NB I · Hungary · Regular Season - 1
HT 0–1
4.38
yellows per match
0.31
reds per match
13
matches of data
13 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 40–45% in the past, 2,356 of 5,296 predictions were correct (44.5%). Accuracy record →
When the model has said 65–70% in the past, 764 of 1,176 predictions were correct (65.0%). 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 (25.7%).
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 792 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 60–65% in the past, 1,726 of 2,749 predictions were correct (62.8%). Accuracy record →
The league's historical "3+ goals" rate is 55.9%. 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: Nyiregyhaza score close to the league average.
Opponent's defence: Ujpest's defence is close to the league average (1.12×), so it doesn't move the expectation much.
Result: These two effects give an expectation of 1.65 goals (including home advantage).
Attack: Ujpest score close to the league average.
Opponent's defence: Nyiregyhaza's defence is close to the league average (1.11×), so it doesn't move the expectation much.
Result: These two effects give an expectation of 1.44 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 792 matches played in this league before kick-off; the team with fewer has 66 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.