With my database now containing five full seasons of data from both Allsvenskan and Superettan, an interesting thing to look at is how teams perform long term.
To do this I’ve plotted both xG and actual goal difference over time for each team in the upcoming Allsvenskan season. Using a 15-game (half a season) rolling mean, the idea is to allow us to really identify long-term trends and hopefully eliminate possible strength of schedule biases.
I’ve also added information on managerial changes taken directly from the database, corrected with the help of Wikipedia where I found it obviously wrong.
Some of these plots really do offer some interesting stories. Here’s a few examples:
As a Djurgården supporter, the poor start to the 2013 season still make me sick to think about. Heading straight for relegation when Per-Mathias (dubbed Per-Messias by the fans) Høgmo took over the club, things quickly turned around though. Both xG and actual goal difference skyrocketed with DIF placing 7th at the end of the season. Sadly Høgmo rejected a lucrative contract extension in favour for the Norwegian national team, but Pelle Olsson has managed to improve Djurgården’s numbers since then. Notice the drop in goal difference in the second half of the 2015 season – right about when Mushekwi left the club and Radetinac got injured.
After some dismal years in Superettan following their relegation in 2009, Hammarby’s improvement and build-up to their comeback in Allsvenskan actually started with Gregg Berhalter back in 2012. Under Nanne Bergstrand the club literally exploded in late 2014, only to see their numbers plummet back down again as they faced the harsh reality of Allsvenskan in 2015.
Elfsborg have been on a roller coaster ride since their last league title in 2012, and a strange pattern have evolved with both their xG and actual goal difference rising during the first half of the season only to fall in the second. With their lowest numbers since at least 2011, the questions is if they will be able to turn things around yet one more time?
If you wanna see individual plots of any of the other teams, just let me know on twitter. I can also do them without the over/underperformance coloring.
Lastly, here’s a plot with all 16 teams together for easy comparison: