Watching the Swedish Women’s National Team beat Brazil to advance to the final in the Olympics, I suddenly realised that the shot location data I collect for Allsvenskan is available for the top women’s league Damallsvenskan as well. I then thought about Chad Murphy’s work collecting data MANUALLY for USA’s NWSL and how I should build an Expected Goals model for Damallsvenskan.

Hoping that the data was of the same quality as that for Allsvenskan, and with the code more or less already in place from my other projects, I plotted out the xG difference for each team this season:

Wow, only 3 teams with positive xGD! Had I found some bug in the code or was the data faulty? Having next to no knowledge about the league I looked it up, and yeah, Damallsvenskan is very skewed towards a few top teams:

Rosengård and Linköping really dominate the league and have all but locked up the Champions League spots with their 30+ points already, while the four bottom teams all have less than 10 points. As we’ve seen above, Linköping is the really superior side when it comes to Expected Goals.

Let’s dig deeper:

Here we see part of why Linköping have such a commanding lead in the xGD table. They take far more shots per game than anybody, and over 4 shots more than closest contestants Rosengård. The two top teams look superior when it comes to shots, but what about effectiveness?

The top teams stands out here too, but this time it’s Rosengård with the superior numbers as they score on nearly every 4th shot! Kristianstad on the other hand are very ineffective with about 13 shots per goal.

The two top sides don’t stand out defensively, instead it’s Kopparbergs/Göteborg who dominate the defensive effectiveness with over 13 shots faced per goal conceded. At the bottom, Mallbacken are struggling defensively with under 5 shots faced per goal conceded!

Looking at Expected Goals for and against we really see how especially Linköping dominate the league by keeping a solid defence and producing a crazy ~3 xG per game!

Player stats

Ok, so that’s it about teams – what about individual players? I ran my player script and was happy to see two players I’ve heard of despite not following women’s football: Stina Blackstelius I know from the Swedish national team and Marta I know because she’s Marta.

There’s a lot of players with a Goal Contribution around 1 per game, and as expected they’re mostly from the top teams. As a Djurgården supporter, I’m happy to see young Johanna Kaneryd placing second of the younger players. So what about Expected Goals?

Not surprisingly, xG queens Linköping occupy the top three spots when it comes to xG per 90 mins. What is surprising though is that top contender Rosengård only have one player in the top 10, with Marta just missing out by 0.03 xG90.

Prediction

What about a prediction then? As we’ve seen, the model clearly ranks Linköping highest and as they’ve got one game in hand and Rosengård left to face at home, this really shows up in the prediction with the model giving them 72% to take the title.