Is it time to analyse attacking data differently?
Is it time to analyse attacking data differently?

The genie is well and truly out of the bottle when it comes to data in football, but there is still plenty of scope to maximise its utility.

In today’s thought experiment, The Athletic asks… should we adjust all on-ball player metrics?

Let’s clarify what that would mean with an example.

In a busy summer transfer window, your team’s recruitment staff are looking for a clinical striker who is quick to get a shot away and will score something out of nothing.

Using data as a filter, is it more impressive for a striker to score 10 goals for a team battling relegation than a striker scoring 25 goals for the inevitable title winners?

When adjusted for opportunity to score, the gap between the players’ output might not be as big as first thought.

This is a simple example, but let’s dig deeper.


Many will be familiar with some of the adjustments already made within player data. The most obvious one is to consider a player’s metrics per 90 minutes rather than their total actions. 

As outlined in The Athletic’s football analytics glossary, this is a crucial adjustment so that a fair comparison can be made between players for the time they are on the pitch.

Adjustments are also made to a player’s defensive metrics. Put simply, a player can only perform a defensive action (e.g. tackles, blocks, interceptions) when their team does not have the ball. If a team has less possession, a player has more opportunities to defend.

To assess all players equally, we can adjust defensive statistics by counting the number of times they make those actions for every 1,000 opponent touches rather than in total.

The question is, should we account for this opportunity in the same way from an attacking perspective?

The impact of these “denominators” — e.g. “per 90 minutes”, “per 100 touches”, or “per minute in possession” — cannot be underestimated as Dan Altman, creator of smarterscout, highlights.

Adjusting data to per 100 touches reveals Messi’s enduring class (Photo: Catherine Steenkeste/Getty Images)

“Getting denominators right is one of the most important — and yet most often ignored — parts of the analysis process. Apart from sophisticated positional play, you can only attack when you’re in possession; you can only defend when you’re out of possession,” Altman says. “Minutes played won’t be a good denominator except for measuring something a player does whether attacking or defending — like going up for aerials.”

Let’s work through a comparison.

Across Europe’s top five leagues last season, mapping players with the top 20 shots per 90 will pull up many of the usual suspects. Robert Lewandowski led the way for a dominant Bayern Munich with an average of 4.8 shots within a game. 

Behind him sat players from similarly high-possession sides, with Mohamed Salah (Liverpool), Zlatan Ibrahimovic (AC Milan), Kylian Mbappe (PSG) and Karim Benzema (Real Madrid) among the prolific cohort.

That is… kind of interesting, but it does not separate those who are shooting frequently because their team is dominant from those who are shooting frequently due to individual playing style.

Altman explains how to find more signal among the noise. “If we’re trying to gauge a player’s style on the ball, then we’d want to know what share of the player’s attacking touches were passes, dribbles, or shots. Here, we’d want to use total attacking touches as the denominator.”

Adjusting players’ shots per 100 touches shuffles the pack neatly. Lewandowski is still near the top, but the top 20 is filled more with out-and-out No 9s whose role is to shoot frequently with the touches they have. 

Here, Anthony Modeste came out on top for Cologne, with 14 shots per 100 touches, showing his propensity to get a strike away — 20 Bundesliga goals earned him a move to Borussia Dortmund this summer.

As a fully fledged member of the no-touch All-Stars, one name near the top of the list aligns with Altman’s example perfectly.

“With Jamie Vardy at Leicester, his team didn’t have many minutes on the ball. Yet when they did have it, they were incredibly efficient. Vardy didn’t take many shots per 90 minutes, but he did take a lot of shots per minute in possession.”

Meanwhile, Pierre-Emerick Aubameyang’s style shone through during his time at Arsenal and Barcelona last season — the man simply shoots frequently.

There is no right or wrong number of touches that a forward should have, but having a standardised measure across all players highlights their tendency to perform a certain action when given an equal opportunity to do so.

Looking at Europe’s prolific pass masters, Marco Verratti, Toni Kroos and Joshua Kimmich are among just six players to average more than 100 touches per game last season. Even after adjusting per 90 minutes, those players are likely to dwarf their peers in other actions such as progressive passing — simply due to their higher overall involvement.

Adjust per 100 touches and Verratti and Kroos fall out of the top 20. This doesn’t mean they are not proficient progressive passers, but simply that such actions are not attempted as frequently as the “per 90” comparison suggests. 

Also, note a certain Lionel Messi showing just how often he still looks to advance the ball towards the opponent’s goal.

Midfielders such as Iker Muniain (Athletic Bilbao), Amadou Haidara (RB Leipzig) and John McGinn (Aston Villa) rise through the ranks, highlighting their greater tendency to play the ball forward when given equal opportunity.

Not better or worse, but once again a stylistic indication of what a player looks to do more or less of when they do have the ball.


Looking per 100 touches is not the only alternative method. 

Much in the same way that John Muller adjusted each player’s metric in The Athletic’s “player roles” analysis, we can look at the share of a player’s actions relative to the whole team when they are on the field.

This method is valuable in showing the degree of influence a player has. For example, focusing solely on the Premier League, we can look at players’ share of team passes into the penalty area when they are on the field. 

Rather than skewing towards the high-possession sides, we see some interesting output in players’ responsibility.

Crystal Palace’s Michael Olise tops the list, scooping nearly 25 per cent of the team’s open-play passes into the box when on the field. Context would need to be applied here — 14 of his 26 Premier League appearances were from the bench last season, meaning an injection of attacking threat was likely his remit when he was on the field.

Elsewhere, you can tease out the responsibilities held by players for their respective team, with Manchester United’s Bruno Fernandes (24 per cent), Newcastle’s Allan Saint-Maximin (20 per cent), Manchester City’s Kevin De Bruyne (20 per cent) and Liverpool’s Trent Alexander-Arnold (19 per cent) all among the expected candidates for their team’s share of creative responsibility to advance the ball into dangerous areas.

Crucially, the tactical role that a player is asked to fulfil can heavily influence their statistical output — and this can change between seasons — but data surrounded in a little more context is far more beneficial to understand what a player truly does on the pitch.

This thought experiment is not a new consideration within football analytics. There are far more complex metrics, models and algorithms within this space, but at the simplest level of analysis, should this be the new normal in how we interpret football data?



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