Serie A · Italy · Regular Season - 38
HT 2–0
Ended level — we gave the draw 9%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
| AC Milan | Salernitana | |
|---|---|---|
| 23 | Shots | 14 |
| 11 | Shots on target | 6 |
| 22 | Shots inside the box | 7 |
| 3 | Goalkeeper saves | 8 |
| 13 | Corners | 3 |
| 1 | Offsides | 1 |
| 66% | Possession | 34% |
| 535 | Passes | 276 |
| 467 | Accurate passes |
4.87
yellows per match
0.07
reds per match
15
matches of data
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.
When the model has said 85–90% in the past, 146 of 165 predictions were correct (88.5%). Accuracy record →
| 217 |
| 6 | Fouls | 9 |
|---|
| 0 | Yellow cards | 1 |
|---|
| 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 50–55% in the past, 2,597 of 5,092 predictions were correct (51.0%). Accuracy record →
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 (8.9%).
First-half double chance
These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 752 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.
Fouls
Corners
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.
When the model has said 75–80% in the past, 131 of 170 predictions were correct (77.1%). 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.
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.
Attack: AC Milan score above the league average (41% more).
Opponent's defence: Salernitana concede 48% more than the league average — the defence is the weakest link in this match.
Result: A strong attack meets a weak defence. The effects compound and the expectation rises to 2.75 goals.
Attack: Salernitana score below the league average (30% fewer).
Opponent's defence: AC Milan's defence is close to the league average (0.95×), so it doesn't move the expectation much.
Result: These two effects give an expectation of 0.51 goals (including the away penalty).
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 752 matches played in this league before kick-off; the team with fewer has 75 matches. No data from after the match was used. The model's track record is published unfiltered.