR languages have monopolized the data industry for years. Now on, it is increasingly necessary to manage unstructured information through non-SQL storage that allows useful flows and with adequate speed.

Program of the subject

1. Definition, Manipulation and Control: DDL, DML and DCL.
2. The data value chain: Data Lake, Data Warehouse, Data Intelligence and Data Science
3. DBMS Query Languages: Extract, Transform, Load, and Queries
4. Introduction to Databases Not Only SQL.
5. Targeted Queries vs Artificial Intelligence
6. Database structure and flow NOT just SQL.

General objective

• Expand students’ decision-making ability to choose between DBMS or Not Just SQL
• Differentiate different DBMS data storage options
• Practice with solutions Not just SQL
• Apply the right solutions at database scale to specific sports
• Discriminate relational database engines and Not Just SQL present in the industry
• Generate DBMS structures in SQL
• Establish MongoDB as a Non-SQL environment with practical applications.

Competences, aptitudes and skills that the student must acquire

• Differentiate advantages and disadvantages regarding scale and speed in SQL projects and not only SQL
• Acquire the ability to size in resources and time a demanded project
• Generate basic structures of open projects that allow them to be concretized before specific designs

Description

The R programming language is of the most used for data analysis and processing using Business Intelligence and Data Mining techniques.

R has special features that make it very versatile for handling statistical elements that facilitates the selection, recoding and retrieval of data very quickly. In another sense, R is a very precise and accurate language for statistical data analysis. It has a large number of packages for the creation of graphs for data exposure. As for machine learning, R has implemented a large number of algorithms.

In its link with sport, R is a tool widely used when processing all the information that is produced in sporting events and through its different libraries allows us to obtain all kinds of representations, making the data, a powerful visual tool.

Program of the subject

1. Installation of the software and the main libraries
2. Basic programming with R.
3. Application of the main libraries.
4. Data cleansing
5. Pictograms
6. Case studies solved

General objectives

• • Know the R programming language.
  • Know the different libraries for data analysis and visualization of the same with application to the world of sports.
  • Learn how to perform different types of visualizations through software.
  • Sports and statistical analysis with R.

Competences, aptitudes and skills that the student must acquire

• Know the basic concepts of the R programming language.
• Understand the main concepts for sports data analysis with R.
• Knowledge of algorithms to be able to represent eventing situations through graphs and pictograms.

Description

Python is one of the most widely used languages in the treatment and analysis of sports data. Its ease of integration with the databases learned in module 4 and the visualization tools that will be learned in module 7 is a great advantage. In addition, it is also the most used programming language in the creation of Machine Learning models, so it will be taught later.

 Program of the subject

1. Introduction to Python with the Anaconda environment (Jupyter Notebook)
2. Opening and closing files
3. Basic syntax with Python
4. API Management
5.  Learning from major libraries such as Pandas, MatplotLib and Numpy
6. Structures (If, For, While) and Functions
7. Operations with DataFrames
8. Integration with Database and visualization tools
9. Basic concepts of Scrapping and data cleansing.
10. Practical exercises applied to football.

General objectives

• Train the student in the use of the Python programming language.
• Analysis of sports data and its processing using Python.
• Acquire the basic competences necessary for module 11
• Know the Python programming language.
• Know the different libraries for data analysis in Python
• Know the integration of Python with MySQL.

Competences, aptitudes and skills that the student must acquire

• Critical spirit of data analysis
• Understand the main concepts for sports data analysis with Python.
• Be able to load, transform, analyze a set of sports data, either through a file, using an API or extracting the data by scrapping.

Description

Data analysis is a breakthrough in sport. Many clubs and sports entities already rely on these new methodologies for their day to day. Whether for their own training, getting to know their rivals, avoiding injuries and studying possible signings. To analyze these volumes of data there are tools that allow us to model, represent in a clear and concise way everything that is demanded by the technical bodies.

And it is that the data, without a good visualization, is unprotected and exposed. Just as important as exporting and contextualizing it is knowing how to represent it. To all this, tools are added, which not only help us to visualize, but also has a great transcendence when it comes to being able to analyze and process all the data.

 Program of the subject

1. Introduction to the different tools for data analysis
2. Techniques for perfect visualization
3. Tableau
4. PowerBI
5. Other alternatives

General objectives 

• • Know the different tools for Data Analysis.
  • Learn to model, analyze, understand and represent data using Dashboard, Stories.
  • Learn about the Tableau tool and its possible functionalities. Download the application.
  • Know the Microsoft Power BI Tool and its possible functionalities. Power BI and R. Download the app.
  • Deepen knowledge in other tools that are very useful for creating reports (Keynotes, Power Point, Canvas, …).

Competences, aptitudes and skills that the student must acquire

• Know how to model the data before uploading it to the different Tools.
• Represent with different graphs, tables the studies carried out.
• To be able to determine where the needs of a football team really lie and, in this way, detect the positions to be reinforced in the next market window.
• Fully understand the operation of BI tools and their integration with Python.
• Interpret and publish reports

Description 

The objective part in the analysis of the individual performance of the player is key, but the subjective point of view is still less important. To visually argue the skills, capabilities and technical-tactical qualities of the player, the deep handling of tools such as NacSport, Eric Sports and Metrica Sports becomes indispensable.

The orientation of the individual videos per player is where this module is aimed, since it is crucial that they justify the patterns of the game concluded from the data, both at the macro level and at the micro level. One of the issues that are often forgotten and that must be addressed with meticulousness is the environment in which the player is involved to analyze, since without the understanding of the game model of his team a large amount of information is lost.

Therefore, the situation of the specific team in its competitive environment is another of the pillars that must be studied in the analysis of individual performance.

Program of the subject

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. Construction of the support support for the written report

General objectives

• Expand knowledge in the analysis software that has a greater presence in elite football
• Understand the evolution in individual and collective analysis with the various softwares and their deep applicability
• Access statistical information based on our own analyses through pre-designed templates 

Competences, aptitudes and skills that the student must acquire 

• Be able to use video tools to complement the objective written report
• Have the ability to work with matrices and know how to convert eventing into data through different softwares
• Be able to get datasets in xls. or in csv., according to the convenience of the same, to be able to start working with them
• Optimize the processing of raw to net images

Description 

Player monitoring has become a fundamental part of football. Many clubs use GPS sensors to monitor the external load of their players, producing a large amount of data. What to do with all this data and how to interpret it to help us plan and control training?

In this course we will address and explain the basics of using GPS in football, discuss the physiological aspects and their relationship to the data provided by GPS. They will learn how to determine the demands/requirements of your team’s game and use this to help build and guide training programs.

In this course we will also cover and explain the basics of using tools to monitor the internal load of players, such as, for example, the Subjective Perception of Effort (PSE). After completing this course, you will be able to manipulate, analyze and interpret all the data to improve the performance of your team and gain an advantage over your opponents.

Program of the subject

1. Introduction

     1.2 – GPS technology in sport

2. Theory

     2.1 – Anatomy and energy systems

     2.2 – Internal load vs external load

     2.3 – Use of data for the field

     2.4 – Principles of training

3. Application of GPS in football

     3.1 – Variables and determination of game performance

     3.2 – Using GPS to create a training session

     3.3 – Periodization  

     3.4 – Planning 

General objectives

• Get to know GPS devices
• Learn how GPS devices can help a football team perform.
• Know the benefits of using global positioning systems in football.
• Learn to use global positioning systems in football.
• Understand how to quantify the external training burden in order to make the best planning decisions.

Competences, aptitudes and skills that the student must acquire

• Know how to interpret and analyze the different metrics provided by the Global Positioning System.
• Develop a load quantification protocol.

Description

Analysts and scouts, beyond processing, ordering and selecting the information, also have to structure the way to present it, either in the form of a report or by proposing a talk with the staff / coaching staff. Having at their disposal data from different sources, internal and external, they must be able to collect, order and present that information in a succinct, explicit and clear way so that its impact (message to be transmitted, support for the decision) is as efficient as possible.

This module aims to provide students with basic notions of how to structure and present reports /presentations in a simple, succinct and effective way, depending on their recipient.

Program of the subject

1. Introduction (types of communication, characteristics of the speaker)
2. Reports (report types)
3. Presentations (types, objectives, and presentation planning)
4. Big Data (dice sources, presentation tools)
5. Technological support tools (preparation of presentations/reports, image processing)
6. Practical exercise – Preparation of a report or presentation in the context of football

General objectives

• Know and know how to differentiate the different types of reports and presentations
• Organize and structure the information according to the recipient, for better understanding
• Establish, in a clear way, the objective and content of the report/presentation
• Know how to structure and present the desired information with clarity
• Know and know how to use the different types of technological tools available

Competences, aptitudes and skills that the student must acquire

• Know how to organize a report or presentation
• Orient the presentation and communication according to the final recipient
• Know the technological tools to support the preparation of reports and presentations

Description

According to the giant Google, in 10 years any organization will be dependent on data to decide. A good data analyst is not necessarily the best mathematician, but one who has the ability to understand the origin of problems, ask the right questions and apply the best Machine Learning tools to solve them.

In this module the student will have his first contact with the world of Machine Learning, through which he will be able to understand the whole process from the initial point (the problem) to the existing solutions, being able to apply them and evaluate the result obtained. The module aims to sequence the knowledge acquired in Python Programming Language and is divided into different stages for better understanding on the part of the student.

Program of the subject

1. Introduction to Machine Learning: Process and Existing Models
2. Data Preprocessing – Information Preparation
3. Regression Models: Logistic Regression and Linear Regression
4. Classification Models: KNN (Nearest Neighbours)
5. Grouping Models: KMeans
6. Results Evaluation Metrics

General objectives 

• • Know, know when to apply and apply the different types of machines learning models.
  • Understand the previous needs for execution of each model
  • Learn how to prepare data with data preprocessing techniques
  • Understand to interpret the needs of the problem to better choose the model to apply
  • Know and be able to distinguish Regression, Classification and Grouping.
  • Know how to apply each model and evaluate the results obtained

Competences, aptitudes and skills that the student must acquire

• The student will have a first contact with machine learning, acquiring sensitivity for the subject, being able to understand when, how and where it can be applied in the context of sport.
• Acquire critical ability to evaluate your own models
• With the knowledge acquired, the student will be able to consider what problems their organization suffers that can be solved with recourse to these models, creating a differential value for their club

Description

Throughout this module, the student will carry out the realization, presentation of a master’s Final Project in which, in a guided way, he must apply the knowledge acquired throughout the modules of the master’s degree and demonstrate that he has acquired the competences and skills necessary to work in the field of Big Data Sports environments.

Program of the subject

1. Introduction to the realization of Sports Big Data Projects
2. Essential guidelines for the organization of the project
3. Completion of the master’s Final Project

Throughout the process of study and realization of the final project of Master, the student will be accompanied by a tutor / mentor who will guide him in the process.

General objectives

• Apply the knowledge acquired through the modules studied throughout the Master.
• Select the theme or field of application on which the project is to be carried out.
• Carry out a study prior to the implementation of the project.
• Develop a Big Data project following the mentor’s instructions.
• Make an executive presentation of the project.

Competences, aptitudes and skills that the student must acquire

• Be able to articulate, in a complete way, a Big Data project.
• Execute, efficiently, this project.
• Communicate in a clear and expository way, the work done.