How Premier League Teams Will Use Stats This Season

The Premier League has been the world leader in football analytics for a decade. But what started as a competitive advantage for a handful of forward-thinking clubs has become standard infrastructure across the top flight. Every club now has a data team. Every manager has access to xG dashboards and pressing heat maps. The question for 2026/27 isn't who uses data — it's who uses it best.

Here's how the biggest clubs are likely to deploy their analytics departments this season, what metrics they'll be prioritising, and what it means for how you watch the game.

Liverpool: The Pressing System, Quantified

Liverpool were one of the earliest Premier League clubs to build their entire tactical identity around a measurable metric: PPDA (passes per defensive action — the number of passes an opponent is allowed to complete before a defensive action is triggered). Lower PPDA = more intense press. Under Klopp, Liverpool ran PPDA numbers that were among the best in Europe, and the club's data team have retained that focus through the management transition.

For 2026/27, the key question for Liverpool's analytics department isn't whether to press — that's settled — but how to manage press intensity across a 38-game season. Research in sports science increasingly shows that high-PPDA systems accumulate more muscular load and carry elevated soft-tissue injury risk. Liverpool's data team will be running fatigue models alongside tactical ones, managing when to press at full intensity and when to sit deeper to protect players across a congested calendar. Watch Liverpool closely when they play their third game in seven days — the PPDA numbers will drop measurably, and it will be intentional.

Arsenal: The Set-Piece Lab

Arsenal's set-piece revolution has been one of the most analysed tactical stories in English football over the past two seasons, and it was accelerated by what clubs observed at the tournament this summer. Set pieces deliver disproportionate xG returns relative to the time spent on the ball. They are the highest ROI area in football analytics, and Arsenal have built a dedicated coaching unit around them.

The metrics Arsenal track go far beyond the basic "how many corners did we win." They model opponent defensive shape, aerial duel win rates across different zones, the movement patterns of their own attackers in relation to opponent zonal or man-marking systems, and delivery zones that generate the highest probability of a header on target. The tournament showed the whole football world what Arsenal already knew. Expect every club to invest more in set-piece analysis in 2026/27, but Arsenal start this season with a significant head start.

Manchester City: Possession-Adjusted Metrics and the New Reality

For years, Manchester City's data advantage was partly hidden in plain sight: when you have the ball for 65% of a game, most per-90-minute stats look exceptional because your players are in attacking positions more often. This is known as the possession distortion problem in analytics, and City's analysts are among the few who have developed genuinely possession-adjusted metrics that strip out that effect.

The challenge for City in 2026/27 is that the possession distortion now works against them in one specific way: it has made several of their players look more valuable than they are in absolute terms, which has inflated their own internal transfer valuations. When their data team models player value using possession-adjusted stats, the numbers are more honest — and occasionally more humbling. How City's analytics department navigates that tension between squad assessment and squad morale will be one of the quieter stories of the season.

Chelsea: The Recruitment Algorithm

Chelsea's transfer strategy has been the most data-driven in the Premier League for the past three seasons — for better and worse. Their recruitment model, built around identifying young players with elite underlying metrics who are outperforming their reputation (and therefore available below their eventual market value), is statistically sound in theory. The execution has been complicated by the volume of signings and the difficulty of integrating them.

In 2026/27, Chelsea's analytics team will be focused on a problem that's harder than recruitment: figuring out which of their current squad their model says should be playing and working backwards from there to build a system that maximises the collective xG output of that group. It's a portfolio optimisation problem as much as a football one. The clubs that solve it well will climb the table. The clubs that sign great players who don't fit together will continue to confuse their own data.

Tottenham: Expected Points and the Variance Problem

xPoints (expected points — the number of points a team should have earned based on the quality of chances created and conceded) is one of the most useful metrics in club analytics because it strips out goalkeeping heroics, finishing variance and bad luck. Over a full season, xPoints is a better predictor of next season's points tally than actual points tally.

Tottenham have had a persistent pattern in their xPoints numbers over several seasons: their underlying performance data suggests they should be finishing higher than they do. The gap between xPoints and actual points at Spurs has been larger than at almost any other top-six club. Their analytics team knows this. The question they can't fully answer is whether the problem is tactical (a system that doesn't convert its underlying quality), psychological (a squad that fails to close out games they should win), or simply finishing variance at scale. In 2026/27, if Spurs' xPoints-to-points gap narrows, they'll have solved something. If it doesn't, the data will be damning.

The Middle-Tier Clubs: Where Analytics Wins Championships

The most interesting analytics stories in 2026/27 won't be at the top six. They'll be at clubs like Brentford, Brighton, and Fulham — mid-table sides who have used data to consistently punch above their financial weight and who are now genuinely competing with clubs that spend twice as much.

Brentford in particular remain the Premier League's most transparent proof of concept for data-driven football. Their recruitment model, built on identifying players whose underlying metrics significantly outperform their reputation in lower leagues, has produced genuine Premier League-quality players at Championship prices. Their set-piece operation is among the most analytically sophisticated in England. And their willingness to sell players when the data says the player has peaked — and reinvest in cheaper alternatives with higher upside — is the kind of disciplined portfolio management that finance professionals would recognise immediately.

What Fans Can Track Themselves

The good news for supporters is that the metrics the clubs are using internally are increasingly available publicly. xG is now displayed on major broadcast graphics. Pressing stats are tracked by multiple free data providers. Heat maps and progressive carry data are available from Fotmob, FBref and Sofascore within hours of a match finishing.

The gap between what clubs know and what fans can access is narrower than it has ever been. What separates the club analysts from the fan with a spreadsheet is mostly proprietary tracking data — the exact position of every player on the pitch 25 times per second, which captures pressing intensity, spatial coverage and defensive shape in ways public data can't. But for understanding the shape of a performance, its underlying quality and what it predicts about the next game, the public metrics are now good enough to form genuinely informed opinions.

This season, watch the xG differential — the difference between your club's xG for and xG against across multiple games. A positive differential of more than +0.5 per game sustained over 10 matches is one of the strongest predictors of where a club will finish. If your club's actual points tally lags their xG differential significantly, the variance will correct itself eventually. The data almost always wins over a full season.