The Genie is okay and real out of the bottle when it comes to data in footballbut there is still much room for maximizing its usefulness.
In today’s thought experiment, the athlete He asks…should we adjust all player metrics on the ball?
Let’s explain what that might mean with an example.
In the busy summer transfer window, the recruiters on your team are looking for a fast-paced clinical striker who will score from nothing.
Using the data as a filter, is it more impressive for a striker to score 10 goals for a team fighting relegation Who is a striker who scores 25 goals for the inevitable title winners?
when modifying for Chance For the record, the gap between the players’ outputs may not be as large as initially thought.
This is a simple example, but let’s dig deeper.
Many will be familiar with some of the modifications that have already been made within the player data. The most obvious is to look at a player’s metrics per 90 minutes rather than their overall actions.
as described in the athleteFootball Analytics DictionaryThis is a crucial adjustment so that a fair comparison can be made between the players during their time on the field.
Adjustments are also made to the player’s defensive metrics. Simply, A player can only take defensive action (such as tackles, blocking, and interceptions) when their team does not have the ball. If the team has less possession, the player has more chances to defend.
To evaluate all players equally, we can Adjust defensive stats by counting the number of times they perform these actions per 1000 touches of the opponent And not in the total.
The question is, should we calculate this opportunity the same way from an offensive perspective?
The influence of these “commons” – eg “per 90 minutes,” “per 100 touches” or “per minute per possession” – cannot be underestimated as Dan Altman, creator smartskot, highlights.
“Getting the right denominators is one of the most important parts of the analysis process — and one that is often overlooked. Aside from sophisticated positional play, you can only attack when you are in your possession; you can only defend when you are out of your possession,” says Altmann. “The minutes played are not going to be a good denominator except to measure something a player does whether they are attacking or defending – like going up in the antenna.”
Let’s work through the comparison.
Across all of Europe’s top five leagues last season, designating players with the best 20 shots per 90 would draw many of the usual suspects. Robert Lewandowski led the way to Bayern Munich’s dominance with an average of 4.8 shots per game.
Behind him sat players from similar obsessive teams, with Mohamed Salah (Liverpool), Zlatan Ibrahimovic (AC Milan), Kylian Mbappe (Paris Saint-Germain) and Karim Benzema (Real Madrid) among the prolific group.
This is… an interesting genre, but it doesn’t separate those who shoot frequently because their team is dominant over those who shoot frequently because of the single-player style of play.
Altman explains how to find more signals among the noise. “If we are trying to measure a player’s style on the ball, we would like to know what share of a player’s offensive touches were passes, dribbles or shots. Here, we would like to use the total offensive touches as a denominator.”
Adjusting players’ shots per 100 touches accurately shuffles the pack. Lewandowski is still close to the top, but the top 20 is filled with more number 9 whose turn it is They shot repeatedly with their touch.
Here, Anthony Modeste came out on top with Cologne, with his 14 shots per 100 touches showing his willingness to take the ball away – 20 Bundesliga goals made him move to Borussia Dortmund this summer.
As a full member of the non-touch All-Stars, one name near the top of the list fits perfectly with Altman’s example.
“With Jamie Vardy at Leicester, his team didn’t have several minutes on the ball. However, when they got it, they were incredibly efficient. Vardy didn’t take many shots every 90 minutes, but he did take a lot of shots per minute in possession.”
Meanwhile, Pierre-Emerick Aubameyang’s style shined during his time at Arsenal And the Barcelona last season The man shoots repeatedly.
There is no right or wrong number of touches an attacker should havebut having a uniform measure across all players highlights their tendency to do a particular action when given an equal opportunity to do so.
Considering Europe’s prolific passing masters, Marco Verratti, Toni Kroos and Joshua Kimmich are among just six players who averaged over 100 touches last season. Even after adjusting every 90 minutes, these players are more likely to dwarf their peers in other actions such as progressive passing – simply because of their high overall engagement.
Set per 100 touches and Verratti and Kroos fall out of the top 20. This doesn’t mean they aren’t good progressive passers-by, but simply that such actions are not attempted as frequently as the “per 90” comparison indicates.
Also, note some Lionel Messi has shown how often he still looks to push the ball into the opponent’s goal.
Midfielders such as Iker Muniain (Athletic Bilbao), Amadou Haidara (Leipzig) and John McGinn (Aston Villa) rise through the ranks, highlighting their greater tendency to play the ball forward when given an equal opportunity.
Not better or worse, but again a stylistic indicator of what a player is looking to do more or less when they have the ball.
Looking at every 100 touches is not the only alternative method.
much the same way John Muller modified the scale of each player the athleteAnalysis of “player roles”we can look at the player actions share for the whole team when they are on the field.
This method is useful in showing the player’s degree of influence. For example, by focusing only on the Premier League, we can look at the share of players in the team’s passes into the penalty area when they are on the field.
Instead of veering towards the higher-possession aspects, we see some interesting results in players’ responsibility.
Crystal Palace’s Michael Ulis tops the list, pocketing nearly 25 percent of the team’s open-play passes in the penalty area when on the field. The context has to be applied here – 14 of his 26 Premier League games were off the bench last season, meaning an attacking threat injection was likely his prerogative when he was on the field.
Elsewhere, you can identify the responsibilities players have for their team, such as Manchester United’s Bruno Fernandes (24 per cent), Alain Saint-Maximin of Newcastle (20 per cent), Manchester City’s Kevin De Bruyne (20 per cent) and the Liverpool striker. Trent Alexander-Arnold (19 per cent) are all among the likely candidates for their team’s share of the creative responsibility to push the ball into dangerous territory.
Crucially, the tactical role a player is asked to take can greatly affect their statistical output – This can change between seasons – But contextual data is much more useful than that for understanding what a player is really doing on the pitch.
This thought experiment is not a new consideration in football analytics. There are more complex metrics, models and algorithms in this space, but at the simplest level of analysis should this be the new normal in how we interpret football data?