matrisx.com
FixturesLeaguesTrack recordAccountPricing
TR·EN
TR·EN
FixturesLeaguesTrack recordAccount
Analytics platform · not a betting siteTrack recordThe last 7 daysLegal noticeRefund policyPrivacyiletisim@matrisx.comFollow on X
← Fixtures

2. Bundesliga · Germany · Regular Season - 2

SpVgg Greuther Fürth–1. FC Nürnberg

2–4

HT 2–2

Kick-off
Saturday, 15 August 2026 at 11:00
Status
Match Finished
Venue
Sportpark Ronhof Thomas Sommer, Fürth
𝕏Share on X

What the model said, and what happened

Too close to call — we gave this result 35%✓ double chance: 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

  • 22'⚽SpVgg Greuther FürthF. Klaus (penalty)
  • 26'⚽1. FC NürnbergP. Scobel (assist: J. Justvan)
  • 41'⚽SpVgg Greuther FürthF. Klaus (assist: D. Srbeny)
  • 45'⚽1. FC NürnbergJ. Justvan (assist: P. Scobel)
  • 54'⚽1. FC NürnbergJ. Justvan (assist: F. Otto)
  • 74'⚽1. FC Nürnberg

Match statistics

Match statistics
SpVgg Greuther Fürth1. FC Nürnberg
14Shots15
6Shots on target8
8Shots inside the box10
4Goalkeeper saves4
4Corners3
2Offsides1
43%Possession57%
317Passes432
259Accurate passes

What we expected, and what happened

  • Expected goals before kick-off: SpVgg Greuther Fürth 1.53 · 1. FC Nürnberg 1.46. On the pitch: 2-4.
  • Shots SpVgg Greuther Fürth 14 · 1. FC Nürnberg 15; on target 6 and 8.
  • 1. FC Nürnberg kept the ball more (57%).

Referee

Tom Bauer, Germany→

4.75

yellows per match

0.25

reds per match

4

matches of data

4 matches is a small sample — these averages are noisy and should not be read as a tendency.

Model prediction

Why this prediction?

  • ·The model puts the most likely outcome at 38.6%: “SpVgg Greuther Fürth to win”. Second comes “1. FC Nürnberg to win” at 35.1%.
  • ·Behind that distribution are the expected goals: 1.53 for SpVgg Greuther Fürth, 1.46 for 1. FC Nürnberg.
  • ·The highest double chance value is 73.7% (12 — any result other than a draw).
  • ·These values were computed from 1,235 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.

SpVgg Greuther Fürth to win38.6%
Draw26.3%
1. FC Nürnberg to win35.1%

When the model has said 35–40% in the past, 1,562 of 4,187 predictions were correct (37.3%). Accuracy record →

P. Scobel
372
9Fouls15
1Yellow cards4
0Red cards0

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.

Both teams to score63.7%

When the model has said 60–65% in the past, 1,567 of 2,553 predictions were correct (61.4%). Accuracy record →

Double chance

1X — SpVgg Greuther Fürth win or draw64.9%
12 — any result other than a draw73.7%
X2 — draw or 1. FC Nürnberg win61.4%

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 (26.3%).

First half

Actual: 2–2
SpVgg Greuther Fürth lead at half-time23.7%
Level at half-time39.7%
1. FC Nürnberg lead at half-time36.6%

First-half double chance

1X — SpVgg Greuther Fürth ahead or level63.4%
12 — not level at half-time60.3%
X2 — level or 1. FC Nürnberg ahead76.3%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 1235 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

SpVgg Greuther Fürth
at least 11Actual: 9
1. FC Nürnberg
at least 11Actual: 15

Corners

SpVgg Greuther Fürth
at least 4Actual: 4
1. FC Nürnberg
at least 4Actual: 3

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

The 3+ goals prediction is not published for this match.

The model gives a probability below the baseline (59.0%). In backtesting the model's edge in the "fewer goals" direction could not be confirmed; we don't publish an unconfirmed prediction.

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 SpVgg Greuther Fürth score1.53

Attack: SpVgg Greuther Fürth score close to the league average.

Opponent's defence: 1. FC Nürnberg's defence is close to the league average (1.00×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.53 goals (including home advantage).

How many goals 1. FC Nürnberg score1.46

Attack: 1. FC Nürnberg score close to the league average.

Opponent's defence: SpVgg Greuther Fürth's defence is close to the league average (1.11×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.46 goals (including the away penalty).

Most likely scorelines

1–112.8%2–19.4%1–28.9%

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 1235 matches played in this league before kick-off; the team with fewer has 137 matches. No data from after the match was used. The model's track record is published unfiltered.

Week 2 of the season. This prediction was computed from previous seasons. Summer transfers, budget changes and squad rebuilds aren't in the ratings yet — the model can only see that a team has been rebuilt once new results come in.

We measured this: the bias is only pronounced in the opening week. In this week the model's overall accuracy does not separate from the rest of the season — that is, we found no measurable flaw. The one tendency across all opening weeks is that home wins are called slightly too often (said 43.0%, got 41.9%; n=583).

This sample is small and not statistically established, which is why we do not correct the model for it. The correction we tried (pulling probabilities toward the league average) didn't win on holdout, so it wasn't added. Read opening-week predictions with a little more caution.