Serie B · Italy · Regular Season - 2
HT 1–2
| Virtus Entella | Cesena | |
|---|---|---|
| 14 | Shots | 7 |
| 3 | Shots on target | 3 |
| 6 | Shots inside the box | 5 |
| 1 | Goalkeeper saves | 2 |
| 6 | Corners | 2 |
| 2 | Offsides | 1 |
| 59% | Possession | 41% |
| 514 | Passes | 371 |
| 437 | Accurate passes |
3.67
yellows per match
0.11
reds per match
9
matches of data
9 matches is a small sample — these averages are noisy and should not be read as a tendency.
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 45–50% in the past, 1,541 of 3,251 predictions were correct (47.4%). Accuracy record →
| 294 |
| 8 | Fouls | 18 |
|---|
| 1 | Yellow cards | 2 |
|---|
| 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 (28.3%).
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 1531 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.
The 3+ goals prediction is not published for this match.
The model gives a probability below the baseline (46.0%). In backtesting the model's edge in the "fewer goals" direction could not be confirmed; we don't publish an unconfirmed prediction.
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: Virtus Entella score below the league average (17% fewer).
Opponent's defence: Cesena's defence is close to the league average (1.02×), so it doesn't move the expectation much.
Result: These two effects give an expectation of 1.30 goals (including home advantage).
Attack: Cesena score close to the league average.
Opponent's defence: Virtus Entella's defence is close to the league average (0.94×), so it doesn't move the expectation much.
Result: These two effects give an expectation of 1.07 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 1531 matches played in this league before kick-off; the team with fewer has 39 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.