As the 2011 film Moneyball draws to a close, Oakland Athletics – a Major League Baseball team – have come up short in the 2002 American League Division Series. The protagonists’ defeat at this late stage is bittersweet: they’ve lost the championship; but as a team on a shoestring budget, making it as far as they have is widely acknowledged to be a remarkable achievement. Professional baseball was, in the early 2000s, a game dominated by spending power, something the Athletics decidedly lacked during the era recounted in Moneyball. Even so, thanks to an innovative data-based approach to the selection of players, this underfunded team rose up to challenge competitors at the highest ranks.

Liverpool fans cheering. Source: Unsplash.

​    Moneyball is based on a true story. It’s unsurprising that the Oakland Athletics, an underdog team that unexpectedly found a way to compete against the sporting heavyweights, would be chosen as subjects for the silver screen. The Athletics’ dramatic triumph against the odds doesn’t just make for entertaining cinema, however. Their real-life accomplishments set the stage for sweeping changes not only to professional baseball in the US, but to sport generally worldwide, and, in particular, to football here in the UK.
 
    In the last few years, the ground-breaking, data-based methods famously exploited by the Oakland Athletics have been adopted by Liverpool Football Club, leading to similarly impressive results for the team. Echoing the Athletics’ rise through the ranks of baseball in the US, Liverpool’s recent series of successes is a gripping demonstration of how this new way of playing the game has the potential to revolutionise English football, a fact that pundits and reporters throughout the UK are coming to recognise.

The story begins with baseball

    To understand how Liverpool came to utilise these methods so effectively, we need to go back two decades and study US baseball at the turn of the century.

    In the United States, data on baseball players have always been collected and disseminated in abundance, but decisions regarding which athletes to sign to which teams rested almost entirely on the scouts’ impressionistic assessments of candidates, players whom they observed in action. Under Billy Beane’s leadership from 1997, however, Oakland Athletics’ management broke from this established pattern of recruitment, opting instead to rely primarily on data for deciding whom to hire. Instead of following the scouts’ gut instincts, the Athletics fed data into predictive models designed to assess the potential of various players’ skillsets when those players were deployed in combination, using the optimum modelled results within their price range to create an effective and affordable line-up. In this way, the team was able to consistently discover undervalued players overlooked by teams with bigger budgets, and use these athletes to build a competitive force in baseball.

    As detailed in Michael Lewis’ (2003) book Moneyball: The Art of Winning an Unfair Game, on which the film is based, one of the first people to recognise the brilliance of the Athletics’ data-based approach was John W. Henry, owner of the Boston Red Sox. He soon adopted the Athletics’ methods to source players for his own team. These new tactics coupled with the Red Sox’s more ample financial resources saw them go on to win the World Series, baseball’s ultimate triumph. In turn, this victory invited greater national scrutiny of the data-based approach to player selection.

    Soon, the impact of Beane, Henry, and other baseball managers breaking with tradition would spread to other major sports played in the US and worldwide. Once this new approach to building competitive teams made it across the Pacific, English football, in particular, would never be the same again.
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Data comes to English football

    In October 2010, Henry’s sports firm, Fenway Sports Group (FSG), acquired ownership of Liverpool Football Club. As evident from opinion pieces published at the time (cf. Thompson, 2016; Herbert, 2020), for fans of the club this was a welcome takeover, releasing the players from mismanagement by previous owners. Liverpool, once the most decorated club in England, had descended into mediocracy, long since overshadowed by wealthier teams when FSG arrived on the scene. With the club in ideological and financial disarray, Henry truly had his work cut out for him to repair the team.

    Throwing cash at the problem was not going to be enough to restore Liverpool to its former glory, however. While FSG had money to invest, their funds weren’t anywhere near the amounts available in clubs owned by oligarchs and sheikhs. Henry and his partners could not outspend their rivals; so their only option was to outsmart them.

    By this point, the data-driven approach to team construction had found popularity among professional baseball, basketball, and other major sports teams in the US. ‘Sabermetrics,’ a term coined by writer Bill James, the original inventor of these methods (Lewis, 2003), had entered common use in baseball commentaries specifically. His work became acknowledged as a foundational tool for managers running baseball teams across the United States (McGrath, 2003).

    Meanwhile, the world of English football had yet to accept that data and statistics could explain the game. Football was considered too complex to be reduced to mere numbers, and the instincts of seasoned professionals held greater value over statistical data, sometimes to the total disregard of the latter. Long-standing club manager Sam Allardyce scoffed at the idea that English football could ever be governed by statistical models, opining that unlike ‘baseball or American football’, his sport of preference was ‘too unpredictable’ to allow for ‘decisions [based] on stats’ (Schoenfeld, 2019). Until very recently, this stance was also illustrated by pundit and former midfielder Craig Burley’s dismissive retort. When asked about predictive models, Burley responded with ‘expect[s] things at Christmas from Santa Claus, but they don’t come’, and the very idea of using data to predict football match outcomes is, according to him, ‘an absolute load of nonsense’ (Schoenfeld, 2019).
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     In spite of widespread mockery, Henry believed English football in 2010 was at the same stage of development that US baseball had been in 2002, and he saw no reason to assume that the sabermetrical methods would be wielded to null effect in the former. The Red Sox’ success story was sufficient evidence for the view that Moneyball-type tactics could be applied in Liverpool’s favour. Naysayers on all  sides and a rocky start to FSG club management notwithstanding, events would eventually conspire to demonstrate that Henry was absolutely right on the money.
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John W. Henry
Source: Wikimedia Commons.

    It took time to find a manager who agreed with Henry’s ideas and was capable of effectively implementing the strategies in Liverpool’s favour. The club cycled through various managers unsupportive of the data-driven approach; results on the field did not initially reflect any improvement relative to Liverpool’s pre-FSG era. However, in 2015, the right man for the job, Jürgen Klopp, turned up, and the FSG jumped at the opportunity to bring him on board.

    As someone who had always supported the use of technology to explain football (Athletic Interest, 2020), Klopp instantly clicked with Liverpool’s owners and their views on data utilisation, and immediately put data-based strategies into play. The difference which resulted, Liverpool’s overall performance improved, culminating in the club’s victory against Tottenham Hotspur in the 2019 Champions League Final. At last, Henry’s long-held convictions and efforts had begun to pay off.
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The stats behind the manager

    Bruce Schoenfeld’s (2019) New York Times Magazine article ‘How Data (and Some Breathtaking Soccer) Brought Liverpool to the Cusp of Glory’, one of the first pieces of sports reporting that acknowledged the successes of data-driven approaches to football, offers important insight into the strategies behind Liverpool’s climb to victory in recent years. What’s especially illuminating in this article is the detail provided on how decisions essential to the club’s success were underpinned by the advice of ostensible sport ‘outsiders’: data analysts with more expertise in science than in football. While Schoenfeld acknowledges that other teams like Tottenham Hotspur and Arsenal also hired data analysts to provide consulting advice, he highlights Liverpool’s decisions, under Henry’s leadership, as particularly noteworthy, not only in bringing a whole panel on board, but also in giving that panel unprecedented influence over the club’s dealings.

Jürgen Klopp at the Liverpool vs. Chelsea game, 2019 UEFA Super Cup.
Source: Wikimedia Commons.
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    Many have argued that the managerial appointment of Jurgen Klopp was the key to Liverpool’s recent success (cf. BT Sport, 2019; Ripley, 2020; Sky Sports Football, 2020). Yet, it may surprise readers to know that this appointment was not owed to Klopp’s reputation, but rested on match statistics modelled by Liverpool’s data team.

    Klopp did not enjoy a privileged route through the ranks of football management. Having worked his way up from smaller clubs, he was used to competing against richer teams and maximising the use of resources that were available to him. His CV displays many impressive results overall apart from one abysmal entry: his final season at Dortmund, a club which had challenged top competitors in previous years but which, to the shock of many, took last place in the German Bundesliga, halfway through Klopp’s last year of managing the team (Clydey2Times, 2019). Even after finishing the season with a partial revival, Klopp’s reputation took a big hit, and people began to doubt his ability as a manager (Holden, 2015).
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    At Liverpool, however, as Schoenfeld (2019) explains, the data team modelled the stats of Klopp’s Dortmund run as a whole, revealing his fiasco of a final season to be an anomaly. Where other commentators saw failure on Klopp’s part, the data team identified a series of outlier matches doomed by freakishly bad luck. General opinion now saw Klopp as an undesirable hire, but Liverpool was able to see through the unfortunate circumstances that had plagued his final season with Dortmund and correctly assess him as a competent manager with enormous potential. They did not hesitate to bring him on board.
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The stats behind the players

    Getting the right manager on board was only the beginning. Choosing the correct players for the job was just as essential, and Liverpool successfully applied data analysis to this task as well.

    In the summer of 2017, Liverpool made one of their major signings in Mohamed Salah from Roma. It’s conventional wisdom in football that a manager should be given free rein to buy the players they personally want (cf. Lusby, 2015; Jackson, 2019; Watson, 2021), but this is not the route that Liverpool took. Salah was not handpicked by Klopp, who had his sights set instead on the up-and-coming German player Julian Brandt – considered to be a superior option by many football observers (cf. Tanner, 2019). In fact, it was the FSG data team who determined that Salah should be brought on board, despite his seemingly lacklustre career prior to 2017.

    Klopp decided to trust the Liverpool analysts’ assessment and agreed to sign Salah for his potential indicated by the statistical models. Predictably, the media did not understand the move; word on the streets claimed Liverpool had lost the plot (cf. Marland, 2020). However, the doubters were quickly silenced. Salah soon broke the record for the most goals scored in a Premier League season. He’s now regarded as one of the best players in the world (Nalton, 2021), while the career of the player Klopp had initially hoped to hire, Julian Brandt, has so far proved underwhelming by comparison.

    Schoenfeld’s (2019) article suggests that Liverpool’s data-based choice of players would be better described as ‘buying goals’ than ‘buying players’. The data team that pushed for Salah’s hiring not only looked at player’s personal list of notable achievements in recorded games, of goals and assists, but analysed every action – pass, tackle, or shot – that a player had taken which contributed in some way to the scoring of a goal. This approach to the data, applied to all player statistics, allowed Liverpool’s analysts to calculate the potential contribution of a new recruit towards the scoring of goals, even in cases where said recruit might not personally kick the ball into the net.

    A character in Moneyball tells Billy Beane, ‘Your goal shouldn’t be to buy players. Your goal should be to buy wins, and in order to buy wins, you need to buy runs.’ Swapping out ‘runs’ for ‘goals’, the success Liverpool has enjoyed under a management willing to listen to data analysts on this point shows that the statement holds as true for football as it does for Major League baseball.
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Conclusions

    In 2020, Liverpool Football Club were crowned the Premier League champions. This victory did not quite top the club’s 2019 Champions League triumph, but was nonetheless hugely meaningful to fans who had been waiting 30 years for Liverpool to retake the Premier League trophy. As was the case when the Red Sox won the World Series in 2004 after 86 years, Liverpool’s win seemed to validate John Henry’s unconventional approach to data analysis in the face of popular scepticism.
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    Liverpool’s data-paved path to victory has not been without its bumps. Schoenfeld’s (2019) article takes care to note that the results of statistical analysis are only as reliable as the data on which they are based, and the algorithms designed and applied by analysts are prone to human fallibility. Until Klopp, managers hostile to the idea of using Moneyball-type tactics undermined the benefits that statistical modelling could bring to a club like Liverpool. Admittedly, not all player signings made on the basis of statistical analysis have turned out as well as the case of Salah (Gates, 2021). But on the whole, the use of statistical data modelling to determine the best choice of manager, players, and gameplay tactics has, to all appearances, worked out very well for Liverpool. With other clubs remarking on Liverpool’s success, it is doubtful that football will ever return to the game it was before. A new era in football is on the horizon, and this era has begun with Liverpool.
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References

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