Hamburger SV vs SC Paderborn 07: Goalimpact of Lineups

Given the starting players and the home field advantage, HSVis the clear favorite. However, Paderborn seems to have better players on the bench then on the field. Maybe they opted for experience and hence older players. This might be a bad choice.

Hamburger SV: 55.7%
Draw: 26.7%
SC Paderborn 07: 17.6%

The betting odds are similar.

Hamburger SV

PlayerGoalimpactPeak GIAgeLast National TeamNo. GamesNo. Minutes
Tolgay Arslan111.9116.324.0Türkei [U21]16712118
Valon Behrami108.3115.229.3Schweiz28122242
René Adler107.8112.029.6Deutschland28126257
Dennis Diekmeier99.9102.524.8Deutschland [U21]19316991
Milan Badelj127.2128.425.4Kroatien24520102
Johan Djourou115.1119.427.5Schweiz22218168
Pierre-Michel Lasogga118.5127.822.7Deutschland [U21]16612047
Ivo Ilicevic86.391.227.8Kroatien24115678
Rafael van der Vaart135.4155.331.5Niederlande52839980
Marcell Jansen89.395.728.8Deutschland30925669
Heiko Westermann94.5110.231.0Deutschland43939980
Average108.6115.827.5
Bench
Petr Jirácek101.7107.728.4Tschechien18312423
Zoltán Stieber96.897.225.8Ungarn14511250
Artjoms Rudnevs98.199.726.6Lettland14710753
Matthias Ostrzolek111.7115.524.2Deutschland [U21]15512424
Cleber101.0106.523.7302708
Jonathan Tah81.9122.718.5574945
Jaroslav Drobný89.189.334.8Tschechien Olymp.21319718
Average97.2105.526.0


SC Paderborn 07

PlayerGoalimpactPeak GIAgeLast National TeamNo. GamesNo. Minutes
Elias Kachunga113.2123.822.314610847
Lukas Kruse83.285.831.117816016
Patrick Ziegler99.0102.224.513711147
Jens Wemmer97.9104.328.819316491
Christian Strohdiek98.5100.026.512510553
Marvin Bakalorz103.7106.124.915611734
Mario Vrancic129.5131.225.2Deutschland [U20]18713529
Daniel Brückner81.0112.633.529322624
Süleyman Koç77.679.425.215511617
Uwe Hünemeier103.7109.928.625422430
Moritz Stoppelkamp87.592.127.722514489
Average97.7104.327.1
Bench
Idir Ouali97.598.126.218913583
Daniel Lück106.4144.623.3353108
Marvin Ducksch101.9123.220.4Deutschland [U17]986659
Stefan Kutschke115.6116.225.81497454
Lukas Rupp103.9109.723.61519426
Marc Vucinovic110.9111.225.8886797
Michael Heinloth115.7125.622.513811884
Average107.4118.423.9



Hamburger SV under different trainers

It is not an easy season for fans - and players - of the Hamburger SV. The results were worse than the already low expectation and with Mirko Slomka now already the fourth trainer tries to improve this. We will have a short look on what players they chose.

Thorsten Fink

Player #Games GoalImpact PlayerAge PeakGI
Rafael van der Vaart 100% 142.1 31.0 154.5
Milan Badelj 80% 128.1 24.9 131.1
Johan Djourou 40% 120.8 27.0 123.0
Maximilian Beister 100% 111.7 23.4 120.2
Jacques Zoua 100% 110.4 22.4 123.9
Heiko Westermann 100% 108.0 30.5 116.9
Petr Jirácek 20% 107.0 27.9 111.1
René Adler 100% 106.4 29.1 106.4
Artjoms Rudnevs 20% 103.7 26.1 103.8
Marcell Jansen 40% 100.4 28.3 104.9
Lasse Sobiech 80% 99.6 23.0 110.0
Zhi Gin Lam 60% 98.5 22.7 110.7
Dennis Diekmeier 100% 97.2 24.3 101.9
Hakan Çalhanoglu 40% 96.0 20.0 127.2
Tolgay Arslan 100% 95.4 23.5 103.6
Tomás Rincón 20% 94.7 26.1 94.8

Fink spent only the first five games of the season on the bench as trainer. He had 17 different players in the starting XI, but the red 9 of them were the backbone and played most of the time. He gained only 4 points in the five games despite a good average Goalimpact of 109.1 in his starting players. However, with hindsight, the results weren't that bad considering they lost against Dortmund, Schlake and Hertha that all play a strong season. Against Braunschweig they won and only the loss against Hoffenheim can maybe considered as below par, depending on your ambition.

Never the less, he was kicked and Rodolfo Cardoso took over as interim coach for one game. After that it was...

Bert van Marwijk

Bert stayed 16 games and failed to improve the table position with these players.

Player #Games GoalImpact PlayerAge PeakGI
Rafael van der Vaart 79% 142.1 31.0 154.5
Milan Badelj 100% 128.1 24.9 131.1
Johan Djourou 71% 120.8 27.0 123.0
Ola John 14% 115.0 21.7 132.7
Pierre-Michel Lasogga 86% 113.1 22.2 127.9
Maximilian Beister 57% 111.7 23.4 120.2
Jacques Zoua 36% 110.4 22.4 123.9
Heiko Westermann 79% 108.0 30.5 116.9
René Adler 71% 106.4 29.1 106.4
Marcell Jansen 93% 100.4 28.3 104.9
Lasse Sobiech 14% 99.6 23.0 110.0
Zhi Gin Lam 21% 98.5 22.7 110.7
Dennis Diekmeier 7% 97.2 24.3 101.9
Hakan Çalhanoglu 100% 96.0 20.0 127.2
Tolgay Arslan 79% 95.4 23.5 103.6
Tomás Rincón 36% 94.7 26.1 94.8
Michael Mancienne 7% 93.5 26.1 93.7
Ivo Ilicevic 14% 93.5 27.3 96.1
Ouasim Bouy 14% 91.4 20.7 117.4
Jaroslav Drobný 29% 87.8 34.3 87.8
Jonathan Tah 93% 75.0 18.0 129.4

In total 21 players were at least once in the starting during his time. Three players from the former backbone saw their playing time greatly reduced:
  • Jacques Zoua who was replaced mainly by Pierre-Michel Lasogga
  • Defenders Lasse Sobiech and Dennis Diekmeier. For them Marcell Jansen and the 17yo Jonathan Tah received their chances.
In total, van Marwijk's teams had a lower Goalimpact. They averaged only 106.5. However, apart from letting Tah play, this was hardly van Marwijk's choice. Rene Adler missed a few games injured and his replacement Drobny just isn't that good. Van der Vaart were also out some games due to injury, as was Maximilian Beister.

Mirko Slomka

It takes only one victory to become the new hope of HSV. Slomka won his first game against Dortmund with these 11 starting players.

Player #Games GoalImpact PlayerAge PeakGI
Milan Badelj 100% 128.1 24.9 131.1
Johan Djourou 100% 120.8 27.0 123.0
Pierre-Michel Lasogga 100% 113.1 22.2 127.9
Heiko Westermann 100% 108.0 30.5 116.9
Petr Jirácek 100% 107.0 27.9 111.1
René Adler 100% 106.4 29.1 106.4
Marcell Jansen 100% 100.4 28.3 104.9
Slobodan Rajković 100% 98.8 25.0 101.5
Hakan Çalhanoglu 100% 96.0 20.0 127.2
Tolgay Arslan 100% 95.4 23.5 103.6
Tomás Rincón 100% 94.7 26.1 94.8

That is closed to the optimal team according to Goalimpact, given that van der Vaart and Beister are injured. It had an average Goalimpact of 106.2, but that could raise to 110 if van der Vaart returns. Beister, unfortunately, will not return this season. Given the lower Goalimpact, the victory against Dortmund was also driven by luck, but with a 110-team the direct matches against other relegation candidates indeed should instill some hope.

Hamburger SV vs FC Schalke 04: Team Roster

Schalke is expected to win. The players are having higher quality. And both teams have some promising talents in their ranks. Watch them next season.

Hamburger SV

PlayerGoalimpactPeak GIAgeTeamNo. GamesNo. Minutes
Jaroslav Drobný88.888.834.2Tschechien Olymp.20719160
Heiko Westermann109.2117.530.4Deutschland42038397
Zhi Gin Lam98.2110.822.61078128
Jonathan Tah74.3131.717.9443844
Marcell Jansen101.3105.728.2Deutschland29424433
Hakan Çalhanoglu96.3128.419.9Türkei1169255
Ivo Ilicevic94.697.027.2Kroatien22013970
Milan Badelj128.7131.924.8Kroatien21817725
Tolgay Arslan96.3104.923.41097778
Rafael van der Vaart143.3155.230.9Niederlande50738395
Pierre-Michel Lasogga112.8128.022.1Deutschland [U21]1319193
Bench
Sven Neuhaus86.586.535.819918371
Dennis Diekmeier97.8102.824.2Deutschland [U21]15313388
Lasse Sobiech99.9110.722.9Deutschland [U21]1189721
Ouasim Bouy91.0117.620.6Niederlande [U19]281994
Petr Jirácek107.2111.127.8Tschechien16911461
Ola John115.8134.221.6Niederlande [U21]1116769
Jacques Zoua110.3124.122.3Kamerun1366899


FC Schalke 04

PlayerGoalimpactPeak GIAgeTeamNo. GamesNo. Minutes
Ralf Fährmann98.398.325.3968875
Felipe Santana112.8116.627.816612104
Sead Kolasinac108.8135.720.5Deutschland [U19]967141
Atsuto Uchida122.1122.725.8Japan22819758
Christian Fuchs107.2110.927.8Österreich37230686
Joel Matip108.9122.322.4Kamerun18314736
Max Meyer96.7146.918.3Deutschland [U17]694618
Kevin Prince Boateng110.5112.226.8Ghana27119512
Roman Neustädter129.1129.525.824219745
Jefferson Farfán130.0135.029.2Peru41033925
Klaas-Jan Huntelaar137.6145.930.4Niederlande39831673
Bench
Timo Hildebrand109.0109.034.8Deutschland42439528
Tim Hoogland109.0113.628.613810899
Kyriakos Papadopoulos110.4127.021.8Griechenland1239368
Marcel Sobottka84.9119.419.7847399
Leon Goretzka88.0131.418.9Deutschland [U21]715329
Chinedu Obasi111.6115.027.6Nigeria14510224
Ádám Szalai113.8113.926.1Ungarn15710808


Preview on the #Nordderby: HSV vs. Werder Bremen: Lineups and Stats


Hamburger SV

PlayerGoalimpactAgeNational TeamNo. GamesNo. Minutes
Rene Adler115.428.6Deutschland24723064
Heiko Westermann115.930.1Deutschland40937374
Johan Djourou129.426.6Schweiz19015363
Zhi Gin Lam99.322.2987601
Jonathan Tah98.117.6292452
Marcell Jansen110.527.8Deutschland27722852
Tomas Rincon96.025.6Venezuela1349535
Milan Badelj129.924.5Kroatien20316330
Petr Jiracek114.727.5Tschechien15911014
Maximilian Beister112.323.0Deutschland [U21]1479613
Rafael van der Vaart149.330.6Niederlande49637323
Bench
Jaroslav Drobny102.633.9Tschechien Olymp.20518974
Lasse Sobiech98.822.6Deutschland [U21]1139407
Hakan Calhanoglu107.519.6Türkei [U21]1007991
Tolgay Arslan96.323.0956625
Artjoms Rudnevs104.725.6Lettland1188929
Pierre-Michel Lasogga119.921.7Deutschland [U21]1167989
Jacques Zoua124.322.0Kamerun [U20]1256506


Werder Bremen

PlayerGoalimpactAgeNational TeamNo. GamesNo. Minutes
Sebastian Mielitz97.424.1Deutschland [U20]11510614
Santiago Garcia90.225.1796782
Luca Caldirola93.422.6Italien [U21]918038
Assani Lukimya102.327.622118356
Clemens Fritz119.232.7Deutschland39332689
Cedric Makiadi96.829.5DR Kongo28021628
Aaron Hunt113.427.0Deutschland [U21]30220920
Aleksandar Ignjovski90.722.6Serbien13811085
Eljero Elia108.826.5Niederlande25216417
Nils Petersen99.524.7Deutschland [U19]19813092
Martin Kobylanski88.819.5Polen [U19]573957
Bench
Raphael Wolf83.325.213712671
Sebastian Prödl103.426.2Österreich19716379
Theodor Gebre Selassie108.726.7Tschechien15013363
Mehmet Ekici106.423.4Türkei14810140
Felix Kroos92.122.51369033
Levent Aycicek108.419.6Deutschland [U17]221543
Julian von Haacke104.619.5483759


Prediction: Final Standings of Bundesliga 2013/2014

The fun part of analysis, at least to me, is to make predictions. Since the new season starts next week, I'll try to predict the final standings at the end of the season with my algorithm.

Most predictions algorithms out there are evaluating the teams' playing strength based on the performance in the previous seasons. As the team is the atomic structure in these, they can't take easily new transfers into account. Goalimpact is evaluating players and thus can, in principle, take team changes due to transfers into account. However, it causes other headaches. Most teams have 22 or more players to choose from, but some, often even many, of them will only get few minutes playing time in a season. A team's playing strength is mainly based on subset of the players, maybe 15 or 16 players.

If I'm going to predict team results without knowing the XI that actual plays, I have to guess the players that will be part of the game. In this case I even need to guess the players that will mostly influence a team over the whole season. This can get very subjective quickly. My usual way around this issue is to use minute weighed average values from past games. This works quite well during a season, but I can't calculate this before the season even started. All newly bought players obviously didn't get any playing time yet and thus would get a weight of zero. My prediction would be based on a distorted estimate of the team composition.

An alternative approach, I considered, was to use the starting eleven predicted by LigaInsider. They provide quite accurate predictions for each match day in Bundesliga. The predicted starting XI for Werder Bremen is for example.


However, this has some other disadvantages. The estimate is for the next match day only. It may or may not be a good prediction for the main XI of the entire season. The main XI will be vague to some extend that early in a season in any case. Probably even the trainer will not now for sure which players will get how much playing time over the season. They are likely to have a rough idea and the have their core of six to eight players fix, but too many things are not projectable. So even though LigaInsider is doing a great job, they can't possibly be correct, independently of which XI they pick. Actually they don't even try this. As they pick the likely players for the next match only, some players are excluded because they suffer from a minor illness. Maybe a prediction for the XI of the season would still include them.

To get around the need to pick players, in the following prediction, I just use the average of all players that have been nominated for the first team as of now. Doing so, will cause a downward bias in the estimates of the team's Goalimpacts. This stems from the fact that the players actually playing in most cases are the players with higher Goalimpacts. The hope would be that the bias is about equal for all teams, but this is not the case. Some teams have a strong core team, but less strong players otherwise. Some teams, in contrast, have rather evenly distributed Goalimpacts over all 22 players. So, unfortunately, I'll have a bias due to this averaging, but I think it is still the best way to avoid introducing arbitrary selections of players. And, I admit, It has the charm of being easily done.

So this is the table with the predicted final standings for Bundesliga this season.

No. Team
Goalimpact
Points
Goal Diff
Bwin Rank
ClubElo
Euro Club
Index
Last Year
1 Bayern München 139,8 84,7 +64,8
1
1
1
1
2 Borussia Dortmund 119,8 60,2 +23,1
2
2
2
2
3 FC Schalke 04 119,0 59,2 +21,3
3
4
4
4
4 Bayer Leverkusen 113,8 52,9 +10,6
4
3
3
3
5 VfL Wolfsburg 112,3 50,9 +7,3
5
7
8
11
6 VfB Stuttgart 107,5 45,0 -2,8
6
13
7
12
7 Hannover 96 106,1 43,4 -5,6
10
8
6
9
8 1. FSV Mainz 05 105,7 42,9 -6,4
13
11
11
13
9 Bor. Mönchengladbach 105,6 42,7 -6,7
6
6
5
8
10 Hertha BSC 105,4 42,5 -7,1
12
14
13
(17)
11 1899 Hoffenheim 105,3 42,4 -7,3
13
16
16
16
12 Eintracht Braunschweig 105,0 42,0 -7,9
18
18
18
(18)
13 SC Freiburg 104,6 41,5 -8,8
13
5
9
5
14 Hamburger SV 103,6 40,3 -10,8
8
10
10
7
15 1. FC Nürnberg 103,5 40,2 -11,0
16
9
12
10
16 Werder Bremen 101,2 37,4 -15,8
11
17
15
14
17 Eintracht Frankfurt 100,7 36,8 -16,8
9
12
14
6
18 FC Augsburg 99,2 35,0 -19,9
17
15
17
15

As comparison, I added the estimated rank implied in the Bwin odds and the current rank according to ClubElo and the Euro Club Index. The first four teams are identical in all predictions. This doesn't come as a surprise as they are identical to the first four of the last season. The only deviation here is that Bwin and Goalimpact put Schalke above Leverkusen while ClubElo and the Euro Club Index kept the order of last season. But opinions diverge a lot on many of the other league ranks.

Goalimpact predicts Wolfsburg to finish 5th and Stuttgart 6th. Interestingly, this is identical to the predictions by Bwin although both teams where nowhere close to such a good rank in the previous season. The Euro Club Index has a similar rank for both. But it sees Hanover and Mönchengladbach stronger and thus the two are on 7 and 8. ClubElo share the view of a strong Wolfsburg, albeit on rank 7, but predicts Stuttgart to finish even below last year's disappointing rank 12.

All three statistic measures see Hanover finishing slightly higher than previous year on tank 6 to 8, but bwin puts them a rank lower on 10. Similarly all statistic based predictions see Mainz heading to a better season than last year's rank 13. Goalimpact is the most optimistic with rank 8, the other put Mainz on 11. Bwin sees no improvement to last year.

The prediction of newly relegated teams is particularly difficult, because they played few games, if any, against the other teams last season. The difference between the leagues is significant and many new teams face relegation just the next season again. This is, in fact, the prediction for Eintracht Braunschweig. ClubElo, the Euro Club Index, and Bwin see them as clear number 18. If you look at score values and odds, they are predicted to be the last by quite a margin. Goalimpact is more optimistic here and ranks them on 12. There first eleven is not outstanding here either, but the other players are not much worse than the team's stars. It might be that Goalimpact is biased upwards here. The other fresh relegated team, Hertha BSC Berlin, is predicted to be save in the middle of the table by all sources. They should end up between rank 10 (GI) and 14 (ClubElo).

Looking at the lower end of the table, Goalimpact predicts Bremen, Frankfurt and Augsburg as relegated teams. Especially, Frankfurt is disputed by the other approaches. They all predict a lower rank the last year's rank 6, too, but they see Frankfurt to end in the nowhere land between rank 9 and 14. Bremen is as a relegation candidate by the club-based algorithms, too. Bwin is here much more optimistic and predicts rank 11. Augsburg is a likely relegation team by all rankings. ClubElo is the last spark of hope by predicting Augsburg to repeat last year's rank 15. 1899 Hoffenheim is predicted to be relegated by both of the club-based approaches. Goalimpact and Bwin, in contrast, both predict a final rank in the middle of the table (11-13).

We will only know with hindsight which prediction was closed to reality. However, we can have short look into the predictions now already by looking into the correlations.

Goalimpact
Bwin Rank
ClubElo
Euro Club
Index
Last Year
Goalimpact
100%
78%
69%
83%
50%
Bwin Rank
100%
75%
87%
75%
ClubElo
100%
92%
91%
Euro Club Index
100%
82%
Last Year
100%

We can see that the two club-based measures are very highly correlated (92%) and also show comparably high correlations to the last year's ranks (91% and 82%). The lower the correlation is to the last years final rank, the braver (but not necessarily better) is the prediction. ClubElo's 91% makes it close to the naive estimation that everything stays as it was. Bwin (75%) and Goalimpact (50%) were bolder in moving away from last year's standings. If that was too bold, we will now in one year from now.

The Market Value of Johan Djourou - Transfer Report

Reportedly, the Hamburger SV wants to buy Arsenal's Johan Djourou. This caused me to compare his Goalimpact with the market value according to transfermarkt.de, to get an idea if it would be a good deal. Here is the development of the Goalimpact over time.



And this compares to the followong market value development.


The two curves are strikingly similar. The Goalimpact rises until February 2007 where it reaches a plateau at 124. The market value also shows an increase until April 2004 when a plateau starts. In October 2007 the Goalimpact starts dropping until it reaches a bottom in March 2008. The market value drops, too. Putting the bottom at July 2008.

The Goalimpact rises again until it reaches another plateau from March 2009 until October 2010. We see again the same pattern in the market value where the plateau lasts from February 2009 until Februrary 2011. Both start raising thereafter again. Goalimpact peaks in February 2011 and drops slightly January 2012. The market value peaks August 2011 and drops slightly February 2012.

Until then, the two values move in parallel to an amazing extend. If you check the date, you will see that the Goalimpact usually reacts about three month earlier.

However, in 2012 the two curve move differently. The market value dropped significantly while the Goalimpact is more or less unchanged. If Hamburg SV could buy him at these low prices, that would be a good deal.