Bundesliga · Germany · Regular Season - 15
HT 0–2
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
| 1. FC Heidenheim | Bayern München | |
|---|---|---|
| 7 | Shots | 23 |
| 1 | Shots on target | 11 |
| 5 | Shots inside the box | 14 |
| 6 | Goalkeeper saves | 1 |
| 0 | Corners | 6 |
| 2 | Offsides | 2 |
| 26% | Possession | 74% |
| 267 | Passes | 778 |
| 200 | Accurate passes |
3.14
yellows per match
0.00
reds per match
7
matches of data
7 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 80–85% in the past, 269 of 319 predictions were correct (84.3%). Accuracy record →
| 709 |
| 8 | Fouls | 6 |
|---|
| 0 | 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 55–60% in the past, 2,565 of 4,457 predictions were correct (57.5%). 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 (11.8%).
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 1052 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 60.8%. 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: 1. FC Heidenheim score below the league average (20% fewer).
Opponent's defence: Bayern München concede 42% below the league average — a defence that seriously brakes even a strong attack.
Result: These two effects give an expectation of 0.64 goals (including home advantage).
Attack: Bayern München score 82% more than the league's average team — one of the sharpest attacks in the league.
Opponent's defence: 1. FC Heidenheim concede 17% 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 2.90 goals.
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 1052 matches played in this league before kick-off; the team with fewer has 82 matches. No data from after the match was used. The model's track record is published unfiltered.