Czech Liga · Czech-Republic · Relegation Group - 3

Mlada Boleslav–Dukla Praha

1–2

HT 0–1

Kick-off: Tuesday, 12 May 2026 at 15:30
Status: Match Finished
Venue: Lokotrans Arena, Mlada Boleslav

Goals and red cards

4'Dukla PrahaK. Milla
55'Dukla PrahaDantaye Gilbert (assist: M. Pourzitidis)
62'Mlada BoleslavOndrej Karafiat (assist: M. Ševčík)

Match statistics

Match statistics
Mlada Boleslav		Dukla Praha
18	Shots	13
3	Shots on target	4
4	Shots inside the box	11
2	Goalkeeper saves	3
4	Corners	3
3	Offsides	2
68%	Possession	32%
620	Passes	290
545	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Mlada Boleslav 1.59 · Dukla Praha 0.88. On the pitch: 1-2.
Shots Mlada Boleslav 18 · Dukla Praha 13; on target 3 and 4.
Mlada Boleslav kept the ball more (68%).

Referee

S. Volek→

4.39

yellows per match

0.37

reds per match

matches of data

Model prediction

Why this prediction?

The model puts the most likely outcome at 53.9%: “Mlada Boleslav to win”. Second comes “a draw” at 26.7%.
Behind that distribution are the expected goals: 1.59 for Mlada Boleslav, 0.88 for Dukla Praha.
The highest double chance value is 80.6% (1X — Mlada Boleslav win or draw).
These values were computed from 1,010 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.

Mlada Boleslav to win53.9%

Draw26.7%

Dukla Praha to win19.4%

When the model has said 50–55% in the past, 1,913 of 3,689 predictions were correct (51.9%). Accuracy record →

10	Fouls	16

0	Yellow cards	2

0	Red cards	0

First half

Actual: 0–1

Mlada Boleslav lead at half-time35.0%

Level at half-time42.7%

Dukla Praha lead at half-time22.2%

First-half double chance

1X — Mlada Boleslav ahead or level77.8%

12 — not level at half-time57.3%

X2 — level or Dukla Praha ahead65.0%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 1010 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

Mlada Boleslav: at least 11Actual: 10
Dukla Praha: at least 13Actual: 16

Corners

Mlada Boleslav: at least 5Actual: 4
Dukla Praha: at least 3Actual: 3

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 Mlada Boleslav score1.59

Attack: Mlada Boleslav score close to the league average.

Opponent's defence: Dukla Praha's defence is close to the league average (1.13×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.59 goals (including home advantage).

How many goals Dukla Praha score0.88

Attack: Dukla Praha score below the league average (39% fewer).

Opponent's defence: Mlada Boleslav's defence is close to the league average (1.14×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 0.88 goals (including the away penalty).

Most likely scorelines

1–113.5%1–012.7%2–010.9%

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 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.