Ligue 2 · France · Regular Season - 1

Montpellier–Dijon

1–1

HT 0–0

Kick-off: Saturday, 8 August 2026 at 18:45
Status: Match Finished

What the model said, and what happened

Ended level — we gave the draw 31%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

Goals and red cards

90'MontpellierLacine Megnan-Pave
90'DijonA. Lembezat

Match statistics

Match statistics
Montpellier		Dijon
22	Shots	10
5	Shots on target	4
10	Shots inside the box	4
3	Goalkeeper saves	4
4	Corners	0
0	Offsides	1
55%	Possession	45%
434	Passes	359
344	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Montpellier 1.25 · Dijon 0.94. On the pitch: 1-1.
Shots Montpellier 22 · Dijon 10; on target 5 and 4.
Montpellier kept the ball more (55%).

Referee

Remi Landry, France→

5.00

yellows per match

0.00

reds per match

matches of data

4 matches is a small sample — these averages are noisy and should not be read as a tendency.

Model prediction

Why this prediction?

The model puts the most likely outcome at 42.9%: “Montpellier to win”. Second comes “a draw” at 30.6%.
Behind that distribution are the expected goals: 1.25 for Montpellier, 0.94 for Dijon.
The highest double chance value is 73.5% (1X — Montpellier win or draw).
These values were computed from 1,370 past matches.

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.

Montpellier to win42.9%

Draw30.6%

Dijon to win26.5%

When the model has said 40–45% in the past, 1,778 of 3,998 predictions were correct (44.5%). Accuracy record →

20	Fouls	18

3	Yellow cards	3

0	Red cards	0

First half

Actual: 0–0

Montpellier lead at half-time30.6%

Level at half-time47.6%

Dijon lead at half-time21.8%

First-half double chance

1X — Montpellier ahead or level78.2%

12 — not level at half-time52.4%

X2 — level or Dijon ahead69.4%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 1370 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.

Expected fouls and corners

Fouls

Montpellier: at least 14Actual: 20
Dijon: at least 12Actual: 18

Corners

Montpellier: at least 4
Dijon: at least 4

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.

What these probabilities rest on

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.

How many goals Montpellier score1.25

Attack: Montpellier score close to the league average.

Opponent's defence: Dijon concede 17% fewer than the league average, which pulls the expectation down.

Result: These two effects give an expectation of 1.25 goals (including home advantage).

How many goals Dijon score0.94

Attack: Dijon score below the league average (21% fewer).

Opponent's defence: Montpellier's defence is close to the league average (0.89×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 0.94 goals (including the away penalty).

Most likely scorelines

1–115.2%1–013.3%0–010.9%

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.

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.