As a kid, one of my favorite movies was Moneyball. For those who have never seen it, the movie tells the story of Billie Bean, the General Manager of the Oakland Athletics baseball team and how he used advanced analytics (known as sabermetrics) to find the most talented players at the cheapest price. Baseball teams from small markets like Oakland have difficulty affording top-notch players who are expensive. Thus, signing players undervalued by the market remains critical to building a winning team. Bean’s focus on sabermetrics pioneered an analytics revolution in baseball that has changed the way executives evaluate talent.
I have been curious about the extent to which analytics play a role in soccer and what—if any—impact there is on player recruitment. In baseball, the emergence of sabermetrics has generated a greater focus on statistics such as home runs and strikeouts and a reduced emphasis on stolen bases and bunts. In basketball, the rise of analytics has encouraged players to seek more three-pointers from the corner and close-range shots, as opposed to mid-range jumpers.
There are possible benefits for executives of elite soccer clubs to adopt advanced analytics. In a sport with so many leagues, translating stats across varying levels of competition could be useful. Yet, this appears to explain one of the key challenges of adopting analytics since adjusting for differences in competition could be a potential difficulty. Advanced analytics might disproportionately benefit strikers, whose stats are already easiest to quantify. And given that comparatively few statistics are used in soccer, an all-encompassing metric such as Wins Above Replacement (WAR) could be difficult to compute.
Despite some of these challenges, there is an interesting history of soccer analytics. Charles Reep, a wing commander in the British Air Force during World War II formulated the idea of the long game. According to Reep’s analysis, a team’s probability of scoring decreased with each additional pass and most goals were scored after three or fewer passes. Thus, short passes were inefficient and it was preferable to launch lengthy downfield passes. However, subsequent analysis has proven Reep wrong. While he focused on the percentage of goals after various passing sequences, he should have focused on the percentage of possessions that resulted in goals.
At the team level, there have been important analytical findings. As a counter to Reep, more recent findings have argued that short and quick passes through the middle of the field are more effective over long clearing kicks and crosses. This has led teams to prioritize shorter passes and longer possessions, most prominently Barcelona through its “Tiki-taka” style.
In recent years, there has been a proliferation of firms offering advanced analytics to clubs. 21st Club—a British company that provides data to European clubs—is one of the more prominent examples. In 2018, 21st Club recommended to Red Star Belgrade—a team in the little-watched Cypriot top division—that it acquire Lorenzo Ebicilio, an attacking midfielder who was not on the club’s radar. Belgrade signed him and he played a critical role in leading the team to the group stage of the European Champions League, including by scoring a goal in group play. 21st Club created Predictive Intelligence Research and Learning Outputs (PIRLO), which seeks to standardize stats like goals, assists, and tackles across leagues. Yet, as some of the company’s analysts have noted, there are preexisting biases among executives that can distort recruitment. For instance, there is often a 15 percent transfer fee for a player who has scored in a World Cup. Similarly, English Premier League executives tend to look favorably on players with Premier League experience, even when there is no statistical evidence that such players perform better on average.
Limited use of analytics has even made its way to the MLS. Early analytic statistics such as a player’s passing percentage or shooting efficiency have evolved to account for how effective players are away from the ball. D.C. United (my hometown team) used advanced metrics to acquire foreign-born players such as Junior Moreno of Venezuela and Ulises Segura of Costa Rica. Some analytics have also focused on how coaches can make better decisions. Bret Myers, an aspiring MLS statistician, published a study arguing that the best times for a trailing team to make a substitution change are before the 58th, 73rd, and 79th minutes. However, there has also been pushback. Bruce Arena, the former head coach of the U.S. Men’s National Team has argued that “analytics in soccer don’t mean a whole lot. Analytics and statistics are used for people who don’t know how to analyze the game. This isn’t baseball or football or basketball. We have a very important analytic and that’s the score. That distorts all the other statistics.”
Soccer analytics have progressed over the past few years, yet it remains to be seen whether they can reach the importance that advanced stats and sabermetrics have attained in other sports. While the growing reliance on short passes over longer ones shows how analytics have helped teams adjust, there is less evidence—with notable exceptions—that they have fundamentally changed the way executives assess talent. It is also an open question as to whether such statistical advances are good for the game. As the video assistant referee (VAR) has shown, technology can corrupt the joy of watching the Beautiful Game. It is possible that the same can be true of using numbers to quantify a sport that might be difficult to quantify. Regardless, it will be interesting to see whether the use of analytics at both the team and individual level progress over the next few years.
Sources
Goff, Steven. “The beautiful game discovers that algorithms can be beautiful, too.” The Washington Post, March 1, 2018. https://www.washingtonpost.com/news/soccer-insider/wp/2018/03/01/the-beautiful-game-discovers-that-algorithms-can-be-beautiful-too/.
Kidd, Robert. “Soccer’s Moneyball Moment: How Enhanced Analytics are Changing the Game.” Forbes, November 19, 2018. https://www.forbes.com/sites/robertkidd/2018/11/19/soccers-moneyball-moment-how-enhanced-analytics-are-changing-the-game/#633a910a76b2.
Myers, Bret. “A Proposed Decision Rule for the Timing of Soccer Substitutions.” Journal of Quantitative Analysis in Sports 8 no. 1 (December 2012). https://cafefutebol.files.wordpress.com/2013/12/substitution_timing.pdf.
Paine, Neil. “What Analytics Can Teach us About the Beautiful Game.” FiveThirtyEight, June 12, 2014. https://fivethirtyeight.com/features/what-analytics-can-teach-us-about-the-beautiful-game/.
Wolf, Nathaniel. “Waiting for the Revolution At The StatsBomb Analytics Bootcampt.” Deadspin, July 10, 2019. https://deadspin.com/waiting-for-the-revolution-at-soccer-analytics-bootcamp-1836224038.
This post made me consider the economic viability of transfer fees, which recently reached an all-time high of 222 million euros for the transfer of Neymar from Barcelona to Paris Saint-Germain. I predict that soccer will follow baseball and basketball and adopt analytics increasingly, which could lead to teams paying more frequent, smaller fees for underrated players, as depicted in Moneyball. Perhaps teams will also decrease the transfer fees they pay for “star players” and instead invest their money in analytics, which would ultimately change the market of speculation on players’ talent.