Data scouting Damallsvenskan: finding the best striker

There has been a lot of controversy in women’s football in the last few days. What is the best league and which league is better than the other? Bit of non-discussion in my opinion, but the most important thing in it all is the accessibility and visibility of the league. I think that the Swedish Damallsvenskan is a league which relatively good and produces strong talents, but due to visibility isn’t really hyped. That’s why I’m writing about it today.

In this scouting piece I’m going to look for a striker who’s good in the box, has volume in shots per 90 and looks to match or overperform his expected goals ratio.

The data
The data used in this analysis comes from Wyscout. In the dataset for the striker, I’ve selected each player who primarily plays on the striker position. Obviously, there are other players who have played in this position, but I’ve only selected the players that have played as a striker as a dominant position in the current season. This leaves me with 71 players who qualify in the Damallsvenskan 2021.

Because I’m looking at the current season, which is a full season, I want to make a selection for players that played a decent amount of games for me to assess them. For me, it’s important that they played at least 600 minutes in this season. After looking at that I’m left with 38 players in my dataset and they will go through my analysis process. The data was retrieved on 18th December 2021.

I will look at the following categories and metrics to assess their abilities through data:

  • Shots
  • Dribbling
  • Offensive duels
  • Assists
  • Goals

After going through the data analysis and visualisation, I will make a shortlist of players who I think are worth keeping your eye on.

Shots
Looking at shot quality can be measured in different things. In the scatterplots below I will look at the volume of the shots and the expected goals that are generated through the shots.

In the shot volume, we can see that Blackstenius 4,82 shots per 90), Gielnik (3,99 shots per 90), and M. Larsson (3,89 shots per 90) stand out in terms of the number of shots.

The best performers in terms of the percentage of shots going on target are Jakobsson with 61,54% shots on target, Rogic with 60% shots on target, and M. Larsson with 54,35% shots on target.

In the scatterplot above you can see the number of shots per 90 of a certain player and the expected goals per 90 of that particular player in question. The reason we look at this is how many shots a player has in a game and how high the probability is of scoring an actual goal.

In the shot volume, we can see that Blackstenius 4,82 shots per 90), Gielnik (3,99 shots per 90), and M. Larsson (3,89 shots per 90) stand out in terms of the number of shots.

Looking at the expected goals generated per game we see the following players coming on top: Blackstenius with 0,81 xG per 90, M. Larsson with 0,8 xG per 90, and Lundin with 0,52 xG per 90.

Dribbling

Dribbling often is linked to wide midfielders of wingers, but it can be a valuable aspect of a striker’s game as well. The ability to control the ball, progress on the pitch, and deal positively with a 1v1 situation with an opponent defender, is not to be underestimated. Especially when you are not playing a typical central forward role, but playing with two strikers.

If we look at the number of dribbles per 90, the following players come out on top of their respect metric: Jónsdóttir with 8,78 dribbles per 90, Kafaji with 8,34 dribbles per 90, and Eiríksdóttir with 8,12 dribbles per 90.

When we look closer to the success rate of the dribbles, we can see that a different set of players scores high – but attempt fewer dribbles per 90: Zamora with 67,5% successful dribbles, Scarpa with 65,38% successful dribbles, and Wangerheim with 65,12% successful dribbles.

Offensive duels

The importance of offensive duels can be seen in two lights. The first one, is to measure the physicality of a strikers and the ability to win offensive duels to create something out of an attack. The second one, is to engage in the pressing style set out by a team. The ability to press a direct opponent and win the ball can also be found in this metric of offensive duels.

The most offensive duels conducted per 90 are by the following players: Da Silva with 19,52 offensive duels per 90, Mijatovic with 19 offensive duels per 90, and Eiríksdóttir with 18,77 offensive duels per 90.

If we look closer at the players that have the highest percentage of won offensive duels, the following players stand out: Hellstrom with 39,52% offensive duels won, Gielnik with 39,17% offensive duels won, and Mijatovic with 39,09% offensive duels won.

Assists

Expected metrics seem simple but can become incredibly complicated when combining things. In the scatterplot above I’ve taken a look at the probability of the pass becoming an assist per 90 minutes and looking at the actual assists of a player per 90 minutes.

If we look at the expected assists per 90, we can see that three players stand out from the crowd with a significantly higher xA per 90 than the rest. Jónsdóttir has 0,26 expected assists per 90, Jalkerud has 0,25 expected assists per 90, and M. Larsson/Mijatovic has 0,24 expected assists per 90.

Looking more closely, we can see that the actual assists per 90 don’t correspond with the three players with the highest expected assists per 90. Bredgaard has 0,33 assists per 90, Kapocs has 0,30 assists per 90, and Jalkerud/Mijatovic has 0,29 assists per 90.

Goals

In the end the most important thing for a striker is his output: goals. I’m looking at the probability of scoring a goal with a certain short and looking at the actual goals scored by a particular player per 90 minutes.

Looking at the expected goals generated per game we see the following players coming on top: Blackstenius with 0,81 xG per 90, M. Larsson with 0,8 xG per 90, and Lundin with 0,52 xG per 90.

When we look more closely to the actual goals scored per 90 we see that Blackstenius stands out with 0,80 goals per 90, followed by Kanu with 0,7 goals per 90,  and M. Larsson with 0,59 goals per 90.

Short list

Four players have impressed me in terms of data and I have made percentile ranks data visualisations of them, before going further and analysing them through video.

After this phase of data scouting and analysing, we will move into video scouting and assess how well they do in certain game situations. This article was an example of how you use data to make a shortlist.

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