Both teams have a clear route to goal: Austria Vienna’s solid attack should trouble WSG Tirol’s leaky back line, while Tirol’s home threat can exploit Vienna’s defensive gaps. Even if one side starts slowly, the open matchup points to chances at both ends—both teams to score looks the most natural call.
Both Teams to ScoreYes
Best Pick
BTTS
55%
Correct Score
Top
1-1
12.3%
6.50
Top
1-0
9.1%
11.00
0-1
9.1%
11.00
2-1
8.3%
10.00
1-2
8.2%
10.00
0-0
6.7%
12.00
2-0
6.2%
15.00
0-2
6.1%
15.00
137%
2.63TOP
X26%
3.40
237%
2.55TOP
1X63%
X263%
1274%
Combined Bet Builder
4 legs
Austria Vienna’s stronger attack (+15%) and WSG Tirol’s weak scoring profile (-26%) favour Draw or Austria Vienna, while both leaky defences support Over 2.5 goals. First-half xG totals 1.34, pointing to an early goal, and combined xCorners of 10.35 make Over 8.5 corners a logical fit.
1Double ChanceDraw or Austria Vienna
2Match GoalsOver 2.5
3Corners Over/UnderOver 8.5
4First Half Over/UnderOver 0.5
Confirmed XI4-3-3
1
Hamzic
5
Murray
23
Boras
14
Kubatta
28
Oswald
6
Striednig
30
Taferner
11
Satin
37
Wels
36
Goiginger
8
Frederiksen
Impact Player Hot Head
WSG Tirol29 players
45.0/ 99Average
Top Rated
1David GugganigDEF77
2N. FrederiksenFWD72
3Matthäus TafernerMID68
Austria Vienna35 players
49.6/ 99Average
Top Rated
1Marko RaguzFWD70
2Manfred Fischer MID69
3Maurice MaloneFWD67
Player Rankings
Snipers
Top goal threats
1
N. FrederiksenWSG Tirol
87
2
Marko RaguzAustria Vienna
76
3
Kelvin Boateng Austria Vienna
65
4
Maurice MaloneAustria Vienna
64
5
Manprit SarkariaAustria Vienna
51
Wall
Top defenders
1
Jamie LawrenceWSG Tirol
70
2
I. BuhariAustria Vienna
66
3
David GugganigWSG Tirol
64
4
Johannes HandlAustria Vienna
64
5
Tin PlavoticAustria Vienna
62
Control
Top playmakers
1
Matthäus TafernerWSG Tirol
75
2
Dominik FitzAustria Vienna
74
3
Marijan ÖsterreicherAustria Vienna
72
4
Manfred Fischer Austria Vienna
62
5
Abubakr BarryAustria Vienna
60
Bad Boys
Most likely to get booked
1
Moritz OswaldWSG Tirol
1
Aggression 64
2
Yannick VötterWSG Tirol
1
Aggression 45
3
Johannes HandlAustria Vienna
1
Aggression 52
4
Dominik FitzAustria Vienna
1
Aggression 53
5
David KubattaWSG Tirol
11
Aggression 49
Power Rankings
Strongest overall
1
David GugganigWSG Tirol
77
2
N. FrederiksenWSG Tirol
72
3
Marko RaguzAustria Vienna
70
4
Manfred Fischer Austria Vienna
69
5
Matthäus TafernerWSG Tirol
68
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for WSG Tirol vs Austria Vienna 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 WSG Tirol vs Austria Vienna 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 WSG Tirol 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 WSG Tirol vs Austria Vienna 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 WSG Tirol 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.