Super League 1 · Greece · Regular Season - 20

Larisa–Panetolikos

1–4

HT 1–3

Kick-off: Saturday, 7 February 2026 at 15:00
Status: Match Finished

What the model said, and what happened

Incorrect — we gave this result 17%

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

2'PanetolikosY. Badji (assist: Farley Rosa)
23'PanetolikosEsteban Diego
31'LarisaL. Garate
45'PanetolikosFarley Rosa (assist: A. Matan)
74'PanetolikosL. Smyrlis (assist: Y. Badji)

Match statistics

Match statistics
Larisa		Panetolikos
11	Shots	8
2	Shots on target	5
5	Shots inside the box	5
1	Goalkeeper saves	1
5	Corners	2
2	Offsides	0
69%	Possession	31%
539	Passes	243
464	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Larisa 1.58 · Panetolikos 0.80. On the pitch: 1-4.
Shots Larisa 11 · Panetolikos 8; on target 2 and 5.
Larisa kept the ball more (69%).

Referee

C. Vergetis→

5.69

yellows per match

0.25

reds per match

matches of data

Model prediction

Why this prediction?

The model puts the most likely outcome at 55.7%: “Larisa to win”. Second comes “a draw” at 26.8%.
Behind that distribution are the expected goals: 1.58 for Larisa, 0.80 for Panetolikos.
The highest double chance value is 82.5% (1X — Larisa win or draw).
These values were computed from 749 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.

Larisa to win55.7%

Draw26.8%

Panetolikos to win17.5%

When the model has said 55–60% in the past, 1,651 of 2,774 predictions were correct (59.5%). Accuracy record →

Both teams to score

16	Fouls	19

4	Yellow cards	2

0	Red cards	0

First half

Actual: 1–3

Larisa lead at half-time40.5%

Level at half-time43.7%

Panetolikos lead at half-time15.8%

First-half double chance

1X — Larisa ahead or level84.2%

12 — not level at half-time56.3%

X2 — level or Panetolikos ahead59.5%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 748 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

Larisa: at least 14Actual: 16
Panetolikos: at least 12Actual: 19

Corners

Larisa: at least 4Actual: 5
Panetolikos: at least 3Actual: 2

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 Larisa score1.58

Attack: Larisa score close to the league average.

Opponent's defence: Panetolikos concede 17% more than the league average, which pushes the expectation up.

Result: These two effects give an expectation of 1.58 goals (including home advantage).

How many goals Panetolikos score0.80

Attack: Panetolikos score below the league average (43% fewer).

Opponent's defence: Larisa'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 0.80 goals (including the away penalty).

Most likely scorelines

1–014.1%1–113.4%2–011.9%

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.