Liga I · Romania · Championship Group - 3

Rapid–Universitatea Cluj

1–2

HT 1–0

Kick-off: Sunday, 5 April 2026 at 17:30
Status: Match Finished

Goals and red cards

20'RapidCătălin Vulturar (assist: C. Petrila)
66'Universitatea ClujO. Mendy (assist: Dino Mikanović)
83'Universitatea ClujO. Mendy (assist: J. Stanojev)

Match statistics

Match statistics
Rapid		Universitatea Cluj
13	Shots	18
5	Shots on target	5
7	Shots inside the box	14
3	Goalkeeper saves	4
6	Corners	8
0	Offsides	0
49%	Possession	51%
385	Passes	393
315	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Rapid 1.32 · Universitatea Cluj 1.14. On the pitch: 1-2.
Shots Rapid 13 · Universitatea Cluj 18; on target 5 and 5.

Referee

M. Barbu→

4.43

yellows per match

0.17

reds per match

matches of data

Model prediction

Why this prediction?

The model puts the most likely outcome at 39.9%: “Rapid to win”. Second comes “Universitatea Cluj to win” at 30.9%.
Behind that distribution are the expected goals: 1.32 for Rapid, 1.14 for Universitatea Cluj.
The highest double chance value is 70.8% (12 — any result other than a draw).
These values were computed from 1,057 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.

Rapid to win39.9%

Draw29.2%

Universitatea Cluj to win30.9%

When the model has said 35–40% in the past, 1,562 of 4,187 predictions were correct (37.3%). Accuracy record →

11	Fouls	9

3	Yellow cards	1

0	Red cards	0

First half

Actual: 1–0

Rapid lead at half-time32.4%

Level at half-time43.7%

Universitatea Cluj lead at half-time23.9%

First-half double chance

1X — Rapid ahead or level76.1%

12 — not level at half-time56.3%

X2 — level or Universitatea Cluj ahead67.6%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 1057 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

Rapid: at least 10Actual: 11
Universitatea Cluj: at least 11Actual: 9

Corners

Rapid: at least 5Actual: 6
Universitatea Cluj: at least 3Actual: 8

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 Rapid score1.32

Attack: Rapid score close to the league average.

Opponent's defence: Universitatea Cluj concede 18% fewer than the league average, which pulls the expectation down.

Result: These two effects give an expectation of 1.32 goals (including home advantage).

How many goals Universitatea Cluj score1.14

Attack: Universitatea Cluj score above the league average (15% more).

Opponent's defence: Rapid's defence is close to the league average (0.88×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.14 goals (including the away penalty).

Most likely scorelines

1–114.8%1–010.4%2–19.3%

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 3 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.