It plays an important role in today’s football industry, and will do so even more so in the future. Analyzing data provides a competitive advantage on and off the field, which is why clubs are increasingly looking for a data and statistics specialist to fill a position on their coaching staff. Big data allows for the extraction of information to improve player performance, prevent injuries, increase team efficiency, and even inform decisions when signing or selecting specific players for the starting lineup.
But since contextualizing information and extracting valuable data isn’t always an easy task, we’ve compiled in this master’s degree all the knowledge and formulas a data analyst needs to be part of a coaching staff at a professional club.
Today, club executives are fascinated by the use and capture of data, so they are investing in people who are capable of understanding it, taking all that data, and extracting insights and solutions for their teams. According to several studies, 80% of new jobs at football clubs are for data analysts and big data experts.
That’s why, after completing this online master’s degree, you’ll be ready to take the leap into the professional world. There are currently a huge number of job openings, but very few professionals specialized in this field. In this course, you’ll learn how to use websites and software to extract data, choose between various signing options by analyzing match data, understand your own playing style or that of your opponent using productivity formulas, predict a team’s performance using machine learning, anticipate potential injuries, and understand how important a player is thanks to formulas for calculating direct contributions to a team’s performance.
SYLLABUS – Data Analyst
TOPIC 1. Data Access: Statistical Software and Websites
- 1.1 Software
- 1.2 Tools and Websites for Data Capture
- 1.3 Free Access to Obtain Statistics
TOPIC 2. The Data Analyst and Decision-Making
- 2.1 Decision-Making Theory
- 2.2 Examples of Decision-Making in Football
- 2.3 Decision-Making Methods
- 2.4 Variable Profiling
- 2.5 Comparative Analysis Methods
TOPIC 3. Analysis and Formulas for Obtaining Relevant Data
- 3.1 Analysis of Own and Opposing Teams Using Productivity
- 3.2 Productivity Formulas
- 3.3 Application Examples for Obtaining Relevant Data on Teams
TOPIC 4. Machine Learning and Predictions
- 4.1 Introduction to Predicting Future Data
- 4.2 Injury Prevention and Machine Learning
- 4.3 Performance Prediction and Competitive Forecasting
TOPIC 5. Player Data
- 5.1 Player Performance in Measurable Data
- 5.2 Direct Contributions of an Outfield Player
- 5.3 Direct Contributions of a Goalkeeper