Serie A · Italy · Regular Season - 1

Inter–Monza

4–1

HT 1–1

Kick-off: Saturday, 22 August 2026 at 16:30
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

6'InterH. Calhanoglu (assist: Andy Diouf)
29'MonzaG. Varela (assist: Mathis Mout)
48'InterPiotr Zieliński
55'InterPio Esposito (assist: Andy Diouf)
63'InterYann Bisseck (assist: Pio Esposito)

Match statistics

Match statistics
Inter		Monza
9	Shots	5
4	Shots on target	2
6	Shots inside the box	5
1	Goalkeeper saves	1
2	Corners	1
0	Offsides	3
72%	Possession	28%
475	Passes	181
424	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Inter 2.41 · Monza 0.49. On the pitch: 4-1.
Shots Inter 9 · Monza 5; on target 4 and 2.
Inter kept the ball more (72%).

Unavailable players

Inter

D. Spenceout · not in squad
H. Mkhitaryanout · suspended
M. Spinacceout · injury

Monza

D. Motaout · suspended
D. Thiamout · yellow-card suspension
E. Akinsanmiroout · Muscle Injury
I. Toureout · Knee Injury
L. Colomboout · injury
M. Pessinaout · Knee Injury
P. Ciurriaout · not in squad

Provider data — may change until the line-ups are announced. Last updated: Thursday, 17 September 2026 at 06:10.

Referee

Livio Marinelli, Italy→

4.56

yellows per match

0.44

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 82.1%: “Inter to win”. Second comes “a draw” at 13.0%.
Behind that distribution are the expected goals: 2.64 for Inter, 0.58 for Monza.
The highest double chance value is 95.1% (1X — Inter win or draw).
These values were computed from 1,520 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.

Inter to win82.1%

Draw13.0%

Monza to win4.9%

When the model has said 80–85% in the past, 269 of 319 predictions were correct (84.3%). Accuracy record →

Both teams to score

4	Fouls	8

2	Yellow cards	0

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.

When the model has said 40–45% in the past, 469 of 1,102 predictions were correct (42.6%). Accuracy record →

Double chance

1X — Inter win or draw95.1%

12 — any result other than a draw87.0%

X2 — draw or Monza win17.9%

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 (13.0%).

First half

Actual: 1–1

Inter lead at half-time64.3%

Level at half-time32.4%

Monza lead at half-time3.3%

First-half double chance

1X — Inter ahead or level96.7%

12 — not level at half-time67.6%

X2 — level or Monza ahead35.7%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 1520 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

Inter: at least 12Actual: 4
Monza: at least 11Actual: 8

Corners

Inter: at least 6Actual: 2
Monza: at least 2Actual: 1

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+ goals63.6%

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 47.2%. 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 Inter score2.41

Attack: Inter score 58% more than the league's average team — one of the sharpest attacks in the league.

Opponent's defence: Monza concede 25% more than the league average, which pushes the expectation up.

Result: A strong attack meets a weak defence. The effects compound and the expectation rises to 2.41 goals.

How many goals Monza score0.49

Attack: Monza score below the league average (29% fewer).

Opponent's defence: Inter concede 21% fewer than the league average, which pulls the expectation down.

Result: These two effects give an expectation of 0.49 goals (including the away penalty).

Most likely scorelines

2–014.3%3–012.8%1–09.8%

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 1520 matches played in this league before kick-off; the team with fewer has 114 matches. No data from after the match was used. The model's track record is published unfiltered.

Week 1 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 and we're not hiding it: in the opening week of past seasons the model came in 3.3 points below the accuracy it claimed (n=220) — for the rest of the season it beats its own claim. The tendency points this way, though it is not proof on its own: in the opening week it slightly over-calls home wins (said 42.7%, got 39.5%) and slightly under-calls draws (said 26.1%, got 28.2%).

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.