Borussia Dortmund vs Bayer Leverkusen: Goalimpact of Lineups

Dortmund has the slightly better starting XI and due to the home field advantage are expected to win. Betfair favors Dortmund slightly more.

Borussia Dortmund: 51.9%
Draw: 28.0%
Bayer Leverkusen: 20.1%

Borussia Dortmund

PlayerGoalimpactPeak GIAgeLast National TeamNo. GamesNo. Minutes
Marco Reus114.5116.225.2Deutschland27121167
Miloš Jojic109.6119.922.3Serbien [U21]734733
Erik Durm104.3115.222.3Deutschland [U21]1209308
Sebastian Kehl104.1142.734.5Deutschland43435004
Mitchell Langerak108.8124.625.9575153
Pierre-Emerick Aubameyang109.0110.925.1Gabun23616667
Sokratis Papastathopoulos105.9106.326.2Griechenland24920504
Ciro Immobile105.4108.724.4Italien1419801
Matthias Ginter107.0127.620.5Deutschland [U21]13011781
Lukasz Piszczek130.1136.929.2Polen26621319
Henrikh Mkhitaryan128.6129.625.5Armenien22418865
Average111.6121.725.6
Bench
Average0.00.00.0


Bayer Leverkusen

PlayerGoalimpactPeak GIAgeLast National TeamNo. GamesNo. Minutes
Hakan Çalhanoglu97.7118.520.5Türkei13510865
Gonzalo Castro122.3125.527.2Deutschland [U21]36529722
Emir Spahic87.6121.733.9Bosnien-Herzegowina24922434
Bernd Leno106.6153.822.4Deutschland [U21]20419034
Karim Bellarabi104.6108.124.3Deutschland [U21]1248615
Stefan Kießling121.3133.130.5Deutschland40630012
Sebastian Boenisch112.1116.327.5Polen17513347
Tin Jedvaj90.3128.918.7Kroatien [U21]221883
Heung-Min Son96.6108.022.1Südkorea15510400
Simon Rolfes125.1152.132.5Deutschland48840314
Ömer Toprak119.7121.725.0Türkei22319068
Average107.6126.225.9
Bench
Average0.00.00.0



Bayer Leverkusen vs Paris Saint-Germain: Stats for Lineups

This is a game of two very good sides. The chances for Leverkusen to advance are better than many expect. Still PSG is the favorite. They have some world-class in their starting XI and no weak links. For Leverkusen Son and Spahic are well below the average team quality.

Julian Brandt is on the bench. One of the most talented young German players. Will be exciting to see him, but his lack of experience make it risky. But he is certainly an option if PSG is in the lead.

Bayer Leverkusen

PlayerGoalimpactPeak GIAgeLast National TeamNo. GamesNo. Minutes
Bernd Leno115.5115.521.9Deutschland [U21]18517236
Emir Spahic91.1119.133.4Bosnien-Herzegowina22920642
Roberto Hilbert111.9117.029.3Deutschland35728505
Ömer Toprak120.0124.124.5Türkei19616711
Simon Rolfes124.3144.332.0Deutschland47039140
Lars Bender115.6119.124.8Deutschland24717962
Sidney Sam126.1126.226.0Deutschland23516190
Andrés Guardado117.9120.827.3Mexiko32826726
Gonzalo Castro125.9127.326.7Deutschland [U21]34728237
Heung-Min Son95.9114.821.6Südkorea1328651
Stefan Kießling125.0130.630.0Deutschland39028619
Bench
David Yelldell91.291.232.320819265
Philipp Wollscheid112.7115.724.917915551
Sebastian Boenisch116.0118.127.0Polen16312231
Stefan Reinartz125.7128.325.1Deutschland [U21]22819442
Jens Hegeler114.1114.226.021614083
Levin Öztunali67.2124.717.9Deutschland [U19]382871
Julian Brandt92.7154.817.8Deutschland [U19]524432


Paris Saint-Germain

PlayerGoalimpactPeak GIAgeLast National TeamNo. GamesNo. Minutes
Salvatore Sirigu117.7117.727.1Italien20619143
Thiago Silva127.4132.529.3Brasilien25623076
Alex138.2155.431.7Brasilien34229391
Maxwell142.3164.632.442134237
Gregory van der Wiel150.5150.526.0Niederlande24521283
Thiago Motta109.8125.531.4Italien32522900
Blaise Matuidi116.6118.326.8Frankreich33027841
Marco Verratti119.0140.421.3Italien [U21]1268684
Lucas114.8134.421.5Brasilien14610548
Zlatan Ibrahimovic164.0185.732.3Schweden58447474
Ezequiel Lavezzi110.9115.728.8Argentinien36428350
Bench
Nicolas Douchez92.992.933.822620884
Marquinhos95.0129.119.8Brasilien [U17]604916
Lucas Digne109.6136.520.5Frankreich [U21]857445
Yohan Cabaye116.3120.728.1Frankreich34026488
Adrien Rabiot92.7136.818.8Frankreich [U21]613992
Javier Pastore115.5119.424.6Argentinien22516742
Jérémy Ménez111.4112.926.8Frankreich33520922


Bayer Leverkusen vs Shakhtar Donetsk: Goalimpacts of Lineups


Bayer Leverkusen

PlayerGoalimpactAgeTeamNo. GamesNo. Minutes
Bernd Leno118.721.6Deutschland [U21]16315159
Emir Spahic108.833.1Bosnien-Herzegowina21619584
Sebastian Boenisch123.426.7Polen15411419
Ömer Toprak120.924.2Türkei17815052
Giulio Donati95.023.7Italien [U21]917620
Simon Rolfes132.031.7Deutschland45237469
Emre Can114.419.7Deutschland [U21]786334
Sidney Sam124.725.7Deutschland22415484
Gonzalo Castro128.426.3Deutschland [U21]33126833
Heung-Min Son100.621.2Südkorea1177540
Stefan Kießling130.429.7Deutschland37227097
Bench
Palop127.139.938735629
Philipp Wollscheid109.524.616714710
Roberto Hilbert119.229.0Deutschland35428233
Jens Hegeler111.925.719913328
Robbie Kruse103.225.0Australien1449850
Dominik Kohr112.219.7Deutschland [U19]654871
Eren Derdiyok116.125.3Schweiz23813744


Shakhtar Donetsk

PlayerGoalimpactAgeTeamNo. GamesNo. Minutes
Andriy Pyatov145.729.3Ukraine22921300
Tomas Hübschman131.132.1Tschechien23419312
Oleksandr Kucher141.330.9Ukraine17115187
Vyacheslav Shevchuk133.334.4Ukraine1149860
Darijo Srna151.031.4Kroatien32729001
Yaroslav Rakitskiy140.824.2Ukraine14913509
Fernando110.821.6Brasilien1118222
Douglas Costa124.923.0Brasilien [U20]1709589
Luiz Adriano134.626.5Brasilien [U20]18512836
Taison122.825.719313603
Alex Teixeira126.123.7Brasilien [U20]1539457
Bench
Anton Kanibolotskiy111.125.4Ukraine [U21]504525
Sergiy Krivtsov109.422.5Ukraine [U21]897087
Taras Stepanenko109.824.1Ukraine12610099
Bernard111.321.1Brasilien796324
Ilsinho126.328.0Brasilien Olymp.16710247
Eduardo138.930.6Kroatien20111254
Facundo Ferreyra98.522.6Argentinien [U20]956804


Manchester United vs. Bayer Leverkusen: Lineups and Stats


Manchester United

PlayerGoalimpactAgeNational TeamNo. GamesNo. Minutes
De Gea131.522.8Spanien [U21]19418061
Patrice Evra168.532.3Frankreich51244216
Rio Ferdinand157.034.8England68161482
Chris Smalling128.923.8England1118486
Nemanja Vidic152.831.9Serbien30927413
Michael Carrick143.432.1England51943143
Antonio Valencia129.528.1Ecuador28021936
Shinji Kagawa133.724.5Japan13810532
Marouane Fellaini117.425.8Belgien26823505
Wayne Rooney168.627.9England53342678
Robin van Persie150.230.1Niederlande44931397
Bench
Anders Lindegaard109.829.4Dänemark958834
Jonny Evans138.025.7Nordirland21618648
Fabio115.823.1754477
Anderson139.025.4Brasilien Olymp.20213040
Ashley Young124.528.1England37030300
Tom Cleverley114.324.1England1229232
Javier Hernandez132.425.3Mexiko22212985


Bayer Leverkusen

PlayerGoalimpactAgeNational TeamNo. GamesNo. Minutes
Bernd Leno115.121.5Deutschland [U21]15414322
Emir Spahic108.633.0Bosnien-Herzegowina21019026
Sebastian Boenisch123.726.6Polen14710885
Ömer Toprak119.224.1Türkei17414680
Giulio Donati93.523.6Italien [U21]887415
Stefan Reinartz126.224.7Deutschland [U21]21618469
Simon Rolfes132.531.6Deutschland44837097
Emre Can115.219.6Deutschland [U21]736058
Sidney Sam124.425.6Deutschland [U21]21915182
Heung-Min Son99.221.1Südkorea1137223
Stefan Kießling130.729.6Deutschland36826831
Bench
Palop129.539.938735629
Philipp Wollscheid107.924.516214245
Roberto Hilbert119.628.9Deutschland35127973
Lars Bender117.824.3Deutschland22816464
Levin Öztunali94.517.5272190
Robbie Kruse101.324.9Australien1399671
Eren Derdiyok117.525.2Schweiz23613658


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.

Hall Of Fame - The Best Football Players of All Time

So far, only 65 players in history managed to have a Goalimpact of more than 160. Here is the full list in order of entry.

NumberDate of EntryNameTeam
104/1976Gerd MüllerBayern München
204/1976Franz BeckenbauerBayern München
312/1976Sepp MaierBayern München
404/1980Berti VogtsBor. Mönchengladbach
505/1981Paul BreitnerBayern München
605/1985Felix MagathHamburger SV
706/1985Bodo RudwaleitBFC Dynamo
804/1986Dieter HoeneßBayern München
906/1986Klaus AugenthalerBayern München
1012/1986Norbert TrieloffBFC Dynamo
1105/1987Erich ObermayerAustria Wien
1206/1987Manfred KaltzHamburger SV
1312/1987Uli SteinEintracht Frankfurt
1406/1988Herbert ProhaskaAustria Wien
1504/1990Hans PflüglerBayern München
1611/1993ZubizarretaFC Barcelona
1710/1994Bernd SchusterBayer Leverkusen
1806/1995Leo LainerRB Salzburg
1910/1997SanchisReal Madrid
2010/1998Lothar MatthäusBayern München
2104/2002Andreas MöllerFC Schalke 04
2203/2003Oliver KahnBayern München
2302/2006Patrick VieiraJuventus
2403/2006Ryan GiggsManchester United
2510/2006Claude MakeleleChelsea FC
2611/2006Paul ScholesManchester United
2712/2006Gary NevilleManchester United
2810/2007Luis FigoInter
2912/2008Thierry HenryFC Barcelona
3012/2008John TerryChelsea FC
3102/2009Edwin van der SarManchester United
3210/2009PuyolFC Barcelona
3310/2009XaviFC Barcelona
3411/2009Frank LampardChelsea FC
3503/2010Iker CasillasReal Madrid
3606/2010Ashley ColeChelsea FC
3706/2010Victor ValdesFC Barcelona
3806/2010Petr CechChelsea FC
3907/2010Lionel MessiFC Barcelona
4011/2010Cristiano RonaldoReal Madrid
4112/2010Bastian SchweinsteigerBayern München
4203/2011Alessandro NestaAC Milan
4304/2011Ricardo CarvalhoReal Madrid
4405/2011Dani AlvesFC Barcelona
4507/2011Philipp LahmBayern München
4609/2011IniestaFC Barcelona
4710/2011Wayne RooneyManchester United
4810/2011Xabi AlonsoReal Madrid
4911/2011KakaReal Madrid
5011/2011BusquetsFC Barcelona
5112/2011Cesc FabregasFC Barcelona
5202/2012LucioInter
5303/2012Patrice EvraManchester United
5405/2012Mesut ÖzilReal Madrid
5505/2012PedroFC Barcelona
5606/2012ArbeloaReal Madrid
5708/2012PiqueFC Barcelona
5808/2012Sergio RamosReal Madrid
5910/2012Mark van BommelPSV Eindhoven
6012/2012Thomas MüllerBayern München
6112/2012AlexParis Saint-Germain
6212/2012Zlatan IbrahimovicParis Saint-Germain
6303/2013Arjen RobbenBayern München
6404/2013Javier MascheranoFC Barcelona
6505/2013Manuel NeuerBayern München


Goalimpact allows, in principle, for inter-temporal comparison. However, we can see that today more player per year enter the list than in the 1990s and before. One reason for this is the emergence of more leagues in my database and a resulting score inflation. Another reason is the higher amount of games played by a single player today as compared to the 'good old times'. This makes the job easier for the algorithm to differentiate between good and bad players and good players therefore can easier reach higher values.

I like historical analysis and if I come around data from the old times that are missing at the moment, I'll try to add them to my database and update this list. One league that comes to mind is the Dutch Eredivisie. I have it only from 2003 onward. Johan Cruyff was at his time in he top 30 of the players all along, but could never reach 160+ because I recorded too few minutes of him and his scored therefore suffered from the regression to the mean. I'd love to add his time at Ajax.