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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