· AFC Cup
Adelaide United
ATT
5/10
DEF
6/10
vs
Lion City Sailors FC
5/10
ATT
6/10
DEF
Scheduled· Thu 29 Oct · 07:45
Best Pick
BTTS
55%
Correct Score
Top
1-1
11.6%
8.62
Top
1-0
10.3%
9.74
2-1
9.5%
10.49
2-0
8.4%
11.84
0-1
7%
14.19
1-2
6.5%
15.28
0-0
6.2%
16.02
2-2
5.4%
18.57
149%
2.03TOP
X24%
4.09
226%
3.82
1X73%
X250%
1275%
Probable XI4-4-2
Cox
Vriends
Minster
Pierias
Talladira
Anselmo
Alagich
Barnett
Yull
Riak
Tatu
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Adelaide United14 players
50.4/ 99Average
Top Rated
1Harry CrawfordDEF59
2Sotiri PhillisDEF59
3Ethan AlagichMID58
Lion City Sailors FC20 players
49.6/ 99Average
Top Rated
1Giovanni HaagMID65
2Kyoga NakamuraMID65
3Hugo GomesDEF58
Adelaide United31
PlayerOverall RatingImpactAggressionDiscipline
Harry Crawford59495667
Sotiri Phillis59525067
Ethan Alagich58564961
Craig Goodwin58625250
Amlani Tatu57575256
Luke Duzel55575450
Brody Burkitt55495767
J. Smits53543950
Fabian Talladira49454756
Jonny Yull49464456
Feyzo Kasumovic46454550
Panagiotis Kikianis46485142
Ajak Riak3245622
Lion City Sailors FC28
PlayerOverall RatingImpactAggressionDiscipline
Giovanni Haag65575472
Kyoga Nakamura65456578
Hugo Gomes58446464
Bailey Wright56454772
Antonio Mance55515361
Ilhan Fandi54524961
Victor Aznar53533950
Diogo Costa53435959
Tsiy William Ndenge53524756
J. Karse52454961
Ivan Sušak51513450
Shawal Anuar47349661
Hariss Harun46454550
Player Rankings
Snipers
Top goal threats
1
Craig GoodwinAdelaide United
62
2
Amlani TatuAdelaide United
57
3
Glenn KwehLion City Sailors FC
53
4
Ilhan FandiLion City Sailors FC
52
5
Antonio ManceLion City Sailors FC
51
Wall
Top defenders
1
Sotiri PhillisAdelaide United
52
2
Toni DatkovićLion City Sailors FC
51
3
Harry CrawfordAdelaide United
49
4
Panagiotis KikianisAdelaide United
48
5
Lionel TanLion City Sailors FC
48
Control
Top playmakers
1
Luke DuzelAdelaide United
57
2
Giovanni HaagLion City Sailors FC
57
3
Ethan AlagichAdelaide United
56
4
Tsiy William NdengeLion City Sailors FC
52
5
Jonny YullAdelaide United
46
Bad Boys
Most likely to get booked
1
Ajak RiakAdelaide United
2
2
Glenn KwehLion City Sailors FC
2
3
AnselmoAdelaide United
13
4
Song Ui YoungLion City Sailors FC
24
5
Lionel TanLion City Sailors FC
31
Power Rankings
Strongest overall
1
Giovanni HaagLion City Sailors FC
65
2
Kyoga NakamuraLion City Sailors FC
65
3
Harry CrawfordAdelaide United
59
4
Sotiri PhillisAdelaide United
59
5
Ethan AlagichAdelaide United
58

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Adelaide United vs Lion City Sailors FC 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 Adelaide United vs Lion City Sailors FC 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 Adelaide United 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 Adelaide United vs Lion City Sailors FC 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 Adelaide United 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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