· UEFA Nations League
Romania
ATT
6/10
DEF
4/10
vs
Sweden
5/10
ATT
5/10
DEF
Scheduled· Mon 5 Oct · 18:45
Best Pick
Under 2.5
54%
Correct Score
Top
1-1
12.4%
8.06
Top
1-0
12%
8.34
2-1
9.2%
10.93
2-0
8.8%
11.31
0-1
8.4%
11.89
0-0
8.1%
12.30
1-2
6.4%
15.57
2-2
4.7%
21.12
147%
2.11TOP
X26%
3.82
227%
3.77
1X73%
X253%
1274%
Probable XI4-4-2
Radu
Rus
Borza
Burcă
Coubiș
Cicâldău
Cîrjan
Olaru
Tănase
Dobre
Petrila
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Romania20 players
50.9/ 99Average
Top Rated
1Lisav Naif EissatDEF65
2Florin TănaseMID61
3Andrei CoubișDEF57
Sweden15 players
51.2/ 99Average
Top Rated
1Yasin AyariMID68
2Lucas BergvallMID65
3Alexander BernhardssonFWD56
Romania46
PlayerOverall RatingImpactAggressionDiscipline
Lisav Naif Eissat65476078
Florin Tănase61497361
Andrei Coubiș57466559
Daniel Bîrligea57584756
Radu Drăgușin  56505461
Vlad Dragomir55514761
Tudor Băluță54575050
Nicolae Stanciu53544556
Tony Strata51564550
Virgil Ghiță51535943
Alexandru Cicâldău51524750
Andrei Burcă49514750
Darius Olaru48494550
Sweden36
PlayerOverall RatingImpactAggressionDiscipline
Yasin Ayari68585478
Lucas Bergvall65525478
Alexander Bernhardsson56566150
Carl Starfelt 52524556
Victor Lindelöf52544256
Alexander Isak52524756
Viktor Johansson50513950
Hjalmar Ekdal50544550
Gabriel Gudmundsson49474456
Viktor Gyökeres49504350
B. Zeneli48484750
Hugo Larsson48494550
Emil Holm 47464750
Player Rankings
Snipers
Top goal threats
1
Daniel BîrligeaRomania
58
2
Alexander BernhardssonSweden
56
3
Dennis ManRomania
52
4
Alexander IsakSweden
52
5
Viktor GyökeresSweden
50
Wall
Top defenders
1
Tony StrataRomania
56
2
Hjalmar EkdalSweden
54
3
Victor LindelöfSweden
54
4
Virgil GhițăRomania
53
5
Carl Starfelt Sweden
52
Control
Top playmakers
1
Yasin AyariSweden
58
2
Tudor BăluțăRomania
57
3
Nicolae StanciuRomania
54
4
Alexandru CicâldăuRomania
52
5
Lucas BergvallSweden
52
Bad Boys
Most likely to get booked
1
Dennis ManRomania
20
2
Răzvan MarinRomania
24
3
Daniel SvenssonSweden
26
4
Virgil GhițăRomania
43
5
Ionuț RaduRomania
50
Power Rankings
Strongest overall
1
Yasin AyariSweden
68
2
Lisav Naif EissatRomania
65
3
Lucas BergvallSweden
65
4
Florin TănaseRomania
61
5
Andrei CoubișRomania
57

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Romania vs Sweden 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 Romania vs Sweden 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 Romania 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 Romania vs Sweden 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 Romania 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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