Month: December 2015

Expected Goals’ Greatest Partnerships

Thom Lawrence, , 2 Comments

I thought it would be fun to have a look at players that had great chemistry through the years. Specifically: which two players generated the highest average chance quality when one passed to the other to shoot?

Here’s the top 20 producers and consumers (10 shots assisted or more):

Producer Consumer Shots Chance Quality
Luis Suárez Daniel Sturridge 13 0.2253
Gregory Van der Wiel Zlatan Ibrahimovic 11 0.2138
Franck Ribéry Mario Mandzukic 17 0.2026
Luis Suárez Neymar 18 0.2015
Theo Walcott Olivier Giroud 14 0.2009
Pablo Zabaleta Edin Dzeko 11 0.2002
Theo Walcott Robin van Persie 23 0.1998
Lukasz Piszczek Robert Lewandowski 11 0.1975
Vieirinha Bas Dost 12 0.1957
Sofiane Feghouli Paco Alcácer 14 0.1934
Daniel Sturridge Luis Suárez 12 0.1895
Thomas Müller Robert Lewandowski 21 0.1883
Jonathan Biabiany Amauri 14 0.1861
David Alaba Thomas Müller 13 0.1859
Gonzalo Higuaín Cristiano Ronaldo 14 0.1850
Ryan Giggs Javier Hernández 16 0.1846
Marcel Schäfer Bas Dost 11 0.1829
Marcelo Karim Benzema 13 0.1824
Gareth Bale Cristiano Ronaldo 47 0.1821
Alexis Sánchez Lionel Messi 21 0.1816

But this is selfish – what about reciprocal relationships? These are the highest average pairings based on chance quality created for each other:

Partnership Shots Chance Quality
Luis Suárez Daniel Sturridge 25 0.2081
Alexis Sánchez Lionel Messi 35 0.1731
Thomas Müller Mario Mandzukic 22 0.1675
Gareth Bale Cristiano Ronaldo 61 0.1664
Luis Suárez Neymar 34 0.1657
Theo Walcott Robin van Persie 39 0.1607
Luis Suárez Lionel Messi 34 0.1574
Henrikh Mkhitaryan Pierre-Emerick Aubameyang 33 0.1546
De Marcos Aduriz 29 0.1512
Aaron Ramsey Olivier Giroud 27 0.1506
Sergio García Christian Stuani 43 0.1502
Cesc Fàbregas Alexis Sánchez 24 0.1462
Jérémy Menez Zlatan Ibrahimovic 31 0.1447
Lionel Messi Pedro 53 0.1433
Karim Benzema Cristiano Ronaldo 93 0.1418
Juan Mata Fernando Torres 40 0.1414
Gareth Bale Karim Benzema 36 0.1396
Mario Götze Robert Lewandowski 41 0.1394
José Callejón Gonzalo Higuaín 38 0.1385
Raheem Sterling Luis Suárez 38 0.1383

The lesson you should take away from this? Even ignoring the biting and racist abuse, you really want Luis Suárez on your side.

Expected Goals’ Greatest Partnerships

The Case of the Missing Throughball, and Other Mysteries

Thom Lawrence, , Leave a comment

Ben Torvaney noted last night that the number of throughballs per game looks like it’s been going down. It’s a pretty pronounced trend:

Count Completed Completion %
English Premier League 4450 1717 39%
2012 1655 596 36%
2013 1286 496 39%
2014 1132 460 41%
2015 377 165 44%
French Ligue 1 3738 1534 41%
2012 1605 593 37%
2013 942 378 40%
2014 825 411 50%
2015 366 152 42%
German Bundesliga 2333 1309 56%
2012 1115 550 49%
2013 715 432 60%
2014 401 254 63%
2015 102 73 72%
Italian Serie A 4985 2114 42%
2012 2789 1123 40%
2013 971 478 49%
2014 943 400 42%
2015 282 113 40%
Spanish La Liga 5601 2010 36%
2012 2146 745 35%
2013 1478 549 37%
2014 1559 557 36%
2015 418 159 38%
UEFA Champions League 1913 801 42%
2012 713 270 38%
2013 458 203 44%
2014 479 211 44%
2015 263 117 44%

If you were to take this at face value, it would be a hugely significant result: throughballs create high quality chances, and in the space of three or four years, defences appear to have discovered how to suppress them.

That’s obviously possible, but I strongly suspect that this is an issue with the way the data is being created. This is probably one of those things that you’re not supposed to talk about, and I don’t want to bite the hand that feeds this blog, so I hope the powers that be will consider this a good-faith bug report, and not the whining of an uppity lamprey complaining about the quality of the scraps it feeds off. Either way,  I caution you to look at any conclusions you make about a team or player’s output based on their number of throughballs over the last few years.

Just so we’re all on the same page, here’s the official definition of a throughball, which by all accounts has remained constant:

A throughball is a pass event which splits the defensive line, creating an attacking opportunity.

It’s difficult for us to confirm one way or another that a pass ‘splits the defensive line’ without watching every game. One thing we can notice from the table above is that conversion rates in some leagues seem to be going up. Perhaps that’s our first clue – is the ‘creating an attacking opportunity’ part being more strictly enforced? Perhaps failed throughballs are less likely to be throughballs.

Another clue is if you look at the next match event after a pass tagged as a throughball:

Season Clearance Interception Keeper Pass Shot
2012 10.13% 14.64% 22.58% 23.06% 13.81%
2013 7.79% 12.37% 26.83% 19.76% 17.25%
2014 8.71% 12.72% 27.17% 18.05% 18.20%
2015 6.80% 13.77% 27.27% 17.87% 20.13%

I’ve included only the types of events that seem to show a change. There are some interesting trends here:

  • Clearances have dropped as a proportion of next events. This backs up the theory that unsuccessful throughballs aren’t as likely to be tagged as such.
  • That said, interceptions have remained steady as a next event, however.
  • Balls that make it through to the keeper have increased somewhat as a proportion, up 5 percentage points from 2012-2014.
  • Throughballs that then set up a another pass have seen a big decline. Perhaps the interpretation of ‘creating an attacking opportunity’ doesn’t cover moves that aren’t as direct.
  • Shots have seen the biggest proportional rise from 2012-2014, which backs up the previous statement – the definition of throughballs seems to be increasingly focused on direct attacks.

There are possible footballing explanations for each of these trends. Maybe the Manuel Neuer effect has taken hold on goalkeeping across the leagues, keepers are pushing up and claiming the ball more, and that explains the increase in keeper touches after throughballs, for example. But overall, taking the absolute numbers, and examining some of the wider context, I’m suspicious.

If anyone can shed any light on the numbers, or has a genuinely persuasive argument that tactics have changed over the last few years, I’m all ears.

The Case of the Missing Throughball, and Other Mysteries

EGMAYO, An Injury Impact Metric

Thom Lawrence, , , , Leave a comment

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 EGMAYO: Expected 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

101 Weird Injury Stats

Thom Lawrence, , , Leave a comment

Everybody likes lists, and I’ve become interested in injury data, so today I’m going to attempt to give you ONE-HUNDRED AND ONE FORTY-THREE unbelievable and fascinating injury stats!

Some caveats before we begin: I only have access to data that’s public on the web, there’s a lot of junk so in some cases I’ve disregarded data that doesn’t seem to add up, and I’ve avoided including career-ending (or indeed life-ending) injuries and illnesses in individual stats, although they will show up in aggregate stats.

I should also point out that I am not a doctor, so I am not going to attempt to group injuries together in sensible ways beyond what’s absolutely obvious. I literally do not know what the knee bone is connected to. Do knees even have bones? What are bones? I don’t know, I only have data and find the human body disgusting. Let’s move on:

  1. Most injuries suffered by a player: 41, Franck Ribéry
  2. Most injuries suffered by a team: 409, Werder Bremen
  3. Longest individual injury: 1064 days, Shaun Barker
  4. Most days spent injured: 1568, Tufan Tosunoğlu
  5. Most days lost by a team to injuries: 15482, Werder Bremen
  6. Most injured body part: Knee, 2694+
  7. Shortest average injury: contused laceration, 4 days
  8. Longest average injury: fractured tibia and fibula, 246 days
  9. Shortest recovery time from fractured tibia and fibula: 44 days, Jan Fitschen
  10. Shortest recovery time from fractured tibia and fibula that I can check by Googling: 128 days, Neil McCann
  11. Longest recovery time from fractured tibia and fibula: 807 days, Christian Muller
  12. Most games lost in total across all leagues to an injury: 34366, Cruciate ligament rupture
  13. Most recurrences of same injury: 10, Tim Petersen, Knee injury
  14. Most different types of injury suffered: 33, Sven Bender
  15. Number of times I’ve got worried I’m not going to get to 101: 1
  16. Most injuries by league since 2012/13: 1081, German Bundesliga
  17. Least injuries by league: 231, French Ligue 1
  18. Most games lost to injury by league: 8954, English Premier League
  19. Least games lost to injury by league: 1569, French Ligue 1
  20. Most days lost to injury by league: 58457, English Premier League
  21. Least days lost to injury by league: 10201, French Ligue 1
  22. Most injuries suffered by a team in a single season: 79, Werder Bremen, 2008/9
  23. Most games lost to injury by a team in a single season: 294, Werder Bremen, 2008/9
  24. Least games lost to injury by a team in a single season: 6, Montpellier, 2011 (not entirely sure I trust the data)
  25. Most injuries suffered by an EPL team: 266, Arsenal
  26. Most games lost to injury by an EPL team: 2184, Arsenal
  27. Most games missed by an EPL player: 250, Abou Diaby
  28. Most different players injured: 85, Arminia Bielefeld
  29. Most different players injured in a single season: 30, AC Milan, 2011/12
  30. Most different players suffering same injury: 28, Austria Vienna, Illness
  31. Biggest outbreak of illness or flu at a club: 11 players, Austria Vienna, 2009
  32. Most different players suffering same injury in a single season: 13, Dundee Utd, Knee injury, 2014/15
  33. Most seasons a team has experienced the same injury: 10, Arsenal, Thigh problems
  34. Number of players relapsing within 1 game: 32, e.g. Vincent Kompany
  35. Longest time between relapses: 2732 days, Leighton Baines, Malleolar injury, 2007 & 2015
  36. Number of people who believed I would actually be able to come up with 101 of these: 0
  37. Fewest games missed for a title-winner: 6, Manchester City, 2011/12
  38. Most games missed for a title-winner: 351, Bayern Munich, 2014/15
  39. Most games missed or a relegated team: 318, Queens Park Rangers, 2014/15
  40. Fewest games missed for a relegated team: 40, Blackburn rovers, 2011/12
  41. Highest coefficient of variation among injury layoffs (10 or more incidences): 302.5%, Pneumonia
  42. Lowest coefficient of variation among injury layoffs:  37.8%, Cruciate ligament surgery
  43. R2 of career games missed to challenges (tackles, aerials, take ons): 0.0128

Okay, so, ran out of steam a bit and I think I’ve tweaked my anterior SQL ligament. If you are not satiated and have any particular stat requests, just ask on Twitter. I will of course be attempting to do some more serious work with this stuff in the coming weeks, but I just wanted to see what the data look like.

In the meantime, sort yourselves out Arsenal and Werder Bremen, you need to learn what serious pain is.

101 Weird Injury Stats