· Australian Championship
South Melbourne
ATT
5/10
DEF
7/10
1:1
Sydney United
5/10
ATT
5/10
DEF
Full-time
Best Pick
BTTS
55%
Correct Score90′
Top
1-1
10.9%
8.00
Top
1-0
10.1%
8.00
2-1
9.8%
8.50
2-0
9.2%
8.50
0-1
6%
13.00
3-1
5.9%
13.00
1-2
5.8%
15.00
0-0
5.6%
13.00
155%
1.83TOP
X23%
4.34
222%
4.48
1X78%
X245%
1277%
per 15 min
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
South Melbourne
Sydney United
Probable XI4-4-2
Charlston
Ramazani
Lampard
Marshall
Painter-Andrews
Menelaou
Leech
Polyzoudis
Yoo
Skapoulas
Mesourouni
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
South Melbourne31 players
No squad metrics available
Jake CharlstonGK
Javier LópezGK
Hassan RamazaniDEF
J. LampardDEF
J. MarshallDEF
J. Painter-AndrewsDEF
Jacob EliopoulosDEF
M. JankovicDEF
R. NkomoDEF
Sasha MurphyDEF
Thomas GiannakopoulosDEF
Alexander MenelaouMID
C. LeechMID
Sydney United7 players
50.3/ 99Average
Top Rated
1Tomoki WadaMID57
2B. RuleDEF50
3Liam McGingDEF50
South Melbourne31
PlayerOverall RatingImpactAggressionDiscipline
Jake Charlston————
Javier López————
Hassan Ramazani————
J. Lampard————
J. Marshall————
J. Painter-Andrews————
Jacob Eliopoulos————
M. Jankovic————
R. Nkomo————
Sasha Murphy————
Thomas Giannakopoulos————
Alexander Menelaou————
C. Leech————
Sydney United30
PlayerOverall RatingImpactAggressionDiscipline
Tomoki Wada57586050
B. Rule50485450
Liam McGing50515050
Tomislav Ozanic50465850
Koya Nakano49465650
M. Vlastelica48494550
Carlos De Oliveria48494550
A. Pavlesic————
Adrian-Jamie Bazina————
Ethan Troselj————
L. Cukar————
Mitchell Carter————
Anthony Krilic————
Player Rankings
Wall
Top defenders
1
Liam McGingSydney United
51
2
M. VlastelicaSydney United
49
3
B. RuleSydney United
48
4
Koya NakanoSydney United
46
Control
Top playmakers
1
Tomoki WadaSydney United
58
2
Carlos De OliveriaSydney United
49
3
Tomislav OzanicSydney United
46
Bad Boys
Most likely to get booked
1
B. RuleSydney United
50
2
Koya NakanoSydney United
50
3
Liam McGingSydney United
50
4
M. VlastelicaSydney United
50
5
Carlos De OliveriaSydney United
50
Power Rankings
Strongest overall
1
Tomoki WadaSydney United
57
2
B. RuleSydney United
50
3
Liam McGingSydney United
50
4
Tomislav OzanicSydney United
50
5
Koya NakanoSydney United
49

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for South Melbourne vs Sydney United 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 South Melbourne vs Sydney United 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 South Melbourne 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 South Melbourne vs Sydney United 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 South Melbourne 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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