In 2015, Liverpool Football Club hired Jürgen Klopp as their new manager. It was a decision that was met with scepticism by many pundits. During Klopp’s last season at Borussia Dortmund the side had dropped to the bottom of the Bundesliga.
But Liverpool had seen something that the pundits had missed. Dr Ian Graham, the club’s Director of Research, had analysed ten years of German league data and found that Klopp’s final season at Dortmund was the second-unluckiest in Bundesliga history. Analysing the team’s performance by going beyond numbers of goals to look at chances created, expected goals, and pitch control put a completely different light on their lowly position in the league. Their results were a statistical anomaly driven by variance. Liverpool hired Klopp because the underlying data showed his process was still among the best in the world.
This distinction between outcomes and process is a subtle but often missed lesson, and one of the reasons why Liverpool FC is one of my favourite case studies about data. When Fenway Sports Group (FSG) bought the club in 2010, the club hadn’t won a league title in twenty years and they were competing against clubs with far larger transfer budgets. FSG’s owners had already seen the power of data when their Boston Red Sox team implemented and scaled principles learned from Billy Beane’s escapades with the Oakland Athletics (as immortalised in the 2003 film ‘Moneyball’) to use data to break an 86-year championship drought.
In 2012, they hired a Cambridge PhD in theoretical physics as Liverpool’s first Director of Research. Dr Ian Graham built what became the Premier League’s first in-house analytics department – a six-person team of physicists, astrophysicists, and software engineers. Their job was to support more traditional football expertise by giving decision-makers better information.
And it really worked. During Graham’s tenure (2012-2023), Liverpool won the Champions League, the Premier League (their first title in 30 years), the UEFA Super Cup, and the FIFA Club World Cup. As Graham himself notes in his book ‘How to Win the Premier League’, data was one of many contributory factors to this success, but there are some fascinating insights from the book on some of the (more widely applicable) techniques that they used to gain such advantage.
The single currency
One of Graham’s central insights was that football is a low-scoring but high-noise sport. Since goals are relatively rare judging players solely on whether they directly produced a goal is statistically unreliable. What the team needed instead was a way to value every action on the pitch, and not just the ones that end up on a scoresheet.
So Graham’s team developed a ‘Possession Value Model’ which assigned a value to every touch of the ball based on how much it increased or decreased the team’s probability of scoring. If a player passed from inside their own half to the edge of the opposition box for example, this might shift goal probability from 0.4% to 1.7%. This may be a tiny shift when looked at in isolation but aggregated across thousands of actions it revealed which players were genuinely improving the probability of winning.
This single currency for value allowed Liverpool’s scouts, analysts, and coaches to evaluate every player using the same framework. A player with a low pass-completion rate might actually be far more valuable than a passer that was considered safe because the passes they completed were high-risk actions that moved the ball into dangerous areas. Conventional statistics would miss this entirely.
Data as a filter
It’s believed that there are roughly 5,000 players in Europe who could theoretically play at Premier League level. No scouting department can watch them all, so Graham’s team used data to filter those 5,000 down to around 50 candidates who matched Liverpool’s specific profile: high pressing intensity, strong possession value scores, and the physicality suited to the demanding system that Klopp had developed. The scouts would then go to work, assessing character, mentality, and tactical fit.
When the data and the scouts disagreed, rather than this divergence being treated as a problem to be resolved, it was used as an investigative tool. What was the quantitative assessment seeing that the qualitative assessment was missing, and vice versa? The tension between the two perspectives was productive, not adversarial.
Finding value where others weren’t looking
This filtering approach gave Liverpool a structural advantage in the transfer market. Rather than competing with wealthier clubs for the same obvious targets, they could identify players whose value was systematically underestimated by conventional scouting.
Andy Robertson was signed from relegated Hull City for an initial fee of around £8 million when no top-six rival was interested. The data saw what the market didn’t, a young full-back whose attacking output and pressing numbers were elite, masked by the poor team around him. Mohamed Salah was acquired from Roma for a reported £36.5 million after the data model flagged his off-the-ball movement and expected goal output. He then went on to break the Premier League single-season scoring record in his first year. The front three of Salah, Mane, and Firmino were assembled for a combined total of around £100 million and became one of the most celebrated attacking lines in modern football.
The benefits weren’t restricted to buying well. Liverpool sold Coutinho to Barcelona for £142 million, informed by an understanding of when a player’s market value had peaked relative to their likely future contribution. That single transaction funded the signings of Virgil van Dijk and Alisson Becker, two players who transformed the team’s defensive foundation. Data didn’t just help Liverpool find the right players at the right time. It helped them understand when to sell, and at what price.
The culture problem
In his book Graham talks about how building the models was the easy part but building trust and a culture around data was much harder. There was a graphic example of this during Brendan Rodgers’ tenure as Liverpool manager, during which time the analytics team’s recommendations were frequently overruled. Against the advice of Graham, Rodgers insisted on signing Christian Benteke for £32.5 million, a forward whose game was built around aerial prowess and holding the ball up. This was precisely the opposite of what Graham’s possession value model said Liverpool needed, which was mobile forwards who pressed aggressively and created chances through movement. Benteke scored just ten goals, lasted one season at the club, and was sold to Crystal Palace at a significant loss.
Ultimately there were three conditions that eventually made it work. FSG were already believers in the power of data from their Red Sox experience, and gave the data team the political cover they needed to fail and iterate. Sporting Director Michael Edwards understood both the data and the football, and so was able to act as kind of translator between analytics and coaching. And Klopp understood the value of having data feeding the decisions that were being made. Graham’s team cleverly used visualisations and video clips rather than complex models and demonstrations to bring to life their recommendations.
Liverpool’s success during this time was not down to data alone, but the context of how that data could be applied, and the alignment that existed between owners, sporting director, analytics, and manager. Put simply, data enabled everyone to do their job much better, and that’s the way it should be in every organisation.
Applying these principles
Sporting case studies are always so interesting but there are some very useful principles here that apply well beyond football. Firstly, separating process from outcome. The decision to make Klopp the manager is a wonderful example of seeing what others don’t see, simply because you are looking deeper. The risk of only focusing on results is that you punish good process and reinforce bad habits.
Yes, aligning around one shared measure of value can prevent fragmented decision-making and create a common language across functions. But what Graham’s possession value model also did was to demonstrate how leading indicators (like every player’s actions on the pitch) can be tied more directly to lagging indicators (like goals). The masters of this (perhaps unsurprisingly) are Amazon, who have a robust process for identifying and controlling the metrics that actually do drive outcomes.
I also like this as an example of using data to focus human attention rather then replacing human judgement. There’s a big lesson in this for the application of AI of course. Use data and automation to handle the search, so that your most valuable human assets can focus on the selection. Rather than replacing human judgement, technology can help focus it.
In some ways this is also an example of so-called ‘Blue Ocean Strategy’ which is usually used in the context of pursuing new and uncontested market space and creating new demand, rather than competing in saturated industries or fighting over existing customers. Unconventional metrics can reveal opportunities that competitors using traditional measures are systematically missing. Liverpool were willing to look beyond where everyone else was looking, using data to find world-class talent in unfashionable places.
And underpinning all of this is culture. Building trust, creating a translation layer between data and decision-makers, securing the leadership alignment that allows insights to actually influence what happens next. These are the true hard yards of any data or AI transformation. The models were the easy part. Liverpool’s real edge was that they built an organisation where better information could actually change behaviour. That’s the bit most organisations still haven’t cracked.
Sources: Dr Ian Graham, How to Win the Premier League, Dr Ian Graham, ‘The Data Science of Football’, StatsBomb Conference.
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