Wednesday, 2 April 2014

Player Impact on Expected Goals Difference

I'm pretty happy with 'Expected Goals Difference' (xGD) as a underlying measure of a team's ability. I used it in my last post to compare Man United's season this year to last. StatsBomb have done a similar thing with West Ham today, successfully debunking the Carroll effect on the Hammers apparent resurgence. I also found that xGD was amongst the best forecaster of future success (points and goals) in some testing I did a few weeks back.

A logical next step for me is to see how individual players impact a team's xGD. Goal Impact do this already with real goals instead of expected goals. They do an absolute incredible job, tracking the impact on their team's goal difference for players around the world right from their earliest youth games. Their system looks very robust indeed. I'm sure they've got some clever stuff going behind the scenes but it's a simple premise: A top down model that doesn't care for the actions a player does on the pitch, but just the output of those actions. Whatever it is, whether it's successful passing, dribbling, tackling, interceptions -  if it leads to or prevents goals for their team then it counts. 

Below is my first stab at the same principle but using expected goals - quality and quantity of chances created and conceded. I am in no position to replicate what Goal Impact have done with their system. They track goal impact and playing time by the minute so can accurately evaluate subs appearances, they have data from numerous seasons from which to correlate plus/minus effect on a team with and without a player. I've had to use a >60 minute playing time cut-off and for now have just processed this season's data. But it's a start. 

With low games played for some players compared to others I have normalised each team's xGD in each fixture against the typical result expected against an opposition. E.G. If Arsenal play Aston Villa at home I'd expect them to achieve an xGD of +0.4 if they performed at an average level for the league. If Arsenal achieved +1.0 then their normalised result would be +0.6.

In the table you'll see I've also included actual goal difference (GD) as a comparison with expected goals, again normalised against opposition. First here's a look at how xGD and GD correlate for players with >1000 minutes played this season.

A pretty healthy and reassuring relationship. That the correlation is 'looser' around the zero marks is a perhaps a useful insight into the advantage of using expected goals over goals. When teams aren't that good scoring is rarer and hence measuring anything/nothing becomes difficult. The best example of this is when a teams score 0 goals. xG's measure of chances created give a better indication of which teams/players were actually the better team.

On to the table then. You'll also see I've included a '+Team' value for both xGD and GD. This is the player's plus or minus difference from their team average. This does produce some wacky result with a lot of players on the worst teams looking strong, particularly the xGD version. There's clearly some sample size issues with this, as there is with the data in general based on just 31 available games. I'm not going to make any comment on the players and values in the table at this time. I'll follow up with some remarks in a separate post at a later date. Suffice to say, there's some interesting stuff thrown out. As usual this table is sortable and searchable. Click on a column header to sort. Type a team code (e.g. 'LIV') in the Search box to search. By default the table is sorted by xGD.


PlayerTeamMins>60 MP0-59 MP0 MPxGD+Team (xGD)GD+Team (GD)
1NavasMCI1510131341.220.282.200.67
2ClichyMCI1212132151.210.282.210.68
3DemichelisMCI1530170101.170.241.980.44
4KompanyMCI1463162121.110.171.610.04
5NegredoMCI159716951.050.111.630.07
6AgueroMCI1191134131.020.071.940.39
7FernandinhoMCI199322171.000.062.130.60
8SilvaMCI1581190110.990.051.860.31
9AllenLIV98298140.970.352.290.85
10FlanaganLIV1226141140.960.351.930.46
11ZabaletaMCI218024150.950.011.830.28
12CoutinhoLIV183119660.950.341.740.26
13ToureMCI232427030.940.001.690.13
14NasriMCI162919560.91-0.041.840.29
15MikelCHE903813100.840.210.71-0.57
16DzekoMCI1253111090.82-0.141.20-0.39
17HartMCI189021090.78-0.181.34-0.24
18NastasicMCI1029103170.77-0.181.25-0.34
19KolarovMCI150015870.74-0.221.21-0.38
20Eto'oCHE118014590.730.101.640.43
21SuarezLIV224325060.730.111.570.08
22SterlingLIV159616960.720.101.730.25
23SchürrleCHE94081580.720.081.900.71
24MilnerMCI92381480.71-0.240.39-1.25
25CissokhoLIV1044113160.710.081.630.13
26CahillCHE216024070.690.061.470.25
27ColeCHE1103130180.690.050.84-0.42
28AggerLIV1141124150.680.061.710.22
29JohnsonLIV1796201100.680.051.46-0.04
30TerryCHE261029020.660.021.19-0.05
31TorresCHE126113990.650.021.19-0.05
32IvanovicCHE254928120.650.021.12-0.13
33HazardCHE263530100.640.011.23-0.01
34CechCHE279031000.640.001.23-0.01
35SkrtelLIV250128030.630.001.44-0.06
36AzpilicuetaCHE178520380.62-0.021.560.35
37MignoletLIV270030010.62-0.011.40-0.11
38HendersonLIV267830010.62-0.011.40-0.11
39RamiresCHE247227130.61-0.030.90-0.36
40OscarCHE189820920.61-0.031.23-0.01
41SakhoLIV1028121150.60-0.030.99-0.55
42WelbeckMAN1197119110.550.250.20-0.21
43Da SilvaMAN1278134140.550.250.580.19
44WalcottARS86094180.530.230.41-0.28
45VidicMAN1655182110.520.220.12-0.30
46FlaminiARS122312980.510.220.66-0.01
47SturridgeLIV175720290.51-0.131.45-0.05
48GerrardLIV219624250.50-0.141.35-0.16
49Luiz RosaCHE1249142150.50-0.141.480.26
50LucasLIV1556173110.49-0.150.89-0.65
51RamseyARS1492162130.490.200.920.27
52LampardCHE144414890.49-0.160.96-0.29
53PienaarEVE161018490.430.200.570.01
54ValenciaMAN162017680.430.130.570.18
55WillianCHE129914680.43-0.211.13-0.12
56ToureLIV1437154120.42-0.220.99-0.54
57GibbsARS178619660.410.121.160.52
58OsmanEVE1676161320.400.170.740.20
59YoungMAN82988150.400.090.28-0.13
60SigurdssonTOT999109120.390.25-0.15-0.27
61MataMAN1509100210.380.070.460.07
62RosickyARS1133128110.360.070.32-0.37
63CarrickMAN187321190.360.060.36-0.04
64TownsendTOT1171128110.360.210.190.10
65SoldadoTOT180320560.360.220.210.12
66ShawSOU237125330.350.110.370.18
67GiroudARS241827130.340.040.700.03
68ClyneSOU1478146110.340.090.710.54
69RodriguezSOU245027400.330.090.11-0.10
70OzilARS182821160.310.020.940.29
71De GeaMAN270030010.310.000.410.01
72CisseNEW1022109120.310.23-0.67-0.80
73RoseTOT1296141160.300.160.540.47
74BorucSOU212723170.300.060.330.13
75EvraMAN245727130.30-0.010.490.10
76SzczesnyARS270030010.29-0.010.62-0.05
77CazorlaARS198122270.29-0.010.66-0.01
78JagielkaEVE216024070.280.040.48-0.09
79FonteSOU255228120.270.030.220.02
80MyhillWBA1260140160.260.17-0.59-0.13
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82MertesackerARS252028030.26-0.040.64-0.03
83JanuzajMAN133014890.26-0.060.450.06
84BainesEVE202922180.260.010.25-0.33
85SessegnonWBA1407164100.260.16-0.300.17
86van PersieMAN1461171130.25-0.060.670.29
87ColemanEVE241827040.250.010.660.10
88DavisSOU189820740.250.010.330.13
89VertonghenTOT198022090.250.10-0.04-0.15
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95AnelkaWBA850102180.240.14-0.260.22
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110JonesMAN1610172120.19-0.120.820.45
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112ArtetaARS171519490.18-0.120.62-0.06
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115WanyamaSOU1296135130.18-0.070.350.15
116ChadliTOT1066118120.170.02-0.24-0.36
117BarkleyEVE175417950.17-0.070.48-0.09
118YacobWBA176120460.170.08-0.64-0.19
119CleverleyMAN1463155110.17-0.150.36-0.04
120ColocciniNEW1684190120.170.090.130.05
121WalkerTOT229725150.160.01-0.22-0.34
122LukakuEVE179419570.16-0.090.920.39
123JonesWBA1558163110.160.06-0.58-0.12
124CanasSWA166719480.150.21-0.44-0.09
125ChambersSOU1384144130.14-0.11-0.33-0.58
126MirallasEVE203423530.14-0.110.33-0.25
127BarryEVE216024070.14-0.110.630.08
128MorrisonWHM1154124150.140.56-0.66-0.49
129OlssonWBA229025140.130.03-0.420.05
130GouffranNEW193220740.120.040.490.45
131McAuleyWBA240827030.120.03-0.50-0.04
132DawsonTOT250128030.11-0.05-0.21-0.33
133WilliamsonNEW219224160.110.020.170.10
134SissokoNEW259428210.100.020.01-0.08
135VormSWA1710190120.100.15-0.070.30
136RidgewellWBA250827210.08-0.02-0.320.15
137KrulNEW270030010.08-0.01-0.01-0.09
138BruntWBA170319380.07-0.03-0.66-0.21
139LlorisTOT270030010.07-0.10-0.01-0.12
140Ben ArfaNEW1186111280.07-0.02-0.59-0.72
141MulumbuWBA241926310.06-0.04-0.63-0.17
142ReidWBA944101190.06-0.04-0.65-0.19
143DembeleTOT176317950.06-0.100.430.35
144DaviesSWA230025150.050.10-0.320.04
145RoutledgeSWA198322540.050.10-0.190.17
146AnichebeWBA10511010100.04-0.06-1.38-0.97
147HernándezSWA11081010110.040.09-0.290.06
148DebuchyNEW215724160.04-0.050.03-0.05
149MonrealARS835811120.04-0.260.23-0.47
150RemyNEW186622180.02-0.070.630.60
151BerahinoWBA99991560.02-0.080.420.94
152ReidWHM1048114160.010.42-0.120.09
153EvansMAN1315142150.01-0.320.30-0.10
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157SantonNEW20832326-0.02-0.110.310.25
158HuthSTO106612019-0.020.15-0.12-0.08
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160AmeobiNEW92291012-0.02-0.110.00-0.08
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162Yanga-MbiwaNEW134313612-0.03-0.120.420.36
163TetteyNOR114813315-0.030.36-0.77-0.19
164AdebayorTOT126514116-0.04-0.200.310.22
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166FosterWBA142816014-0.04-0.15-0.430.04
167LennonTOT168820110-0.05-0.21-0.11-0.22
168ChicoSWA24042704-0.06-0.01-0.270.08
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