Why Data is Changing Football – and Why It Can’t Do It Alone
Football has always been a game of intuition, rhythm and feel, of watching a player and “just knowing” that they’re special. But behind the curtain, the modern game is evolving fast, and data is becoming an increasingly important tool for clubs, coaches, analysts and journalists alike.
Whether it’s helping to identify undervalued talent in the transfer market or analysing tactical trends, data offers a way to look deeper than the naked eye. It doesn’t replace scouting , it complements it. Used well, it sharpens judgment, adds context to performance and reduces the margin for error in high-stakes decisions.
But data doesn’t speak for itself. It’s only useful when interpreted with nuance. A raw number is just that, raw, and without tactical context, player role or game state, it can mislead as much as it can inform. Still, understanding the common metrics used in modern football can help fans, coaches and even players see the game in sharper focus. Let’s break down a few of the most important that we often use in our long-reads.
Progressive Passes & Progressive Passing Distance
These metrics track a player’s ability to move the ball forward with intent. A progressive pass is defined as a completed pass that moves the ball at least 10 yards closer to the opponent’s goal (with some exceptions).
Progressive passing distance aggregates the total distance advanced by such passes over a game or season. These stats help us identify players who break lines and add verticality, think midfielders like Rodri or our very own Sandro Tonali.
Limitations: Not all forward passes are equal. A simple ball into space counts the same as a threaded through-ball. Context, pressure, opposition shape, and execution matters.
Progressive Carries & Progressive Carry Distance
Similar to progressive passes, progressive carries track when a player moves the ball forward on the dribble by a meaningful amount (usually 5-10 yards or more toward goal).
This stat is especially useful for identifying players who can resist pressure and carry their team up the pitch, think Bukayo Saka or Khvicha Kvaratskhelia. Progressive carry distance sums up the total distance a player advances with the ball.
Limitations: Carries that go nowhere near goal still count. Again, intent and context are key.
Touches in the Attacking Third
This metric tracks how often a player is involved in the final third of the pitch, a valuable proxy for offensive involvement and positional influence.
High numbers here usually indicate a player’s consistent presence in build-up or final actions. It’s particularly useful for comparing full-backs, midfielders, or wingers across different tactical systems.
Limitations: Volume doesn’t equal quality. A player may have many touches in the final third without producing real danger.
Press Resistance
Not always a publicly available stat, but increasingly used by clubs and analysts, press resistance attempts to measure how well a player retains possession under pressure, through dribbles, passing, or shielding.
It’s particularly prized in midfielders and centre-backs who can escape pressing traps and maintain control in tight areas. Rodri (him again) and Frenkie de Jong are often cited examples.
Limitations: Pressing varies wildly between teams. Retaining possession against a low block isn’t the same as evading a coordinated press.
Shot-Creating Actions (SCAs)
This is one of the most illuminating stats in modern football. Shot-creating actions are the two offensive actions directly preceding a shot, typically passes, dribbles, or drawn fouls. Sometimes referred to as a “pre-assist”.
It’s a great way to measure creative involvement beyond goals and assists. Kevin De Bruyne, unsurprisingly, tends to top these charts.
Limitations: It includes shots from low-value positions and doesn’t account for quality. It’s a volume metric, not a value one.
Expected Assists (xA)
xA measures the quality of a chance created, based on the probability that the resulting shot will be scored. It helps assess how dangerous a pass or cross is, regardless of whether the finish is converted.
This metric adds important context to assist totals. A player might have low assists but high xA if teammates keep missing big chances.
Limitations: It still relies on shot models, which can’t perfectly capture every nuance (e.g., a clever layoff or disguised through ball). It also doesn’t consider the creativity of off-ball movement.
Per 90 vs Total Stats – Measuring Volume and Efficiency
Another key distinction in football data is between total stats and per 90 metrics. Total numbers, like 85 key passes in a season, reflect a player’s overall output, which is useful for understanding volume and durability. But they can be misleading when comparing players who’ve played vastly different minutes. That’s where per 90 stats come in. By showing a player’s output per 90 minutes, we can compare players on a level playing field, regardless of how much they’ve played.
Understanding Percentiles – Comparing Players to Their Peers
One of the most common ways you’ll see data presented, especially in our articles— is through percentiles. Rather than giving a raw number (like 4.3 progressive passes per 90), percentiles show how a player ranks compared to others in a similar role. For example, if a midfielder is in the 90th percentile for progressive passes among central midfielders in Europe’s top five leagues, that means they perform better in that metric than 90% of their positional peers. It’s a valuable tool for seeing who stands out, who’s above average, and who might be flying under the radar.
The Bottom Line
Data isn’t magic, and it doesn’t give you all the answers. But it does help ask better questions. We hope this breakdown helps you understand how these concepts work and why we use them in our analysis. As always, the numbers are just part of the story but with the right lens, they can help us see the game more clearly.
