It’s been over six weeks since my last Allsvenskan update but now I finally have time to get to it. Six rounds have been played since last time and a lot has happened. Let’s take a look at the league table:
Compared to last time, we can immediately see that reigning champions Norrköping have climbed up above Malmö to claim the top spot, which is very impressing given the players who have left the club, and the mid-season managerial change.
At the other end of the table, Djurgården have (luckily for me) picked up pace under new manager Dempsey and moved up from 14th to 11th, while Helsingborg and Sundsvall have struggled – only picking up 2 points each.
Let’s have a closer look on how the teams have performed:
Despite giving up the first place in the table to Norrköping, Malmö have distanced themselves from the rest in terms of shot dominance. Not much else has changed, Örebro are still involved in some very open games while Gefle struggle to create chances.
Örebro and Elfsborg have moved into the ‘constant threat’ quadrant thanks to some effective scoring, while Hammarby have done the opposite. Kalmar have improved their effectiveness, but at the same time seen a drop in shots taken per game.
Here we see how AIK’s and Norrköping’s improvements come mainly from their defensive work; both sides have been better at keeping shots from going in since the last update. Kalmar’s defensive effectiveness has improved as well.
Expected goals for and against look much like they did last time but AIK’s defensive improvements have seen them close in on the top 2 sides, as they’ve increased their xGD by nearly 0.20 per game.
How about a prediction then?
Malmö’s defeat to Djurgården has really opened up the title race, but my model still fancy them. Norrköping have improved though, and we could be in for a very interesting finish to the season. AIK have improved as well, and have seemingly all but locked in a top-3 spot. In the other end of the table Falkenberg have plummeted from around 22 expected points to less than 16, with the model giving them no chance of reaching the relegation play-off spot occupied by Helsingborg.
Djurgården under Mark Dempsey
As mentioned earlier, as a Djurgården supporter I’m very happy with how the form has improved under new manager Dempsey. In the last update I showed the long-term trends leading up to Olsson’s sacking, and now that Dempsey’s been in charge for 7 games we can see how he’s managed to turn things around:
While shots conceded actually declined during Olsson’s last season, so did shots taken. What we see under Dempsey’s rule is clear: everything have improved! Djurgården now concede less and take more shots but more importantly, both actual goal difference and xG difference has improved, leading to more points and a climb in the league table.
Though a bit of hindsight, through my work with Norwegian football I was optimistic about Dempsey coming in as I knew he would provide the energy needed for a turnaround. Let’s hope Djurgården can continue to pick up points to climb further.
Another thing I mentioned in the last update was how Opta data is now available for Allsvenskan, and I showed some passing maps heavily inspired by 11tegen11 and David Sumpter. I’ve since then played around with the script to create passing map animations, which received a lot of positive feedback on twitter and have now been dubbed ‘passing spiders’, often a quite fitting name.
I don’t know enough about tactics to determine if these animations holds some analytical value, but they are fun to look at and could possibly be used to provide an interesting narrative of individual games combined with other types of analysis. I got a lot of good advice on improvements on the animation and will implement some of it in the future.
That’s it for now!
3 thoughts on “Allsvenskan round 23 update”
Love the site, thanks for keeping it up this season. I’ve been piloting a logistic model in Allsvenskan this year, and am enjoying comparing it to your xG simulator. It mostly agrees with your rankings, but relative to the market it loves Norrköping and Örebro and hates Hammarby and Östersunds.
One question: are you including awarded goals in your actual goal tally? In reality, Malmö have scored 45 and conceded 19, Östersunds have scored 26 and conceded 38, etc. The predictive value of these awarded games has to be negative, no? Why include them in anything?
(As an aside, it’s such a shame that the Swedish football authorities award goals and victories: that nonsensical 3-0 to Malmö in Gothenburg has a huge chance of costing Norrköping the title!)
Thanks a lot!
You actually caught me making a mistake there, I had to check but it turns out I’m using the awarded goals so thanks for finding that ‘bug’ for me.
And I agree on the awarded wins, it’s really a shame that games can be decided away from the pitch, but there’s good news as at least the Jönköpings Södra – Östersund decision was overturned the other day, with the correct 1-1 result now back.
That is good news! I hadn’t heard that, thanks for updating me.
One other question for you! How do you treat homefield advantage in Djurgården-Hammarby games in your model? I’ve been using neutral site (no homefield advantage), though this can’t be right: I know that in Australian rugby (which has a lot of shared stadiums, and with them a robust sample to explore) there is an effect, but when I was backtesting my Swedish model I didn’t see enough of a difference between Djurgården at home and Hammarby at home, after controlling for team strength, to move confidently off neutral site.