2. Bundesliga
Retrospective analysis — the model is trained only on matches played before this one.
The model's probabilities for a home win, draw and away win — with reasoning and recommended markets.
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Bielefeld
Hertha
Expected goals, over/under and both teams to score — priced by the model before kick-off.
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The model's top final scores with the best bookmaker odds.
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Half-time score: 0–1
| # | Team | K | V | U | T | +/− | xG +/− | P |
|---|---|---|---|---|---|---|---|---|
| 1 |
Schalke 04
|
33 | 20 | 7 | 6 | +18 | +16.6 | 67 |
| 2 |
Elversberg
|
33 | 17 | 8 | 8 | +22 | +8.8 | 59 |
| 3 |
Hannover
|
33 | 16 | 11 | 6 | +16 | +12.5 | 59 |
| 4 |
Paderborn
|
33 | 17 | 8 | 8 | +12 | +13.6 | 59 |
| 5 |
Darmstadt
|
33 | 13 | 13 | 7 | +14 | +2.3 | 52 |
| 6 |
Hertha
|
33 | 14 | 9 | 10 | +8 | -3.1 | 51 |
| 7 |
Kaiserslautern
|
33 | 15 | 4 | 14 | +4 | -2.3 | 49 |
| 8 |
Nurnberg
|
33 | 12 | 9 | 12 | +2 | +1.5 | 45 |
| 9 |
Karlsruhe
|
33 | 12 | 8 | 13 | -10 | -19.7 | 44 |
| 10 |
Bochum
|
33 | 10 | 11 | 12 | +1 | +4.1 | 41 |
| 11 |
Holstein Kiel
|
33 | 11 | 8 | 14 | -3 | -11.6 | 41 |
| 12 |
Magdeburg
|
33 | 12 | 3 | 18 | -5 | +6.8 | 39 |
| 13 |
Dresden
|
33 | 10 | 8 | 15 | +0 | +3.6 | 38 |
| 14 |
Braunschweig
|
33 | 10 | 7 | 16 | -17 | -4.3 | 37 |
| 15 |
Fortuna Dusseldorf
|
33 | 11 | 4 | 18 | -17 | -6.5 | 37 |
| 16 |
Bielefeld
|
33 | 9 | 9 | 15 | -3 | +2.2 | 36 |
| 17 |
Greuther Furth
|
33 | 9 | 7 | 17 | -22 | -9.4 | 34 |
| 18 |
Preußen Münster
|
33 | 6 | 12 | 15 | -20 | -15.1 | 30 |
Calculated from our results data. “xG +/−” is expected goal difference — a positive number with a low league position suggests an undervalued team. Ties on points are sorted by goal difference; some leagues use other rules.
J. Kersken
G
C. Lannert
D
M. Bauer
D
M. Grosser
D
A. Sicker
D
S. Russo
M
S. Schreck
M
M. Worl
M
M. Corboz
M
T. Momuluh
F
J. Grodowski
F
Coach: Michel Kniat
T. Ernst
G
J. Eitschberger
D
L. Gechter
D
N. Kolbe
D
P. Klemens
M
M. Cuisance
M
P. Seguin
M
F. Reese
M
S. Gronning
F
Coach: Stefan Leitl
Green columns are the outcomes that came in.
Kaiserslautern
Bielefeld
Bielefeld
Bochum
Preußen Münster
Bielefeld
Bielefeld
Nurnberg
Karlsruhe
Bielefeld
The analysis is a statistical estimate based on the teams' results and underlying performances (xG) — not a guarantee. See the model's results.