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August 3, 2016

Unpacking Packing

By Bobby Gardiner

packing

You’ve probably heard of Packing data by now. If you haven’t, it may be worth having a look at Raphael Honigstein’s primer. In effect, Packing is assigning a value to the amount of opposition players ‘taken out of the game’ by a pass or dribble.

Its popularity comes predominantly down to its intuitiveness. Where more complicated metrics can alienate in their codification and complicated statistical methods, this is a fairly comprehensible way of coding something in football terms. That has value, especially when it comes to selling to football people.

Nonetheless, there has been an obvious question to those with experience with event data. Is Packing really an upgrade? Put more articulately: Does Packing provide significant additional value over what we already have?

For Euro 2016 data that I kindly received a sample of,  I found around 70% of the variation in ‘average outplayed opponents’, effectively the mean amount of times a player ‘takes out’ other players per 90, could be explained in a linear regression model with only forward passes and successful dribbles per 90 as inputs. These aren’t complicated or unintuitive metrics, rough proxies for ‘verticality’, and yet they can explain a significant portion of the Packing numbers. That isn’t great.

Another version of Packing looks only at the amount of defenders taken out by players. Using another regression model with only passes and dribbles in the box per 90, 50% of the variation in these player values can be explained. Again, these are really simple metrics explaining a lot of what is meant to be unique insight.

With event data, it is sometimes forgotten that although something is not explicitly measured, this doesn’t mean that it is totally ignored. When players are passing forwards or successfully dribbling, they are implicitly taking players out of the game. Another example is that although Opta shot data doesn’t include defensive pressure, there is an implicit relationship between how close you are to the goal and the likely amount of pressure you face to take a shot; caveats, like whether or not an attack is a counter attack, also carry implicit connotations about the amount of pressure faced.

Any metric being marketed as a one-hit solution to football analytics is going to be a disappointment, as they can’t really exist in a game this complicated. Packing may be a useful part of quantifying player and team verticality, but I would be hesitant to call it much more than that, and it isn’t really anything that couldn’t be done with, say, Opta’s event data. To be fair to them, I know nothing about their pricing system, and it may be that clubs who want to only gain specific insights to complement coaching styles prefer to buy into something like this than an event data subscription.

I do also think that Packing and its popularity highlights a weakness in the analytics community’s current attempts to quantify stylistically past, say, goalscoring or chance creation. There is a lot of hype about positional data at the moment, but we are still nowhere done with analysing event data in unison with football theory to quantify tactical styles on a player and team level. What is the difference between how Sergio Busquets and Luka Modric play, and how are those differences useful in their respective team styles? These are questions that are perhaps more about efficient use of data to empirically answer football questions than actual statistical methods, but this is all part of what I believe to be the future of actionable analytics in the game.

Using the first regression model, I predicted Packing values p90 for the Premier League 15/16 season for players who played more than 10 90s. To clarify, this is players ranked by the predicted amount of opponents they ‘take out of the game’:

  1. Santi Cazorla
  2. Gianelli Imbula
  3. Yaya Toure
  4. Mousa Dembele
  5. Cesc Fabregas
  6. Ross Barkley
  7. David Silva
  8. Eden Hazard
  9. Bastian Schweinsteiger
  10. Alexis Sanchez
  11. Jordon Ibe
  12. Aaron Ramsey
  13. Mamadou Sakho
  14. Fernandinho
  15. Alex Oxlade-Chamberlain
  16. Michael Carrick
  17. Wilfried Zaha
  18. Manuel Lanzini
  19. Lucas Leiva
  20. Francis Coquelin

It’s notable that Mamadou Sakho is the only centre-back in the top 20, and this is because of his unusual rate of forward successful passes per 90. In the Euros list, Mats Hummels and Jerome Boateng ranked highly because they do this too.

Some interesting results there, and a decent first attempt at quantifying player ‘verticality’.

Is Packing the future of football analytics? Generously speaking, maybe in spirit, but probably not in practice.

Article by Bobby Gardiner