EGMAYO, An Injury Impact Metric

Different injuries have different impacts. In this article I am going to look at how historical injuries have affected teams from the perspective of expected goals. Given each squad member’s xG per 90, and the number of games they missed, what’s the total amount of xG that was sidelined in a season?

I call this metric EGMAYOExpected Goals Missed due to the Absence of Your Offence. Here are the top 10 EPL seasons by EGMAYO:

Season Team EGMAYO
2014 Arsenal 26.9
2010 Arsenal 23.3
2013 Arsenal 22.9
2012 Manchester City 19.4
2014 Liverpool 17.7
2013 Manchester City 17.4
2014 Manchester City 17.2
2014 Newcastle United 15.0
2011 Manchester United 14.8
2012 Manchester United 14.2

This indicates it’s not necessarily overly dramatic to point out that Arsenal’s injuries have had a big impact. Their lowest EGMAYO season was 2012, scoring 7.1, against an overall EPL average since 2010 of 6.7. Man City were title runners-up in their worst EGMAYO season:

Season Team Player Games Chance Quality per 90 Chance Quality missed
2012 Manchester City Jack Rodwell 18 0.36 6.42
2012 Manchester City Sergio Agüero 7 0.45 3.18
2012 Manchester City Micah Richards 22 0.12 2.67
2012 Manchester City Maicon 16 0.15 2.43
2012 Manchester City Mario Balotelli 4 0.53 2.12
2012 Manchester City David Silva 3 0.23 0.69
2012 Manchester City Aleksandar Kolarov 6 0.10 0.57
2012 Manchester City Vincent Kompany 7 0.06 0.42
2012 Manchester City Samir Nasri 2 0.16 0.32
2012 Manchester City Javi García 3 0.09 0.28
2012 Manchester City James Milner 2 0.12 0.23
2012 Manchester City Pablo Zabaleta 1 0.08 0.08
2012 Manchester City Joleon Lescott 2 0.02 0.03

Obviously it’d be far more interesting if we could better capture Vincent Kompany’s 7 game absence from City’s back line, or David Silva’s expected assists missed in his 3 games, but we’re not there yet, which brings me to:

Caveats

Sometimes my kids go up to a box of toys and just empty it onto the floor, play briefly with a couple of things, and then bog off to let mummy and daddy deal with it. Perhaps I haven’t made this abundantly clear, but this is very much my approach to football stats. I enjoy cutting data up, throwing it haphazardly on the floor, and seeing what it looks like, especially to other people. I intend to return to this later to clean up, but I’d like to make a few things clear:

  • This metric takes no account of the squad members that come in and replace injured players. Obviously these replacements have their own output in terms of xG, which may even exceed the injured player. Ideally, we would capture all of this in a similar way to Chad Murphy’s model, or even in more detail to capture the strength of schedule faced during each injury.
  • It takes no account of the importance of midfielders, defenders or goalkeepers. It’s only interested in the xG per 90 of a injured players, and therefore is weighted heavily in favour of strikers. I’m merely using it as one way to look beyond raw injury stats, I’m not saying it’s the final destination.
  • The EGMAYO calculation uses the same season as the injury for xG per 90, so players injured early on, or starting the season injured, aren’t measured particularly accurately.

So, I know all that, don’t point it out – I’m working on it. I just want to get this up for discussion’s sake, because it adds more context to articles like this in the Telegraph today. Comments welcome here, or on Twitter.

EGMAYO, An Injury Impact Metric

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