Twenty years ago, football, as well as many other sports, was heavily based on expert opinions. The decision-making process, from which players to hire, which ones should be in the starting eleven to match specific tactics, would be based on the combination of experience and gut-feeling of relevant stakeholders.
In the turn of the millennium, it would be very unlikely to encounter a head coach of a professional football club who would be willing to take advice from a data analyst without any football-related background. Nevertheless, being a big business above all, parties in the football world are always searching for ways to improve. Less than two decades later, in December 2017, after Liverpool FC`s player Phillipe Coutinho scored a free-kick goal, Jurgen Klopp, of one the most successful football coaches of his generation, credited the goal to his team of data analysts (Evans, n.d.). This gesture was a clear sign that football has moved from a purely emotional sport to an increasing data-driven one. Clubs such as FC Barcelona, Manchester City, AC Milan and Arsenal are currently already utilizing data at the core of every decision. Through the use of sensors and wearables, clubs are able to run extensive analytics on the performance of their players. Every move a player makes on the field is captured, stored and analyzed. By leveraging the vast amount of generated data, clubs not only improve their players’ performance but can also use predictive analytics to mitigate injuries and evaluate different team tactics. (Evans, n.d.). Besides in-field player management, data analytics has flourished across multiple fields in the football industry. Nowadays, having the right information strategy can be the tipping factor between a successful or failed season. Babb (2020) highlights how data-driven decisions were the crucial pillar of Liverpool’s FC stellar seasons between 2018 and 2020. By leveraging a highly skilled team of data analysts, the football club has become a global reference for both in-field player management analytics as well as data-driven player recruitment.
Football will always remain a passion sport, the emotion of the fans singing from the stands to support their clubs will always remain a critical factor. Nevertheless, it is irrefutable that professional football clubs will face the increasing necessity to adopt data analytics to every process within their organizations. The rise of data analytics in football has just recently started. However, it is already undeniable the impact it has caused. Drawing from other industries, it is plausible to say that the data-driven revolution for football clubs has just begun.
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The use of data analytics in sports was perhaps first popularized by the Oakland Athletics baseball teams, who from the early 1990s began using “sabermetrics” to identify undervalued baseball players. Despite having a far smaller budget than the larger MLB teams, the A’s nevertheless broke the American League record for consecutive wins, and sabermetrics is often attributed as one of the reasons for their 20 game win streak. The A’s success inspired most other MLB teams to incorporate data analytics and sabermetrics into their front office decision making. While sabermetrics was therefore a disruptive technology when it was first used by the A’s, it has now become mainstream and much of the competitive advantage that the A’s derived from it has perhaps been reduced (as everyone now uses the same or similar metrics to value players). Much the same, no doubt, will happen with professional football. Given that, what do you think is the long term value of data analytics in professional sport, at least insofar as drafting or signing players is concerned?
Interesting post on the use of data analytics in football Alexandre! Of course, most teams use data analytics for multiple parts of their organisation. Midtjylland in Denmark is taking it a step further. They are a live experiment of how data analytics can run a football clubs. They are the proof that football is going through a revolution. They buy all there players and coaches based on data. Since implementing this approach, they won the league several times, after not winning it at all in the large history before it. This concludes the success of data analytics in football.
Hi Alexandre,
Thank you for this interesting blog post on data analytics of football. I fully agree with you, data analytics in the sports industry becomes a big deal. Not only on the entertainment side but also on the field. I found this really interesting article that goes into depth about different data analytical analyses and tools that are used at FC Barcelona. An interesting topic that was discussed is “ghosting”. Ghosting predicts the most likely move of players in the field (Burn-Murdoch, 2018). Using this technique data scientists can predict how likely certain strategies will work against an opponent. For example, how will an opponent defend if the team performs certain attacking strategies (Le at al., 2017).
Some clubs take it one step further and are using data science in key operations, such as scouting. The Danish club Midtjylland uses a team of data scientists for the search of new players instead of traditional scout (Ingle, 2015).
References
Burn-Murdoch, J., 2018. How data analysis helps football clubs make better signings. [online] Available at: [Accessed 4 October 2020].
Ingle, S., 2015. How Midtjylland took the analytical route towards the Champions League. [online] Available at: [Accessed 4 October 2020].
Le, H., Carr, P., Yue, Y. & Lucey, P., 2017. Data-Driven Ghosting using Deep Imitation Learning. MIT Sloan Sports Analytics Conference
Hallo Alexandre, interesting topic you present, I had no idea that data played such an important part on the football pitch already. Before I read your article, I had heard that some football clubs started using the Moneyball principle/statistics for recruiting in baseball. I wonder whether you could share more of your thoughts on recruiting specifically, do you think this happens on a big scale already? If not, do you believe that data will takeover the recruitment process for every team in the near future?
Hi Alexandre, thank you for your interesting post! I similarly chose to focus on the the subject of data analytics and its application to football, which I think is a super interesting topic. Liverpool’s success since 2018 is in large-part thanks to data analytics indeed, and I am super curious to how their approach and decision making processes have changed as a result. With regards to your example of Coutinho’s freekick, it would be great to know how data influenced Klopp’s decision at that phase of the match. Was he the one to make the decision to let Coutinho take the freekick and were factors such as his previous free kick conversion rates and the positioning of the free kick taken into consideration? These would be some really could metrics to know, as I assume there are so many different factors which weigh into this decision.
Furthermore, its super interesting how you chose to focus on some of the in-game decisions and how those are influenced by predictive/data analytics. I, on the other hand, focused more on the application of data to decision making from the club’s staff perspective. For example, recruitment decisions can be made based off of algorithms which analyze the amount of space a player creates. Messi, as one of the best, creates the most space on the pitch per match compared to any other player, while walking. On the other hand, he creates the least amount of space on the pitch per match while tracking his movement at running-pace.
It would be (I think for the both of us) really enticing to learn more about this topic and its application to specific scenarios in the football/sports industry!