It’s a cliché that American sports fans are obsessed with stats while Europeans, especially soccer fans, are indifferent to them. American fans, the story goes, are numbers addicts, suffering through brief bursts of gameplay for the reward of seeing weaponized laser graphics that show that teams who complete the flying trapdoor play 48 percent of the time win 57 percent of games played at sea level. Europeans, more in tune with the romance and lore of the game, prefer to concentrate on the ineffable poetry of Cristiano Ronaldo’s latest vacation photos.
Those of us who have been battered by ESPN to the point that we now instinctively shrink from percent signs will see in this cliché, for all its obvious problems, an element of truth. (Of those problems, it’s probably enough to point out that several other American sports-culture tropes—the improbable comeback, the underdog story—exist not only alongside our statistical mania but in quiet opposition to it.) It’s been argued, seriously, that all soccer needs to become a major sport in America is a better suite of stats. Give us the hard numbers, and we will give you Peoria.
Well, they’re trying. Last fall, New England Sports Ventures, the sabermetrically inclined owners of the Boston Red Sox, bought the storied English football club Liverpool. They installed as their “director of football strategy” an executive named Damien Comolli, a Frenchman known for using unconventional scouting metrics to discover undervalued talent. Suddenly, the English press flooded with articles about Moneyballand baseball wonks and the “whirring internal cogs” of the computers that, at Liverpool and maybe throughout the game, were about to replace soccer’s age-old human focus with an American-style reign of algorithms.
For the directors of soccer clubs, the allure of a statistical revolution is obvious. As the least stat-heavy of all major sports—FIFA didn’t start counting assists until 1994—soccer has always been something of a mystery even to its most accomplished practitioners. Managers train their teams and install tactics and strategize over squad selection. Then the match comes, and after 80 minutes of fruitless struggle, the goal arrives out of nowhere during a moment entirely unlike the ones the team prepared for. In a game that lives on hunches, any concrete insight, to say nothing of the kind of advanced insight that sabermetrics brought to baseball, can be a major advantage. Refine your understanding of the game by gazing into neglected data, and you can populate your team with underpriced players who excel in categories only your club knows are important. Discover something you’d overlooked about your players’ tendencies, and you can fine-tune your tactics to exploit their best attributes.
Win more by spending less money—it’s enough to turn the erogenous zones of most sports executives fluorescent. It’s no wonder that teams have been scrambling to unearth the secret math that will put them one over on their rivals. Liverpool is only the most prominent (and the most baseball-connected) recent example. Since the arrival of advanced stats-tracking companies like Prozone and Opta in the late ‘90s and early 2000s, clubs like Manchester United and Chelsea have spent hundreds of thousands of dollars trying to gather and analyze all the data that gets kicked up on the pitch. Arsenal manager Arsene Wenger famously receives 60 pages of numbers after every match. In France, Olympique Lyonnais has become a soccer power on the back of a value-maximizing transfer strategy—don’t sign older players, sell any star for the right price—that’s drawn endless Moneyball comparisons. In the United States, Moneyball hero and stats-analysis folk troubadour Billy Beane has himself embarked on the quest for a winning soccer metric, after persuading the Oakland A’s ownership group to buy him the San Jose Earthquakes in 2007.
So the notion of soccer as a kind of quaint, starry-eyed endeavor that can’t be explained by the numbers is a little outdated. There’s just one problem with the sport’s newfound sophistication, which is that soccer happens to be a quaint, starry-eyed endeavor that can’t be explained by the numbers. That kind of statement immediately marks one as a paleo-romantic philistine in Bill James’ America, but the paleo-romantics have a point when it comes to soccer. Because the complexity of the game is so enormous, reasonably thorough stat-tracking requires independent companies and expensive systems, systems whose own complexity introduces a further degree of uncertainty.
Opta’s game trackers, for instance, log more than 350 different kinds of match action. These are not the same as the actions tracked by Prozone, or by the American company Match Analysis, which has worked with Beane and MLS. Before a forward-thinking soccer executive can set about replacing your hunches with precise data, he must first choose from among different, vastly complicated stat-tracking systems, each of which reflects a different philosophy. Just about the only thing to guide him when choosing between these systems is a hunch.
Soccer isn’t baseball, in other words, and even leading researchers in soccer numerology doubt they’ll discover a universally applicable set of metrics—there are simply too many differences between teams and cultures, and too much chaotic complexity within the game itself. Sure, soccer has passes, shots, crosses, free kicks, and so on. But there are also long sequences of play when, say, a defender boots the ball forward, and two players jump to contest it in the air, and it sort of slouches off to one side, and the opposing right back (who’s stuck covering the midfield because his teammates are scattered out of position) gets to it first, and angles what looks like a long pass to the left winger, only the ball swerves in the air and is picked off by the other team’s goalkeeper, who rolls it back to the defender, who boots it forward, and so on. How do you account for that?
Bill Gerrard, a Leeds University professor who has worked with Beane along with several English clubs, told me that the key issue in soccer-stat analysis is “how to weight the different player actions in the overall calculation.” It’s not enough to know how many tackles your central defenders make; you also have to know how well the tackles-made stat helps explain wins and losses. (Maybe not very well: Good defenders position themselves to cut off attacking moves without attempting a tackle in the first place.) If metrical analysis is going to do for soccer what it’s done for baseball, it needs to produce a set of formulas that are transferrable, broadly speaking, across teams and leagues. In baseball, Gerrard says, there’s “pretty universal agreement about what the stats of top players look like compared to journeyman players.” In soccer, beyond the roaringly obvious—scoring a lot of goals is generally a good thing—there’s no such agreement to be found.
And Gerrard told me that he doubts that such agreement will be found. Teams operate under divergent tactical philosophies, player movement is interdependent, and a swarm of unanalyzable factors like “collective motivation” and “unity of vision” contribute something (but how much?) to every outcome. Stats can help soccer clubs, Gerrard says, with metrics designed around the clubs’ specific needs. If Arsenal’s whirring cogs tell them they need a defensive midfielder with ball-retention skills, an Arsenal-specific formula could help them isolate the right player to bid on. But even there, the corporate-contractor nature of the stat-trackers imposes potential distortions. Match Analysis President Mark Brunkhart told me that over the last decade, “a number of companies have very successfully marketed the idea that certain stats that are costly to measure, and for which they just happened to sell six-figure systems, were critically important.” If the stat-tracking companies are engaged in that kind of marketing, how can clubs be sure that the numbers they’re getting are the right ones?
The rise of for-profit sabermetrics marks a big difference between the quest for better soccer stats and the baseball story that helped inspire it. Baseball-focused empiricism started as a grassroots phenomenon, centered on Bill James’ Baseball Abstracts, in the 1970s and 1980s. As Jamesian discoveries began to infiltrate the sport, teams brought on their own analysts, trying to keep at least some of the game’s secrets in-house. Yet today, baseball wonkery remains largely open-source—any lost soul can still browse to Baseball Prospectus and FanGraphs to be enlightened about the difference between VORP and RARP.
Because of the highly fluid, stat-unfriendly nature of soccer, fans have been mostly shut out from its version of the numbers revolution, which has been team- and business-driven from the start. Nobody’s sharing their metrics, because nobody wants to lose the competitive advantage they hope their metrics will confer. Services like the Guardian’s brilliant chalkboards feature let fans access a limited range of Opta data, but the clubs’ formulas for using those data are guarded like Cold War intelligence. In 1999, baseball analyst Voros McCracken changed the game by introducing the concept of defense-independent pitching statistics in a public Usenet newsgroup. Now, he works as an analyst in European soccer—but what he’s doing, or for whom, is a secret.
It’s hard to imagine a revolution in understanding a popular sport that could entirely circumvent that sport’s followers. But that, weirdly, is what the soccer clubs seem to be aiming at: a great, obscurantist leap forward that will enable them to win more matches without anyone outside their own offices knowing precisely why. You can’t really blame the clubs for this; it’s their job to win more matches, after all. But in the meantime, fans are left looking in on a world of hidden complexity, a world in which experts sift through data we can’t see to make decisions we can’t understand. This isn’t exactly the bright beam of American math the media keep anticipating. Instead, it’s as if the more you try to quantify soccer, the more mysterious it gets.
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