Allsvenskan 2016 summary pt. 2

A week ago I published the first part of – hopefully – three Allsvenskan 2016 summaries, then focusing on team performance. Now it’s time to have a look at individual players, much like I did back in July. Though there now exists detailed Opta data for Allsvenskan, my work on this site has mostly been based on the older, less detailed data sources focused on shots and thus this summary will only look at attacking players.

I’ve again had a look at Goal Contribution (goals+assists) and Expected Goals, dividing all players into three age groups, and also had a closer look at a few interesting players:

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The Goal Contribution chart is unsurprisingly headed by Häcken’s John Owoeri who clinched the title as the league’s top scorer with his 4 goals against Falkenberg in the last round of the season. Interestingly, Owoeri only came alive in the second half of the season, scoring 15 of his 17 goals after the summer break.

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Assist monster Magnus Wolff Eikrem sits in second, with his 0.71 assists per 90 minutes playing a big part in Malmö retaking the title. Of the other top players, Antonsson, Kjartansson and Nyman left the league during the summer transfer window but still impressed enough during the spring to remain in the top 10.

 

Djurgården’s Michael Olunga sits top among the players aged 20-23. Dubbed ‘The Engineer’ for his ongoing studies, Olunga just like Owoeri needed time to get going, scoring all of his 12 goals during the last 13 games when Mark Dempsey came in to steer Djurgården away from the relegation battle.

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Comparing Owoeri and Olunga, it’s clear from the shot maps why Owoeri was the superior goalscorer this season. He only shoots slightly more than Olunga, but does so from far better locations closer to goal, with his average xG per shot at 0.16 while Olunga at 0.12 rely more on his finishing skill from longer range. If ‘The Engineer’ can work on his shot selection for next season I really think he can challenge for the top scorer title.

 

AIK’s Alexander Isak reign supreme among the youngest players, with his 0.62 G+A90 very impressive for a player who only turned 17 late in the season. He’s quite good at getting into good shot locations as well, with 5 of his 10 goals coming from a sweet spot just in front of goal.

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There’s been plenty of rumours of an upcoming big transfer during the winter window and looking very much like the real deal, Isak could very well break Zlatan Ibrahimovic’s transfer record from 2001. Here’s a nice radar plot from Ted Knutson showing Isak’s skills:

 

Malmö’s Vidar Kjartansson was the king of xG this season, and the club impressingly still managed to secure the title after selling him during the summer transfer window. Kjartansson combined both quantity with quality, taking most of his shots from very good locations with an average xG per shot of 0.2.

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Östersund’s Abdullahi Gero was a bit of a surprise for me, but his shot locations are good with an average xG per shot close to Kjartansson at 0.19. He could very well go on to score more next season if given the chance in Graham Potter’s Östersund side which have done so well xG-wise this season – actually finishing 4th in xG Difference per game!

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As a Djurgården supporter I’m glad to see 20-year old Tino Kadewere’s development this season. Though his 793 minutes played was less than the 900 needed to be included above, he racked up an impressing 0.79 G+A90 which would see him sit 8th overall, just above Olunga, and top the players aged 20-23 if the cut-off would have been 1/4 of the league minutes played instead of 1/3. Focusing more on assists than Olunga, the two could form a dynamic partnership for Djurgården if they get the chance next season.

 

That’s it for now, but if you want to see more shot maps, just give me a shout on twitter. If I’ll find the time, I’ll also write a third summary looking at how my predictions have done over the season and how my model did against the betting markets.

Allsvenskan 2016 summary pt. 2

Allsvenskan and Superettan player update

With Allsvenskan back in action after the summer break and the transfer window approaching, I thought it would be interesting to take a look at some individual player stats. Some players have already left the league for new challenges abroad, and when the window opens on July 15th we’ll likely see some moves between Swedish clubs as well. Given the limitations of my database, this post will focus on attacking players as the most advanced data available is shot coordinates.

I usually don’t write about Sweden’s second tier Superettan, simply because I don’t follow it at all, but as there are several players who could move to Allsvenskan and even abroad, I’ve also looked at players from this league.

So let’s start with taking a look at which players have produced the highest number of goals, assists and Expected Goals so far this season. I’ve only included players who have played 1/3 of the league so far (390 and 450 mins for Allsvenskan and Superettan, respectively) and also looked closer at younger players, splitting them into two groups: players aged 20-23 and players aged up to 19.

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Player profiles

There are some players in the plots above worth looking closer at. Let’s start out with last year’s top scorer, Emir Kujovic:players_03Kujovic has just recently signed with Belgian side Gent, leaving reigning champions Norrköping after a couple of highly productive seasons. His goal and xG output this season are on par with last season’s but he’s also doubled his assists per 90 minutes, having been involved in almost a goal per game this season. I don’t know much about Belgian football, but given the right kind of attacking style he may very well score some goals over there.

players_04Häcken’s Paulo De Oliveira, or Paulinho as he’s called, leads the league in assists+goals per 90 with his impressing 1.13 made up of just goals. Having overperformed against xG since his return to Swedish football, Paulinho may very well be the best finisher in the league.

players_05With Malmö struggling to capitalise on their xG last season, Vidar Kjartansson has grown into a very good signing for them. I haven’t seen him played that much, but given his shot profile he looks just like the strong center forward scoring from mostly inside the box they needed to gradually phase out the aging Markus Rosenberg.

players_06Struggling to take a regular spot at Malmö for the last couple of seasons, Pawel Cibicki’s move to newcomers Jönköpings Södra has worked out well for him. With more playing time, he has continued to improve his goal output and given his age he could be on his way to a bigger club abroad soon.

players_07One who has already taken the next step is AIK’s loan Carlos Strandberg, who after struggling at Russian CSKA Moscow returned to Sweden this season. The young and forceful striker continued where he picked up and has been crucial for his club this season. From his shot profile we can see that he favours shooting from the left, mostly due to his powerful left foot. Set to return to Russia soon, Strandberg will make his last game against Malmö this weekend.

players_08Leading the youngest players in xG per 90 is another AIK striker, 16 year old Alexander Isak, who has impressed so far and is supposedly targetted by a number of big European clubs. Yeah, the sample size is small but given his very young age he’s done well and should he continue to improve he could grown into a class player.

 

Superettan

As I’ve said, I very rarely watch Superettan so I know next to nothing about most players in the league. Here I’ve just picked out a few interesting players from the ranking below to look closer at, and I’ll leave the shot profiles uncommented.

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That’s it for now, but if you want to see shot profiles from any other player in these leagues, just hit me up on Twitter.

Allsvenskan and Superettan player update

Preview: Allsvenskan Qualification Play-off

With the regular Swedish season being over and Norrköping crowned champions, all that’s left now is to decide who’ll get the last spot in next years Allsvenskan. In this qualification play-off, Sirius finishing 3rd in Superettan is pitted against Allsvenskan’s 14th placed Falkenberg in a two-game battle.

Let’s have a look at some stats for the teams, compared to both the teams in Allsvenskan (blue) and Superettan (red):

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From this graph, Sirius actually look really good with especially a strong defensive, even when compared to the Allsvenskan teams, while Falkenberg’s defence looks really poor. However, this doesn’t say much about how the teams compare to each other since Falkenberg has had to face far tougher opponents in Allsvenskan.

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Looking at the xG maps what again stands out is the defensive performances of the teams. While Falkenberg have conceded a massive 415 shots, almost 14 per game, Sirius have only conceded 241 shots or about 8 per game. Not only that, Sirius’ xG per conceded attempt is 0.111 while Falkenberg’s is a staggering 0.154, meaning they concede shots in quite bad (for them) situations – not a good thing.
play_off_04Looking at individual players we can se how Sirius’ Stefan Silva is the big overperformer here with his 12 goals almost doubling his xG numbers. Also, Falkenberg seem to have more goalscoring options with three players over 6 goals while Sirius only have Silva.

As always, I’m not willing to present any prediction for individual games, but here I had hoped to show the results of a simulation covering both play-off games including possible extra time and penalty shoot-out. I have run such an simulation, however I’m not happy with the results as my model seems to be favouring Sirius too heavily. This is almost certainly due to the different leagues involved, making Sirius look way better than they would be against an Allsvenskan side. Since I only came up with writing this post this morning, I haven’t had the time to look into a possible league strength variable to use in the simulation.

But if I had to guess, I’d say that Sirius looks like a real strong side and should possibly be considered favourites for promototion here, mostly due to Falkenberg’s nasty habit of conceding a lot of shots with high goal expectancies.

Preview: Allsvenskan Qualification Play-off

Preview: AIK vs. IFK Göteborg

Monday night AIK will host IFK Göteborg for an extremely important game in the race for the Allsvenskan title. Both teams are close behind Norrköping in the lead and will surely go for the win here to challenge for the title, and I thought it would be a good idea to have a look at some team stats as a preview to this crucial game.

The plot below contains goals, shots, Expected Goals, xG per attempt, goal conversion % and shot on target % – both for and against, normalized per game where necessary. Home and away stats for each team in the league are separated with home in blue and away in red. For each subplot the lower right corner is preferable, with high offensive and low defensive numbers.

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Besides SoT%, both AIK and Göteborg appear to be among the best in the league in each stat, which partly explain why they are fighting for the title. What is really striking though, and could be seen as a indicator of team style, is that while AIK’s offensive numbers at home are really good, Göteborg’s strength when playing away is their defence.

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This is also evident from each teams xG maps, where it is clear that AIK’s main strength is their attacking power and ability to produce high volumes of shots with high xG values each game. Göteborg on the other hand rely heavily on their defensive skills to protect their box and limit the opposition’s scoring chances. This clash of styles adds yet another interesting flavor to an already interesting game.

aik_gbg_04Looking at each teams top 5 goalscorers it is clear that AIK’s impressive attack rely heavily on Henok Goitom. His 16 goals this season are pretty much in line with his xG of about 15 while Göteborgs Søren Rieks seems to be overperforming with his 10 goals equalling almost two times his xG numbers. Both teams have sold one of their best offensive players with Bahoui and Vibe both making a move abroad this summer.

What about a prediction then? While I won’t reveal any percentages for this (or any) game, what I can say is that my model is pretty much in tune with the betting market. AIK is a slight favourite due to their home advantage, but this is really anybody’s game and it will hopefully be highly entertaining.

Preview: AIK vs. IFK Göteborg

The Model part 2 – The Data

In my last post I discussed the concept of Expected Goals and how its probabilistic nature opens up for simulations. Today I’m going to talk about another cornerstone when building my model – the data. I do this because I think it’s important to fully explore the data when building a model, to understand its strengths and weaknesses, its advantages and limitations and how these affect the model and its output and performance. No model is perfect, but if we’re aware of its biases and limitations we can still make good use of it.

While Opta produces very advanced data covering every on ball event in the bigger leagues, the data available for Swedish football is lesser in terms of detail, quality and reliability. What’s available for use is pretty much just shots, and there is no distinction between different types of shots besides penalties. Only shots that ended up as goals have detailed information on whether it was headed, came from a set piece and so on. Using this information would result in a skewed model, rating for example headers too high since every existing header is also a goal. I’ve therefore treated all these types of situations as regular shots. Furthermore the location of the shots is recorded with less accuracy than Opta’s. The x and y coordinates are recorded with only integers, making them less precise and the location of the shots is sometimes plain wrong. I regularly examine the shot maps of games I’ve watched live and there always seems to be some errors, but I’m hoping these will be insignificant. There’s no information on passes, defensive actions or anything like that, the only events recorded besides shots is fouls, corners, offsides, substitutions and cards.

Data exists for the top league Allsvenskan, but also second tier Superettan and the two Division 1 leagues below it, from season 2011 and onwards. However, the data from Division 1 seems to be of too poor quality for modelling and substitutions were not recorded properly until season 2013, so per90 stats from seasons 2011 and 2012 are pretty much useless. Anyway, here’s a shot map of every shot recorded for Allsvenskan and Superettan from season 2011 up till now.

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With so many shots taken from the exact same locations, it’s probably easier to get a sense of the distribution of the shots through a hexbin plot, showing what could be described as the shot density of every location on the pitch:

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As we can see, the penalty box and the area just in front of it seems to be the most frequent shooting locations, which makes sense. Also, the penalty spot stands out with so many shots taken from the exact same location.

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Looking at only goals, the penalty spot again stands out but we can also see that most goals are scored inside the box, especially from more central locations. This again makes sense.

It’s also a good idea to take a look at the general characteristics of the games you want to model, so I’ve created some histograms of goal and shot distributions from Allsvenskan.

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Examening these, we can see that an average game ends up with a total of 2.74 goals, with the home side having a 0.433 goal advantage. What about shots?

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As expected, the home side also enjoy an advantage when it comes to shots, about 2.481 on average, while the average total number of shots in an Allsvenskan game is 21.931.

I think we have a good sense of the league and games we want to model now, so I’ll end this post here. Next up I’ll get down to business, building the model and putting it to the test.

The Model part 2 – The Data

Norrköping vs. Djurgården

Though I had planned not to share any shot maps before I had discussed the concept of Expected Goals and my model properly first, after watching last night’s game between Norrköping and Djurgården I just couldn’t help myself. Now some of you probably have seen this kind of plot before so I will leave out the explanation for later posts.

Peking-DIF_2015Here’s last night’s high-scoring game. Being a lifelong Djurgården supporter this was not a pleasant game to watch, and to add to the pain I actually had a bet on the under here. Sigh.

While I don’t believe much in year to year trends such as Team A vs. Team B always produces a lot of goals in today’s modern football where players and managers change teams frequently, watching the game I got a vague feeling of déjà vu and just had to look up this fixture from the last few years.

Peking-DIF_2014Peking-DIF_2013Looking at the games from seasons 2014 and 2013, it seemed there is some truth to the myth. But while it may look like this particular fixture usually end up a high scoring affair, in the other seasons in my database (2011 and 2012), the games ended 2-1 and 1-1 respectively. Furthermore, Djurgården actually only had two players starting in all three games: Kenneth Høie and Emil Bergström. The same goes for Norrköping with only David Mitov Nilsson and Andreas Johansson starting all three games.

With so few players playing all three games and the games therefore being played under completely different preconditions, I think we can safely put this high-scoring trend down to pure coincidence. I still feel like a fool for betting the under though.

Norrköping vs. Djurgården