· UEFA Nations League
Gibraltar
ATT
4/10
DEF
6/10
vs
Malta
5/10
ATT
4/10
DEF
Naplánováno· Mon 16 Nov · 19:45
Nejlepší tip
Pod 2.5
58%
Přesný výsledek
Výborná
1-1
13%
7.71
Výborná
1-0
12.6%
7.94
0-1
9.8%
10.18
0-0
9.5%
10.47
2-1
8.6%
11.68
2-0
8.3%
12.02
1-2
6.7%
14.99
0-2
5.1%
19.78
143%
2.31TOP
X28%
3.62
229%
3.44
1X71%
X257%
1272%
Probable XI4-4-2
1
Banda
21
Pons
6
Lopes
5
Bent
20
Britto
25
Haro
22
Torrilla
16
Stevens
Coombes
Hmidi
7
Richards
Impact Player Hot Head
Sestavy ještě nebyly oznámeny — níže kompletní analýza kádru
Gibraltar18 hráči
49.4/ 99Průměrný
Nejlepší
1Bernardo LopesDEF62
2Ethan JolleyDEF60
3Julian ValarinoDEF59
Malta18 hráči
51.8/ 99Průměrný
Nejlepší
1Teddy TeumaMID73
2Brandon PaiberMID72
3Matthew Guillaumier MID68
Gibraltar34
PlayerCelkové HodnoceníImpaktAgreseDisciplína
6
Bernardo Lopes62713761
2
Ethan Jolley60565661
3
Julian Valarino59515467
21
Kai Mauro55653950
5
Dan Bent53684538
1
Bradley Banda52524550
12
Jesse Gomez52455956
18
Jaiden Bartolo52544550
11
James Scanlon51465261
23
Jaylan Hankins50513450
21
Alain Pons50524550
7
Carlos Richards48455256
16
Han Stevens46454550
Malta32
PlayerCelkové HodnoceníImpaktAgreseDisciplína
10
Teddy Teuma73575489
6
Brandon Paiber72765267
6
Matthew Guillaumier 68575378
18
Carlo Zammit Lonardelli 64557556
8
Paul Mbong63539172
5
Kurt Shaw56575154
15
Myles Beerman55624550
4
Gabriel Mentz54554556
19
Trent Buhagiar54545850
9
Jake Grech51525250
1
Henry Bonello47462350
2
Jean Borg 47484550
2
Adam Magri Overend45454550
Žebříčky hráčů
Střelci
Největší gólová hrozba
1
Jaiden BartoloGibraltar
54
2
Trent BuhagiarMalta
54
3
Paul MbongMalta
53
4
James ScanlonGibraltar
46
5
Carlos RichardsGibraltar
45
Zeď
Nejlepší obránci
1
Bernardo LopesGibraltar
71
2
Dan BentGibraltar
68
3
Kai MauroGibraltar
65
4
Myles BeermanMalta
62
5
Kurt ShawMalta
57
Kontrola
Nejlepší tvůrci hry
1
Brandon PaiberMalta
76
2
Matthew Guillaumier Malta
57
3
Teddy TeumaMalta
57
4
Jake GrechMalta
52
5
Han StevensGibraltar
45
Zlobivci
Nejpravděpodobnější žlutá karta
1
Nicholas PozoGibraltar
26
2
Enrico PepeMalta
26
3
Graeme TorrillaGibraltar
30
4
Kian RonanGibraltar
32
5
Zach MuscatMalta
32
Žebříček síly
Nejsilnější celkově
1
Teddy TeumaMalta
73
2
Brandon PaiberMalta
72
3
Matthew Guillaumier Malta
68
4
Carlo Zammit Lonardelli Malta
64
5
Paul MbongMalta
63

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Gibraltar vs Malta based on each team's expected goals (xG). The top-3 most likely scorelines are shown above with their probability percentages. Even the highest-probability score rarely exceeds 12-15% chance, as football is a low-scoring sport.

The Bet Builder combines multiple selections from the Gibraltar vs Malta match into one bet. Our AI identifies outcomes that complement each other — for example, a home win paired with over 2.5 goals and both teams to score. All selections must win for the combined bet to pay out, which means higher combined odds but also higher risk.

xG (expected goals) measures the quality of scoring chances. If Gibraltar has an xG of 1.8, it means they are expected to score around 1-2 goals based on chance quality. The model uses pre-match xG to build a probability table for all possible scorelines. A team can overperform xG (score more than expected) or underperform xG (score fewer).

Yes, all predictions on ProSoccer are completely free. You can view predicted scorelines, over/under probabilities, and bet builder suggestions for Gibraltar vs Malta without paying or registering. Odds values are revealed after a tap to keep the page compliant with search engine guidelines.

A value bet occurs when the probability of an outcome is higher than what the bookmaker odds suggest. For example, if our model gives Gibraltar a 55% chance of winning but the odds imply only 40%, that is a value bet. Our AI highlights selections where the model probability exceeds the implied probability from the odds.

AI predictions use statistical modeling (Poisson distribution) based on historical data and pre-match metrics. They are more reliable over large samples than for any single match. No prediction is guaranteed — football has inherent unpredictability. Our model typically identifies the correct top-3 scorelines in 25-30% of matches, which is significantly better than random chance.

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