2. Bundesliga · Germany · Regular Season - 5

1. FC Heidenheim–Holstein Kiel

1–0

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'Holstein KielJ. Therkelsen · red card
83'1. FC HeidenheimJ. Föhrenbach (assist: M. Kaufmann)

Match statistics

Match statistics
1. FC Heidenheim		Holstein Kiel
19	Shots	13
4	Shots on target	2
12	Shots inside the box	7
3	Goalkeeper saves	3
6	Corners	8
4	Offsides	1
51%	Possession	49%
372	Passes	364
294	Accurate passes

What we expected, and what happened

Expected goals before kick-off: 1. FC Heidenheim 2.01 · Holstein Kiel 1.33. On the pitch: 1-0.
Shots 1. FC Heidenheim 19 · Holstein Kiel 13; on target 4 and 2.

Announced lineup

1. FC Heidenheim

Formation: 4-4-2 · Manager: F. Schmidt

Starting XI

T. Dähnegoalkeeper
M. Heuledefender
J. Föhrenbachdefender
P. Mainkadefender
M. Buschdefender
M. Costlymidfielder
J. Schöppnermidfielder
M. Honsakmidfielder
O. Mhamdimidfielder
J. Niehuesforward
A. Vogtforward

Substitutes

C. Conteh
Frank Feller

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 41.8%: “1. FC Heidenheim to win”. Second comes “Holstein Kiel to win” at 33.2%.
Behind that distribution are the expected goals: 1.73 for 1. FC Heidenheim, 1.54 for Holstein Kiel.
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.

1. FC Heidenheim to win41.8%

Draw24.9%

Holstein Kiel to win33.2%

When the model has said 40–45% in the past, 1,777 of 3,996 predictions were correct (44.5%). Accuracy record →

11	Fouls	12

1	Yellow cards	0

0	Red cards	1

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.

Holstein Kiel

Formation: 4-1-4-1 · Manager: T. Walter

Starting XI

T. Weinergoalkeeper
Leon Parduzidefender
D. Zecdefender
I. Nekićdefender
H. Sekinedefender
J. Meffertmidfielder
J. Therkelsenmidfielder
I. Ugohmidfielder
G. Balzimidfielder
A. Kaprálikmidfielder
P. Harresforward

Substitutes

G. de Regt
J. Tolkin
Mark Richter Monserrat
N. Madsen
Niko Takahashi Cendagorta
S. Skrzybski
T. Tasdelen
T. Wiese

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 11:42.

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

Double chance

1X — 1. FC Heidenheim win or draw66.8%

12 — any result other than a draw75.1%

X2 — draw or Holstein Kiel win58.2%

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

1. FC Heidenheim lead at half-time28.3%

Level at half-time35.9%

Holstein Kiel lead at half-time35.7%

First-half double chance

1X — 1. FC Heidenheim ahead or level64.3%

12 — not level at half-time64.1%

X2 — level or Holstein Kiel ahead71.7%

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

1. FC Heidenheim: at least 11Actual: 11
Holstein Kiel: at least 10Actual: 12

Corners

1. FC Heidenheim: at least 3Actual: 6
Holstein Kiel: at least 5Actual: 8

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+ goals64.6%

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 1. FC Heidenheim score2.01

Attack: 1. FC Heidenheim score above the league average (21% more).

Opponent's defence: Holstein Kiel's defence is close to the league average (1.01×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 2.01 goals (including home advantage).

How many goals Holstein Kiel score1.33

Attack: Holstein Kiel score close to the league average.

Opponent's defence: 1. FC Heidenheim's defence is close to the league average (1.13×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.33 goals (including the away penalty).

Most likely scorelines

1–111.4%2–19.6%1–28.4%

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.