The mention of ‘expected goals’ can start arguments on social media. The phrase ‘there’s only one stat that counts’ is heard semi-frequently on television coverage of the Premier League. Numbers in football are, in their own way, controversial.

Data is an enormous part of the modern game, though. Signings, tactics and even in-game decisions are guided, if not dictated, by data.

The use of data – or lack of – has become a differentiating factor between clubs.

Liverpool, for instance, have embraced data – the FSG ownership group did so with the Boston Red Sox following the Moneyball Oakland Athletics at the start of the 21st century.

The same approach has been used in football – it is the bedrock of Liverpool’s transition from upper-mid-table sleeping giant to the best club team in the world.

Sporting director Michael Edwards is a former analyst at Portsmouth and Tottenham. Edwards is regarded as the mastermind of Liverpool’s squad construction.

He hasn’t done it alone, however, as FSG have led a marked evolution of the staff on Merseyside. Ian Graham leads the research division and an astrophysicist and philosopher are involved in the data science team.

None of this takes away from the accomplishments of Jurgen Klopp and his coaching staff, but Liverpool are as good an example as any of data use in professional football.

They aren’t the only ones, of course, though their resurgence is the most high-profile example of its impact.

Teams and franchises are always looking for a marginal advantage in elite sport. Well-used data, and an extensive team of analysts can put a team more than a nose in front.

The contrast between Liverpool’s use of their resources and some of their supposed peers is stark, and should be alarming for their respective hierarchies.

Signings

Perhaps the most obvious application of data, the way a squad is built goes a long way to determining a team’s ceiling.

Recruitment has been a major part of professional football for decades, but with such riches at the top clubs now, finding an under-priced gem is more valuable than ever.

Matthew Benham, owner of Brentford, is a great example of how data can alter the ceiling of a club. Brentford’s revenue should lock them into League One. Instead, the west London outfit have flirted with promotion to the Premier League in recent seasons.

The Bees identify talent using data and focus on recruiting from undervalued leagues and nations. This brought Benham success at FC Midtjylland, and the same approaches have been used to recruit at Griffin Park.

Brentford are comfortable selling players when they believe they’re getting above market value, and this has helped the club sustain their place in the upper echelons of the Championship.

Neal Maupay and Chris Mepham are just two examples of the players they have made vast profit on. Clubs will use data in player recruitment to different extents.

Liverpool’s current squad was data-led and can be a way of finding players to fit a specific role in a team that others may not consider that player to be an option for. It can identify skills that conventional football statistics do not.

Tactics

Liverpool’s tactics have equally been influenced by data. It’s a natural progression, of course. Getting players at a good value isn’t worth much if they aren’t then used properly.

The concept of ‘pitch control’ has been invaluable to Liverpool and can be seen in many of their patterns of play. Liverpool’s full-back to full-back passes are a trademark of their game under Klopp. Defensively, the influence is clear, too.

Controlling the centre of the pitch is important. The best shots are created from in line with the goal. Liverpool are willing to give their opponents the flanks at times, and squeeze in the middle, forming a compact central block.

There’s no space between the lines for opponents to expose – it forces them to either go backwards or wide.

Pitch control uses tracking data to discover the impact of each action on the probability of a goal being scored. The key is making sure that Liverpool ‘control’ the most dangerous zones on the pitch, stopping their opponents from generating high-value shots.

They are fine with the opposition taking the ball wide on the halfway line. The tracking data identifies the spots on the pitch that red shirts can reach before the opposition.

Whether fans are aware or not, whether it is appreciated or not, data is changing the way football is run, and even the way teams are set up.

Analytics play a major part in baseball and basketball. Football is the same, though it is perhaps still catching up with its American counterparts.

Data is used in Premier League tips, player evaluation and in-game tactics. Before long, data analysts should be regulars on television broadcasts.

The coverage is edging that way (expected goal numbers are shown on Match of the Day and occasionally on Sky).

Betting

Expected goals should be a staple for football bettors. Professional sports bettors will look to the numbers that aid their ability to predict match outcomes.

The top football skills for young footballers are much the same, yet football is changing. The way teams are run and managed are led, to varying degrees, by data science. Betting is the same.

In the way a team would use data to predict the progress of a player of their team’s performance, bettors look to do the same. Expected goals models can give an indication of the sustainability of a team’s form.

Crystal Palace’s struggles under Frank de Boer were a good example of this – their expected goals numbers were solid, and a recovery later in the season was almost inevitable.

Data science underlies the decisions at football clubs. It guides wagers. The impact it is having, and will have, on the top level of professional football cannot be overstated.

*Credit for the main photo belongs to Jon Super / AP Photo*