Football returns to Sweden on 14 June and that means the return of Allsvenskan. Unlike many leagues around the world, Allsvenskan is starting a new season, due to its calendar running from March to November. They simply had to delay the start of a new season rather than needing the current one to be finished quickly.

To help get you excited about the return, here is a data analysis of Allsvenskan’s playmakers. In this analysis, we will search through the data to find players putting up stand-out statistics in the metrics I have deemed important to the role of a playmaker.

The data set used features all players that played primarily in any midfield position, as modern playmakers could operate from wide or central areas. In the case of Liverpool’s Trent Alexander-Arnold, we can see that Premier League clubs are starting to utilise full-backs as playmakers. In this case, I wanted to focus on midfielders though. All players have featured for at 1000 minutes over the course of the season.


The first thing to look for in a playmaker is whether they can get on the ball regularly. In watching them, this would be looking to see how they find space to receive the ball in order to do damage with it.

In this case, I’m going to look at passes per 90 as the first metric to judge a player’s involvement.

Allsvenskan: Finding the best playmakers - Data analysis tactics

This is a good starting point to identify a few standout players. The first is David Batanero, who made 83.66 per 90. He is particularly impressive as he was featuring for GIF Sundsvall, who were relegated. He has since signed for Mjällby who won promotion to Allsvenskan, keeping him in the top division. It’s interesting to note that both the highest performers in this metric played for Sundsvall. Juanjo Ciércoles (81.14) was Batanero’s midfield partner.

In fact, Sundsvall made the most passes in the league as a team, with 563.94. It would be worth analysing how a side who dominated the ball managed to get relegated, but that’s for another day.

Immediately an issue has come up though: players who play in teams that have a high proportion of possession are most likely to show up by searching passes per 90. If we really want to find a playmaker we need to find a player who makes a large proportion of their team’s total passes, regardless of how many passes that team makes.

Therefore, I decided to calculate each players’ % of total team passes played.

Allsvenskan: Finding the best playmakers - Data analysis tactics

Several of the names highlighted in the previous visual have come up again here, but the leaders in the metric aren’t as far separated from the rest of the data-set. It’s noticeable that Erik Friberg of BK Häcken, who made 66.52 passes per 90, which was enough to put him 3rd in that metric, came out on top for percentage of team passes (15.11%).

Batanero and Ciércoles both still perform very highly as well, so they’ll be players to keep an eye on as we search through other metrics.

Darijan Bojanić and Jeppe Andersen, both of Hammarby also show up very well, making 14.2% and 14.72% of their team’s passes respectively. It also caught my eye that they were the only players in this top group to be under the average age of the data-set (27.34).

Ball Progression

When it comes to being a top playmaker, being heavily involved isn’t enough. A player could be heavily involved and simply make all their passes in their own half. A proper playmaker drives their team forwards.

With that in mind, I used the same principle as above and calculated the percentage of passes played which are progressive. For reference, Wyscout’s definition of a progressive pass is one that moves the ball 10m closer to the goal when played in the opposition’s half and 30m closer when played in the player’s own half.

Once I had that metric, I plotted it against the percentage of team passes metric and came up with this graph.

A few names are now popping up regularly. Friberg, Batanero, Bojanić, Ciércoles, and Andersen all feature again.

There are others that need to be mentioned as well. Rasmus Elm, playing in his final season in professional football, made the highest percentage of progressive passes with 16.04% In fact, he’s one of the top performers on this graph as although he isn’t the top performer in the percentage of team passes metric, he’s so far ahead in the percentage of passes progressive.

How do they progress the ball?

There’s more to ball progression than passing. Some players progress the ball through passing, others do it by carrying the ball forwards.

Allsvenskan: Finding the best playmakers - Data analysis tactics

This shows stand out players in the progressive passes metric I used in the previous graph as well as those that stand out as ball carriers. I have highlighted the top performers in both metrics, including 6 players who feature in the ‘double trouble’ sector of the graph. This means they are above average for both metrics.

Bojanić is a top performer once again, ranking 3rd in the data-set for % progressive passes, whilst sitting comfortably above average for progressive carries. Bojanić has now featured in all of these graphs in the early part of our process, so he’s definitely one to consider regarding top playmakers in Allsvenskan.

This graph is also interesting because it has highlighted players who play in wide positions, whereas the previous graphs have all thrown up central midfielders only.

Sead Hakšabanović is one of those players. The 21-year-old Montenegrin is on an 18-month loan at IFK Norrköping from West Ham United and was one of the best attacking players in the league from his position on the left-wing. This was an important comeback for him after a failed loan to Malaga. On this graph, he is only just over average for % of passes progressive with 7.51. However, no player who ranks higher than him for progressive carries (he has the 4th most with 3.33) makes a higher proportion of progressive passes than him.

The most balanced of the players featured in the ‘double trouble’ section is Vladimir Rodić of Hammarby. He is a 26-year-old right-sided midfielder from Croatia putting up well above average numbers in both metrics. He made 2.62 progressive carries per 90 and 9.57% of his passes are progressive. When Rodić has the ball, he’s comfortable moving his team forwards by carrying the ball or by passing it.

Line breaking

 Another thing that a good playmaker needs, as well as progressing the ball, is a way of breaking organised opposition lines. Wyscout’s ‘smart passes’ metric attempts to quantify numbers of passes which are ‘creative and penetrative’ and ‘attempt to break the opponent’s lines.

I used that metric to assess the data-set and created another swarm plot to show the top performers.

Allsvenskan: Finding the best playmakers - Data analysis tactics


Once again, some similar names are popping up. Batanero sits in 3rd with 2.03 smart passes per 90. We can now say that he’s not simply a player that makes a lot of passes. He’s a player that makes a lot of passes that progress his team forwards and looks to create dangerous attacking positions for his team.

The two top-ranked players are Tobias Sana (2.17) and Patrik Karlsson-Lagemyr (2.16), both of IFK Göteborg. This perhaps suggests something about the style of attacking football their team plays. Neither of these two players have appeared in this analysis so far, so this suggests that though they are less involved as some of their counterparts, they are very aggressive when they do have the ball.

The fourth rated in this metric is another Hammarby player: Muamer Tanković. He is a player that just missed out on being included in the ‘double trouble’ sector of the ball-progression graph. This is due to his percentage of passes progressive being just below average. Combining with Rodić, they make Hammarby’s midfield a dangerous one.

After that standout cluster of four players, Daleho Irandust is the top performer of the rest of the pack with 1.69. The BK Häcken right-winger appeared as a top performer for progressive runs but was below average for progressive passes. This suggests that when he gets on the ball he primarily wants to run with it before looking to play a killer pass in behind the defence.

Creative Threat

Speaking of killer passes leads us nicely on to our final section. The best playmakers need to make passes into the opposition penalty area and also need to be able to create good chances for their teammates to score.

In order to look into this aspect, I’m looking at ‘deep completions’ which are passes played to an area no further than 20m away from the opponent’s goal, as well as expected assists per 90. When assessing creativity, it’s always better to look at expected assists rather than assists, as that purely values the quality of the chance created, not whether it was converted into a goal.

Allsvenskan: Finding the best playmakers - Data analysis tactics
Allsvenskan: Finding the best playmakers – Data analysis tactics

According to the deep completions swarm plot, Tanković is by far the most impressive, being the only player to average more than 3 per 90 minutes. As we know, he was already impressive in the line-breaking ‘smart passes’ metric. As he pops up here, we can conclude that he is largely breaking defensive lines rather than midfield lines.

Friberg is appearing as an impressive performer again after being present regularly during the early part of the process. It’s notable that lots of the top performers in this metric are attacking midfielders, such as Irandust and Hakšabanović, who we’ve looked at earlier in this analysis. Both of those average over 2 deep completions per 90 However, Friberg plays primarily as a defensive midfielder, so it’s particularly interesting to see him rank highly for deep completions. This could be that he’s playing raking passes from deep, or he may carry the ball forwards before playing it into the box. This would have to be assessed properly by watching the video.

Allsvenskan: Finding the best playmakers - Data analysis tactics
Allsvenskan: Finding the best playmakers – Data analysis tactics

After creating a swarm plot of xA per 90, I was surprised by how little crossover there was in the two metrics. The only player to appear in the top group of both charts is Alexander Kačaniklić, who plays on the right side of midfield for Hammarby. As he hasn’t shown up earlier in the process, I don’t think he should be considered as a top playmaker, but he would be one to look at further if I was simply focussing on attacking midfielders.

Allsvenskan: Finding the best playmakers – Data analysis tactics

Plotting these two metrics together allowed me to find the correlation properly. Whilst the top performers aren’t exactly the same, there are plenty of players that performed well above average in both metrics.

The three most balanced players of the top performers across both metrics are Sana, Batanero, and Elm. All three create high-value chances for their team, whilst playing a well above average volume of deep completions. These are all players we’ve seen before in this analysis, so they will be considered amongst the top playmakers in the division.


Throughout this analysis, I have identified top performers in the key aspects I believe make a good playmaker. It has thrown up several of the same names along the way, though it’s clear some are ‘advanced playmakers’ which makes them top performers in more attacking focussed metrics. Others are ‘deep-lying playmakers’, who dictate play and progress the ball forwards to their attacking teammates to create chances.

Rasmus Elm is a candidate for the top of the deep-lying playmakers. It’s quite remarkable to see him popping up so often as a high performer considering this was his final season. He is also one he performed highly in ball-progression as well as creativity.

Of the more attacking-minded players, Irandust, Hakšabanović, Tanković, and Sana stand out, but there are several other players that show up in the graphs that haven’t been mentioned.

The player who came up the most often in the graphs was David Batanero. The 31-year-old Spaniard is probably the most rounded of all these players as he appeared as a high-ranking player in the graphs that featured ‘deep-lying playmakers’ as well as appearing in the final scatter graph depicting creativity and threat. It is very surprising that he managed to perform so well individually whilst his team got relegated.

In order to fully analyse and decide on who the top playmakers are, video needs to be analysed, with a full scout report of Batanero being the next step to take.