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May 15, 2014

Are some Goalkeepers better at saving shots than others?

By Colin Trainor

This post has been written by Dan Kennett.

In the last year, this site has led the way on Goalkeeping analytics.   In that time the focus has become more on “Expected Saves” rather than the humble old Save% (Sv%) i.e. /

Following a chance discovery on the NBC Sports website (example here), it’s been possible to quickly collect Sv% data going back to 2007/08 for England & Germany and 2008/09 for Italy & Spain, resulting in almost 48,000 Shots On Target with an average of 360 saves for 95 Goalkeepers (Individual Goalkeeper sample size is currently the main constraint with Goalkeeping models).

With this data it’s now possible to re-revisit the humble old Sv% and ask if some Goalkeepers are betting at saving shots than others over the course of their career. All Goalkeepers with > 100 saves were put into a funnel plot and the results are below:

GK_Funnel_v2

The purpose of the funnel plot is to show how randomness decreases as the sample size increases. In this case, as a Goalkeeper faces more Shots On Target. This is represented by the curved lines getting closer to the horizontal line.

 

  • The horizontal black line is the overall Sv% for the 48,000 shots (72.13%)
  • The curved black lines represent 2 standard errors above and below the mean

 

At the top centre of the chart there are 5 dots above the curved line that tally with the received wisdom of “good goalkeepers”: Gigi Buffon, Marc-Andre Ter Stegen, Petr Cech, Christian Abbiati and Manuel Neuer. Maybe Neuer genuinely does deserve his unofficial title of “world’s best Goalkeeper”?

Some love should also be shown for Tim Howard who has been consistently excellent for Everton over 7 seasons.

At the bottom centre of the chart there’s a cluster of 3 keepers from the Premier League who should set alarm bells ringing for fans: Brad Guzan, Boaz Myhill and Tim Krul. This statto will also be keeping close tabs on Ruben Ivan Martinez of Rayo Vallecano from now on!

 

 

Geek Notes

Sv% is normally distributed (p = 0.256)

There is a weak relationship between Sv% and SoT Faced (r = 0.345) but the p-value is very low (0.001)

We can rule out “league effects” as once goalkeepers from different leagues are grouped together and compared, there is no significant difference amongst the means (p=0.471)

Data

The following is a copy of the collated data:

GKCtrySavesOn TargetSv%
HitzGER1171540.75974026
BorucENG1061590.666666667
YoelSPA1121630.687116564
HeimerothGER1221810.674033149
Da CostaITA1261900.663157895
CarrizoITA1311900.689473684
NetoITA1441940.742268041
BetoSPA1342000.67
BardiITA1322040.647058824
PadelliITA1502140.700934579
StockdaleENG1502160.694444444
RafaelITA1592220.716216216
MannoneENG1732240.772321429
NavasSPA1822290.794759825
LlorisENG1552300.673913043
RubinhoITA1772420.731404959
PuggioniITA1772450.72244898
BenussiITA1662450.67755102
RiesgoSPA1642470.663967611
BizzarriITA1932490.775100402
RosatiITA1592500.636
AsenjoSPA1762510.701195219
BrkicITA1892570.73540856
FahrmannGER1942600.746153846
EstebanSPA1742710.642066421
CodinaSPA2193060.715686275
CasillaSPA2173080.704545455
MielitzGER2013120.644230769
GuaitaSPA2393300.724242424
CastellazziITA2373350.707462687
CourtoisSPA2513430.731778426
HarperENG2503460.722543353
HildebrandGER2373480.681034483
MullerGER2533500.722857143
WetkloGER2653570.742296919
RubenSPA2263580.631284916
KraftGER2483660.677595628
RobertoSPA2733700.737837838
KameniSPA2753790.725593668
GuzanENG2553800.671052632
De GeaENG2913860.75388601
PerinITA2733910.698209719
LenoGER2793930.709923664
PalopSPA2853960.71969697
VormENG3024190.720763723
VarasSPA2984330.688221709
SzczesneyENG3164430.713318284
MyhillENG3054580.665938865
SorensenENG3394650.729032258
RuddyENG3264690.695095949
StarkeGER3414740.719409283
TrappGER3454760.724789916
ValdesSPA3735000.746
Ter StegenGER3895020.774900398
AndresSPA3405200.653846154
CaballeroSPA3775230.7208413
KrulENG3525260.669201521
MoyaSPA3785320.710526316
BuffonITA4305600.767857143
TonoSPA3945690.692442882
BegovicENG4355990.726210351
AbbiatiITA4746000.79
MignoletENG4366010.725457571
ZielerGER4286050.707438017
AgazziITA4296090.704433498
CasillasSPA4506260.71884984
GivenENG4746540.724770642
MarchettiITA4846660.726726727
De SanctisITA4926670.737631184
AndujarITA4776670.715142429
CurciITA4866980.696275072
FosterENG5137010.731811698
ConsigliITA5127030.728307255
BaumannGER5097050.721985816
AranzubiaSPA5257190.730180807
BravoSPA5077210.703190014
UlreichGER5177510.688415446
Diego AlvesSPA5717700.741558442
LopezSPA5547820.708439898
MiranteITA5587940.702770781
WeidenfellerGER6058100.74691358
DrobnyGER6228210.757612667
CechENG6608560.771028037
NeuerGER6708580.780885781
ReinaENG6809050.751381215
SchaferGER6529190.709466812
IraizozSPA6649320.712446352
HartENG7069360.754273504
AdlerGER6739430.713679745
HandanovicITA7029590.732012513
FriedelENG7139650.738860104
BenaglioGER72310010.722277722
SchwarzerENG76910270.748782863
HowardENG81410780.755102041
JaaskeleinenENG84311650.72360515

Article by Colin Trainor