2. Bundesliga · Germany · Regular Season - 5

VfL Osnabrück–Hertha BSC

1–3

HT 0–0

Kick-off: Sunday, 13 September 2026 at 11:30
Status: Match Finished

What the model said, and what happened

Prediction correct

This verdict goes into the public record unchanged; we do not filter out the ones we get wrong. The model's track record

Goals and red cards

50'Hertha BSCJ. Brekalo
70'Hertha BSCK. Sessa
82'VfL OsnabrückJ. Gómez (assist: A. Beck)
90+2'Hertha BSCFarid Alfa-Ruprecht (assist: S. Gouram)

Match statistics

Match statistics
VfL Osnabrück		Hertha BSC
14	Shots	22
7	Shots on target	7
9	Shots inside the box	14
4	Goalkeeper saves	6
4	Corners	5
0	Offsides	3
43%	Possession	57%
381	Passes	509
277	Accurate passes

What we expected, and what happened

Expected goals before kick-off: VfL Osnabrück 1.49 · Hertha BSC 1.78. On the pitch: 1-3.
Shots VfL Osnabrück 14 · Hertha BSC 22; on target 7 and 7.
Hertha BSC kept the ball more (57%).

Announced lineup

VfL Osnabrück

Formation: 3-4-2-1 · Manager: T. Schultz

Starting XI

J. Krumreygoalkeeper
N. Wiemanndefender
J. Müllerdefender
R. Fabinskidefender
J. Gómezmidfielder
B. Jacobsenmidfielder
F. Wagnermidfielder
P. Kammerbauermidfielder
A. Beckmidfielder
L. Münstmidfielder
R. Meißnerforward

Substitutes

B. Riesselmann
D. Kyereh

Referee

The referee for this match hasn't been announced by the data provider yet. We don't guess it.

Model prediction

Why this prediction?

The model puts the most likely outcome at 45.6%: “Hertha BSC to win”. Second comes “VfL Osnabrück to win” at 29.6%.
Behind that distribution are the expected goals: 1.43 for VfL Osnabrück, 1.79 for Hertha BSC.
The highest double chance value is 75.1% (12 — any result other than a draw).
These values were computed from 1,266 past matches.

This is a prediction, not a guarantee; a probability is not a certainty.

These sentences are built by a fixed template; every number in them comes straight from the model's computation, none invented while the text was written.

VfL Osnabrück to win29.6%

Draw24.9%

Hertha BSC to win45.6%

When the model has said 45–50% in the past, 1,541 of 3,251 predictions were correct (47.4%). Accuracy record →

12	Fouls	12

2	Yellow cards	2

0	Red cards	0

The measurements our data provider sent for this match. A row that was not measured shows “—”; writing 0 into an empty field would claim we measured it and found none.

Hertha BSC

Formation: 4-2-3-1 · Manager: S. Leitl

Starting XI

M. Gersbeckgoalkeeper
M. Pedersendefender
N. Kolbedefender
L. Gechterdefender
J. Eitschbergerdefender
K. Sessamidfielder
P. Klemensmidfielder
J. Brekalomidfielder
O. Adewumimidfielder
J. Þorsteinssonmidfielder
S. Grønningforward

Substitutes

D. Zeefuik
Farid Alfa-Ruprecht
G. Nilsson
J. Diallo
L. Schuler
M. Dárdai
S. Gouram
Selim Telib
T. Goller

Lineups are announced about an hour before kick-off and can still change until the first whistle. The list is shown exactly as the provider sends it and does not feed the prediction. Last updated: Sunday, 13 September 2026 at 11:44.

When the model has said 65–70% in the past, 575 of 859 predictions were correct (66.9%). Accuracy record →

Double chance

1X — VfL Osnabrück win or draw54.4%

12 — any result other than a draw75.1%

X2 — draw or Hertha BSC win70.4%

These three values add up to 200%, not 100% — and that is not a mistake. The options are not mutually exclusive; every outcome is counted in two of them. For the same reason 1X plus X2 exceeds 100% by exactly the probability of a draw (24.9%).

First half

Actual: 0–0

VfL Osnabrück lead at half-time17.0%

Level at half-time36.1%

Hertha BSC lead at half-time47.0%

First-half double chance

1X — VfL Osnabrück ahead or level53.0%

12 — not level at half-time63.9%

X2 — level or Hertha BSC ahead83.0%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 1266 matches. Its edge over the base rate is markedly smaller than the full-match model's — the measured figure is on the accuracy record. Read them more cautiously. First-half goal markets are not published because they failed to beat the base rate.

Expected fouls and corners

Fouls

VfL Osnabrück: at least 10Actual: 12
Hertha BSC: at least 11Actual: 12

Corners

VfL Osnabrück: at least 4Actual: 4
Hertha BSC: at least 5Actual: 5

These figures are set to hold in at least 60% of matches; in the rest the count comes in below them. No upper bound is stated — we make no "at most" claim. Yellow cards were measured and are not published: there our model did not beat the historical average.

Goals market

3+ goals63.4%

When the model has said 60–65% in the past, 1,139 of 1,798 predictions were correct (63.3%). Accuracy record →

The league's historical "3+ goals" rate is 59.3%. For this match the model gives a value above that baseline — and in backtesting the model's edge was confirmed only in this direction.

What these probabilities rest on

Expected goals comes out of a team's attack weighed against its opponent's defence. A strong attack is damped when it meets a strong defence — which is why comparing the ratings on their own is misleading.

How many goals VfL Osnabrück score1.49

Attack: VfL Osnabrück score close to the league average.

Opponent's defence: Hertha BSC's defence is close to the league average (0.98×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.49 goals (including home advantage).

How many goals Hertha BSC score1.78

Attack: Hertha BSC score close to the league average.

Opponent's defence: VfL Osnabrück concede 32% more than the league average — the defence is the weakest link in this match.

Result: These two effects give an expectation of 1.78 goals (including the away penalty).

Most likely scorelines

1–111.6%1–210.1%2–17.9%

A single scoreline is always less likely than an outcome: “home win” is the sum of 1-0, 2-0, 2-1 and dozens more. So the most likely single score can be a draw while the most likely outcome is a home win; the two do not contradict each other.

Ratings were computed from 1266 matches played in this league before kick-off; the team with fewer has 38 matches. No data from after the match was used. The model's track record is published unfiltered.