2. Bundesliga
Dixon-Coles model — fitted 16 Mar 2026 on 1,872 matches since 2020-01-01
Home Advantage (γ)
1.1676
>1 = home favoured
ρ (rho)
-0.09712
low-score correction
Teams modelled
33
Log-likelihood
-449
Team Parameters
Attack (α) — normalised attacking strength; mean ≈ 1.0. Defense (β) — defensive weakness; lower = harder to score against.
| # | Team | Attack (α) | Defense (β) | Matches |
|---|---|---|---|---|
| 1 | Hamburg | 1.6963 | 1.1277 | 186 |
| 2 | Darmstadt | 1.4338 | 1.0752 | 174 |
| 3 | Werder Bremen | 1.4162 | 1.0852 | 34 |
| 4 | Elversberg | 1.3538 | 0.9737 | 90 |
| 5 | Heidenheim | 1.3290 | 0.9392 | 118 |
| 6 | Hannover | 1.3143 | 1.1194 | 208 |
| 7 | St Pauli | 1.3015 | 0.9888 | 152 |
| 8 | Kaiserslautern | 1.2012 | 1.2907 | 124 |
| 9 | Magdeburg | 1.1978 | 1.4589 | 124 |
| 10 | Paderborn | 1.1676 | 1.0353 | 192 |
| 11 | Nurnberg | 1.1528 | 1.2567 | 208 |
| 12 | Karlsruhe | 1.1385 | 1.5815 | 208 |
| 13 | Bielefeld | 1.1234 | 1.1345 | 72 |
| 14 | Dresden | 1.1151 | 1.4599 | 72 |
| 15 | Hertha | 1.1122 | 0.9961 | 90 |
| 16 | Bochum | 1.0787 | 0.9248 | 72 |
| 17 | FC Koln | 1.0782 | 0.8186 | 34 |
| 18 | Greuther Furth | 1.0337 | 1.7652 | 174 |
| 19 | Schalke 04 | 0.9959 | 0.9213 | 124 |
| 20 | Holstein Kiel | 0.9846 | 1.2232 | 174 |
| 21 | Stuttgart | 0.8786 | 1.0002 | 16 |
| 22 | Fortuna Dusseldorf | 0.8692 | 1.1299 | 192 |
| 23 | Braunschweig | 0.8669 | 1.3866 | 158 |
| 24 | Ulm | 0.8470 | 1.2562 | 34 |
| 25 | Preußen Münster | 0.8283 | 1.0896 | 56 |
| 26 | Erzgebirge Aue | 0.7733 | 1.8117 | 84 |
| 27 | Wurzburger Kickers | 0.7701 | 1.5121 | 34 |
| 28 | Sandhausen | 0.7365 | 1.5087 | 118 |
| 29 | Wehen | 0.7251 | 1.3581 | 50 |
| 30 | Osnabruck | 0.6855 | 1.6604 | 84 |
| 31 | Ingolstadt | 0.6792 | 1.5906 | 34 |
| 32 | Hansa Rostock | 0.6236 | 1.3969 | 102 |
| 33 | Regensburg | 0.6074 | 1.6228 | 152 |