What if the future of football was also played out off the pitch, in algorithms and statistics?
Data science and football: When algorithms revolutionize the game
STEM and sport: a strategic alliance for performance
Passionate about football and science? Science sometimes intersects with other disciplines in unique and innovative ways. STEM fields—Science, Technology, Engineering, and Mathematics—provide revolutionary tools that transform sports analysis and preparation, offering new perspectives for optimizing performance on the pitch. Today, we’ll explore the captivating world of data science applied to football!
What is data science and why is it crucial in sports?
First, a quick explanation of what data science is:
“[it] is an interdisciplinary field that uses mathematics, statistics, scientific computing, scientific methods, processes, algorithms, and automated computer systems to extract and extrapolate knowledge from large amounts of structured or unstructured raw data.” [1].
It is therefore necessary to be competent in different programming languages such as Python, R, HTML, Oracle and SQL for example, to be able to understand and analyze a large mass of data. From this analysis, the objective is then to produce algorithms and analysis methods to create models and productions useful to the field of application. This can be varied: aeronautics, finance, media, health, tourism, climate…
Data analysis for football: what are the key indicators?
In elite sport, data analysis is particularly crucial for teams and athletes: it allows them to identify trends in games and thus provides the means to develop appropriate strategies to improve performance. Specifically, in football, here are some examples of data that can be used to improve a team’s performance:
- The average speed, distance and space covered by the players during the match;
- The percentage of successful passes and the number of key passes, the distribution of shots, which allows us to know which players are aiming at which parts of the goal;
- Analysis of the opponent’s game: how they pass the ball, how they organize their attacks…
Thus, the intelligent exploitation of this data makes it possible to refine tactical strategies and significantly improve performance on the field, making the data scientist an essential partner for the club to guide players on the field!
Sarah Rudd, pioneer of data science applied to football
One woman who was particularly influential and pioneering in the development of data science applied to football was Sarah Rudd. A graduate of Columbia University with a Master’s degree in Computer Science, she began her career at Microsoft as a software engineer. Her desire to work in football led her to independently explore the links between data and sports performance, at a time when this intersection was still in its infancy. She created a blog to share her analyses on the subject, and then the ideal opportunity arose: StatDNA, a company that sells analytics services to sports teams, held an open competition, making its database available to develop new studies. There, Rudd developed a model for evaluating player actions, such as passing and shooting, impressing StatDNA, which offered her a position in 2011. The company was acquired by Arsenal in 2012, and Rudd subsequently spent nine seasons with the team as director of analytics and software development.
In 2022, she created SRC to sell its services to football teams that were beginning to integrate data analysis into their systems.
Sarah Rudd has pioneered a revolution in football: today, data no longer just analyzes the game, it shapes it. With the development of artificial intelligence and machine learning, there are still enormous possibilities to explore in this field! How far can data science take sport?
To learn more, you can watch this video where Sarah Rudd discusses football analysis at Arsenal:
Written by Nastia and edited by Intan
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