· Liga 1
Adhyaksa
ATT
4/10
DEF
5/10
0:0
PSIM日惹
4/10
ATT
6/10
DEF
直播 62′
最佳推荐
两队进球
54%
精确比分62′
顶级
1-1
12%
7.50
顶级
1-0
10.1%
12.00
2-1
9.2%
12.00
0-1
7.9%
9.50
2-0
7.7%
17.00
1-2
7.1%
10.00
0-0
6.7%
13.00
2-2
5.4%
15.00
145%
3.75TOP
X25%
2.25
230%
2.63
1X70%
X255%
1275%
进球概率更新中
56%
Before FT
Next 5'
13%
Next 10'
25%
Adhyaksa xG
0.85
exp 0.81+0.04
PSIM日惹 xG
1.81
exp 0.81+1.00
Quiet
xG — Expected Goals
Adhyaksa
Adhyaksa exp
PSIM日惹
PSIM日惹 exp
goal
Adhyaksa9Avg 15 · Peak 45
Tactical
PSIM日惹9Avg 25 · Peak 44
Momentum
0'15'30'HT60'75'90'
GoalShot
每15分钟
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
Adhyaksa
PSIM日惹
62′Trends
Dead Game
Adhyaksa
10-min momentum
PSIM日惹
38%62%
Adhyaksa xG
0.85
exp 0.86-0.01
PSIM日惹 xG
1.81
exp 0.86+0.95
Goal Probability Window
Next 5'
13%
Next 10'
25%
Next 15'
35%
Attack vs Defense
Adhyaksa Attack
3.6/10
PSIM日惹 Attack
3.9/10
Adhyaksa Defense
5.4/10
PSIM日惹 Defense
6.7/10
FT Predictions
大 0.5
19%
大 1.5
19%
大 2.5
5%
大 3.5
1%
小 1.5热门
81%
小 2.5热门
95%
小 3.5热门
99%
两队进球
10%
No more goals
44%
Next Goal
Adhyaksa scores next
20%
PSIM日惹 scores next
36%
No more goals热门
44%
Adhyaksa
2
exp 3.3-1.3
PSIM日惹
4
exp 3.3+0.7
Goal Probability Window
Next 5'
40%
Next 10'
64%
Next 15'
78%
FT Corners · 6 scored
大 9.5
32%
小 9.5热门
68%
大 8.5
54%
小 8.5
46%
大 7.5热门
77%
Next Corner
Adhyaksa wins next corner
50%
PSIM日惹 wins next corner
50%
Adhyaksa
0
exp 1.2-1.2
PSIM日惹
0
exp 1.2-1.2
总计
0/ 3.5
Late-game intensity
Mid
~0.6 remaining
0.6
Exp. FT
3.5
FT Cards Predictions
大 3.5
0%
小 3.5热门
100%
大 2.5
2%
小 2.5热门
98%
Live 1X2
Adhyaksa 胜
16%
平局热门
51%
PSIM日惹 胜
33%
Goals
大 2.5
5%
小 2.5热门
95%
两队进球
10%
Corners
大 9.5
32%
大 8.5
54%
Pressure Index
Adhyaksa9
PSIM日惹9
Confirmed XI5-4-1
1
Frigeri
18
Ramdani
33
Ribeiro
4
Lelis
37
Ramadhan
74
Cruz
12
Manneh
30
Pondaag
8
Abdillah
14
Nishioka
97
Baiano
Impact Player Hot Head
Adhyaksa22 球员
37.5/ 99发展中
最佳
1Lucas FrigeriGK55
2Muhammad FayrushiDEF55
3Léo LelisDEF54
PSIM日惹21 球员
49.1/ 99中等
最佳
1Muhammad IqbalMID87
2Zé ValenteMID69
3Yusaku YamaderaDEF64
球员排名
狙击手
最大进球威胁
1
Andi IrfanPSIM日惹
68
2
Mauro Andrés Caballero AguileraPSIM日惹
48
3
Sérgio MendonçaAdhyaksa
41
4
Adrián DalmauPSIM日惹
37
5
Adilson SilvaAdhyaksa
35
城墙
最佳后卫
1
Léo LelisAdhyaksa
62
2
Rafael RibeiroAdhyaksa
56
3
Muhammad FayrushiAdhyaksa
53
4
Franco Gastón Ramos MingoPSIM日惹
51
5
O. RudzanPSIM日惹
48
控制
最佳组织者
1
Muhammad IqbalPSIM日惹
90
2
Zé ValentePSIM日惹
90
3
Ezequiel VidalPSIM日惹
70
4
S. ShevaPSIM日惹
63
5
Ghulam Fatkur RahmanPSIM日惹
56
坏小子
最可能吃牌
1
Modou MannehAdhyaksa
2
2
Ghulam Fatkur RahmanPSIM日惹
13
3
Douglas CruzAdhyaksa
17
4
Brayen PondaagAdhyaksa
19
5
S. ShevaPSIM日惹
21
力量排名
综合最强
1
Muhammad IqbalPSIM日惹
87
2
Zé ValentePSIM日惹
69
3
Yusaku YamaderaPSIM日惹
64
4
Andi IrfanPSIM日惹
61
5
Lucas FrigeriAdhyaksa
55

常见问题

Our Poisson model calculates the probability of every possible scoreline for Adhyaksa vs PSIM日惹 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 Adhyaksa vs PSIM日惹 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 Adhyaksa 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 Adhyaksa vs PSIM日惹 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 Adhyaksa 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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