Zlatan Ibrahimovic and the Importance of Balance: The Zlindex~!

With my transfer evaluator finished, and no real interesting transfer rumors the last few days, I wanted to play with the algorithm and see how many points each EPL team would gain by signing Zlatan Ibrahimovic as a replacement for their main striker. Borrowing from Dirty Tackle‘s love of Zlatan, I named it the Zlindex~!

I applied my SVM to each of the 20 EPL teams, figuring how many points they would be expected to earn over the EPL season. Then I removed their main striker’s stats 1 and substituted  Zlatan’s statistics. I added his stats into the team stats, and re-calculated the results, Finally, I subtracted the expected points from the regular striker from the expected points if Zlatan was the team’s striker to calculate his added value.

Expected Points(Zlatan) – Expected Points(Regular Striker)

The unsurprising news is that Zlatan would improve 16/20 EPL teams, and would improve West Ham by somewhere around 7-8 points (enough to let them challenge for a Europa League spot according to my predictions). He’d also be a fairly significant upgrade for most of the top 6 teams.2 The full table is in the figure below.



However, as Brooks Peck pointed out, it doesn’t make sense that he doesn’t improve the teams at the bottom.3 My first hypothesis is that these are teams that would suffer if they had too many karate kicks and ponytail related assaults, but the model doesn’t account for those so I’m probably wrong there. I did some quick exploration of the data, and found a consistent pattern for three of the four teams (Man City, Norwich City, and Crystal Palace). He doesn’t tackle as much as their current striker, he takes too many shots outside the area and too few inside the area.4

Zlatan Comparison


The two that stood out to me the most were “tackles” and “Shots inside the area”, and those are the two that seem to correlate most highly with points lost. Interestingly, this also fits what I see as a bigger pattern for Zlatan, having watched him a lot when he was with Milan5: he’s often lazy and uninterested on defense, and takes a lot of odd shots outside the area. To his credit, he can make those long distance shots work as well as anyone, but most teams prefer their striker operating a little closer to goal.

Newcastle United still remains a mystery to me – looking at the data for Papiss Demba Cisse, the big area where Zlatan differs is in passing: he passes the ball quite a lot more than most of the strikers in this list, and that may make the model think negatively of him. He might look more like a #10 than a #9, which fits the deep-lying forward style he was used in at Milan (holding up the ball, transitioning from defense to attack). This may be a latent variable the numbers are measuring, and that his style doesn’t fit the few teams he wouldn’t improve.

The important lesson here I think is balance: not every player’s style improves every team. Zlatan is one of the best pure strikers on the planet, but he’s a tall, strong, physical striker who can wear down defenders as good as anyone out there. This doesn’t necessarily fit with what teams are looking for, and even some mid-table teams wouldn’t benefit from his addition to the squad.6

  1. In cases where teams play with two strikers, I picked one at random
  2. Re: Chelsea, he’s basically breakeven with Costa, but is a big upgrade over Pedro
  3. Brooks pointed out that it’s not necessarily surprising Zlatan is a downgrade over Sergio Aguero for Man City, and I’d agree. I’ve been a fan of his since before he was at City because he won several Golden Boots for me in an FM2012 save
  4. The method here is fairly simple: I took the team’s current striker’s stats and subtracted Zlatan’s stats to see the difference between the two
  5. Forza Milan~!
  6. Someone mentioned on Twitter that teams can change styles based on new players, which is a real possibility the model can’t account for, but that leads to other issues in terms of team chemistry and whatnot so I’m not too concerned about that honestly

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