Serie B · Italy · Regular Season - 3

Cremonese–Padova

3–1

HT 2–1

Kick-off: Sunday, 6 September 2026 at 17:30
Status: Match Finished
Venue: Stadio Giovanni Zini, Cremona

Goals and red cards

1'CremoneseF. Bonazzoli (assist: Adin Ličina)
5'CremoneseAdin Ličina (assist: T. Berti)
45+1'PadovaM. Pompetti (assist: L. Carissoni)
56'CremoneseF. Bonazzoli (assist: J. Vandeputte)

Match statistics

Match statistics
Cremonese		Padova
10	Shots	12
6	Shots on target	7
6	Shots inside the box	9
6	Goalkeeper saves	3
3	Corners	5
1	Offsides	8
54%	Possession	46%
565	Passes	453
481	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Cremonese 1.88 · Padova 0.68. On the pitch: 3-1.
Shots Cremonese 10 · Padova 12; on target 6 and 7.

Referee

Gianluca Manganiello, Italy→

2.89

yellows per match

0.11

reds per match

matches of data

9 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 50.6%: “Cremonese to win”. Second comes “a draw” at 27.0%.
Behind that distribution are the expected goals: 1.58 for Cremonese, 0.99 for Padova.
The highest double chance value is 77.6% (1X — Cremonese win or draw).
These values were computed from 1,548 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.

Cremonese to win50.6%

Draw27.0%

Padova to win22.4%

When the model has said 50–55% in the past, 1,337 of 2,542 predictions were correct (52.6%). Accuracy record →

8	Fouls	6

0	Yellow cards	0

0	Red cards	0

First half

Actual: 2–1

Cremonese lead at half-time26.1%

Level at half-time44.2%

Padova lead at half-time29.7%

First-half double chance

1X — Cremonese ahead or level70.3%

12 — not level at half-time55.8%

X2 — level or Padova ahead73.9%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 1548 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

Cremonese: at least 13Actual: 8
Padova: at least 13Actual: 6

Corners

Cremonese: at least 6Actual: 3
Padova: at least 2Actual: 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.

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 Cremonese score1.88

Attack: Cremonese score above the league average (15% more).

Opponent's defence: Padova'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 1.88 goals (including home advantage).

How many goals Padova score0.68

Attack: Padova score below the league average (19% fewer).

Opponent's defence: Cremonese concede 24% fewer than the league average, which pulls the expectation down.

Result: These two effects give an expectation of 0.68 goals (including the away penalty).

Most likely scorelines

1–113.7%1–011.3%2–110.4%

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.

Week 3 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.