⚽ FootballData
Dixon-Coles/Bundesliga

Bundesliga

Dixon-Coles model — fitted 16 Mar 2026 on 2,369 matches since 2020-01-01

Home Advantage (γ)
1.1809
>1 = home favoured
ρ (rho)
-0.07542
low-score correction
Teams modelled
40
Log-likelihood
-1014

Team Parameters

Attack (α) — normalised attacking strength; mean ≈ 1.0. Defense (β) — defensive weakness; lower = harder to score against.

#TeamAttack (α)Defense (β)Matches
1Bayern Munich
2.5597
0.7798
209
2FC Koln
1.7994
1.1210
238
3Dortmund
1.6017
0.9037
218
4Hoffenheim
1.4701
1.2282
221
5Stuttgart
1.4488
1.0990
203
6Ein Frankfurt
1.4448
1.4656
209
7Eintracht Frankfurt
1.3900
1.4109
59
8Leverkusen
1.3665
0.9363
221
9RB Leipzig
1.3248
1.1342
217
10Salzburg
1.3018
1.0868
54
11Holstein Kiel
1.1791
1.9044
40
12Sturm Graz
1.1249
1.1044
54
13Austria Wien
1.1124
1.3333
86
14AC London
1.1080
1.3790
54
15Wolfsburg
1.0636
1.5887
219
16Freiburg
1.0427
1.2897
217
17WSG Tirol
1.0134
1.3005
54
18LASK Linz
0.9736
1.3151
111
19Mainz
0.9695
1.0720
217
20M'gladbach
0.9580
1.2967
209
21Hertha
0.9339
1.4774
115
22Borussia Mönchengladbach
0.9200
1.3422
59
23Union Berlin
0.9096
1.1915
218
24SK Rapid
0.8996
1.1127
56
25Werder Bremen
0.8977
1.3749
189
26Hartberg
0.8850
0.9618
55
27Augsburg
0.8391
1.1716
218
28Hamburg
0.8280
1.0664
30
29Ried
0.8117
1.2467
22
30Fortuna Dusseldorf
0.8007
1.0080
16
31Paderborn
0.7992
1.0152
16
32Grazer AK
0.7825
1.4256
54
33Schalke 04
0.7631
1.5845
81
34Bochum
0.7620
1.5981
140
35Rheindorf Altach
0.7257
1.0570
54
36Heidenheim
0.7130
1.7041
102
37Greuther Furth
0.7009
1.6605
34
38Bielefeld
0.6530
1.1800
68
39St Pauli
0.6497
1.1373
68
40Darmstadt
0.5736
1.8162
33