Serie A · Italy · Regular Season - 36
HT 0–1
Ended level — we gave the draw 11%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
| Juventus | Salernitana | |
|---|---|---|
| 25 | Shots | 13 |
| 6 | Shots on target | 4 |
| 9 | Shots inside the box | 7 |
| 3 | Goalkeeper saves | 5 |
| 9 | Corners | 4 |
| 3 | Offsides | 1 |
| 67% | Possession | 33% |
| 515 | Passes | 258 |
| 444 | Accurate passes |
5.66
yellows per match
0.21
reds per match
29
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 80–85% in the past, 269 of 319 predictions were correct (84.3%). Accuracy record →
| 186 |
| 15 | Fouls | 12 |
|---|
| 2 | Yellow cards | 5 |
|---|
| 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 45–50% in the past, 1,425 of 3,100 predictions were correct (46.0%). 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 (11.2%).
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 735 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 70–75% in the past, 405 of 553 predictions were correct (73.2%). Accuracy record →
The league's historical "3+ goals" rate is 47.3%. For this match the model gives a value above that baseline — and in backtesting the model's edge was confirmed only in this direction.
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: Juventus score close to the league average.
Opponent's defence: Salernitana concede 48% more than the league average — the defence is the weakest link in this match.
Result: These two effects give an expectation of 2.29 goals (including home advantage).
Attack: Salernitana score below the league average (30% fewer).
Opponent's defence: Juventus concede 32% below the league average — a defence that seriously brakes even a strong attack.
Result: These two effects give an expectation of 0.49 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 735 matches played in this league before kick-off; the team with fewer has 73 matches. No data from after the match was used. The model's track record is published unfiltered.