· Liga 1
Arema FC
ATT
6/10
DEF
5/10
2:2
Bhayangkara Presisi Indonesia FC
5/10
ATT
4/10
DEF
Konec
Nejlepší tip
OBÁ SKÓRUJÍ
55%
Přesný výsledek52′
Výborná
1-1
11.2%
7.00
Výborná
1-0
10.1%
9.00
2-1
9.7%
10.00
2-0
8.7%
11.00
0-1
6.5%
12.00
1-2
6.2%
13.00
0-0
5.9%
11.00
3-1
5.6%
17.00
152%
1.93TOP
X24%
4.21
224%
4.10
1X76%
X248%
1276%
xG — Očekávané góly
Arema FC
Arema FC oček.
Bhayangkara Presisi Indonesia FC
Bhayangkara Presisi Indonesia FC oček.
goal
za 15 min
0–15′
15–30′
30–45′
45–60′
60–75′
75–90′
Arema FC
Bhayangkara Presisi Indonesia FC
Confirmed XI4-3-3
30
Satryo
19
Syarif
3
Landis
5
Maia
87
Farisi
34
Otusanya
66
Yama
25
Blade
7
Silva
98
MV
95
Careca
Impact Player Hot Head
Arema FC23 hráči
50.6/ 99Průměrný
Nejlepší
1Teuku Razzaa Fachrezi AzizFWD83
2Robi DarwisMID73
3Hansamu YamaDEF65
Bhayangkara Presisi Indonesia FC25 hráči
48.2/ 99Průměrný
Nejlepší
1Nehar SadikiDEF75
2Moussa SidibéFWD71
3Frengky Deaner MissaDEF68
Žebříčky hráčů
Střelci
Největší gólová hrozba
1
Teuku Razzaa Fachrezi AzizArema FC
99
2
Moussa SidibéBhayangkara Presisi Indonesia FC
73
3
Ryan KurniaBhayangkara Presisi Indonesia FC
66
4
AllanoBhayangkara Presisi Indonesia FC
56
5
Sani Rizki FauziBhayangkara Presisi Indonesia FC
55
Zeď
Nejlepší obránci
1
Hansamu YamaArema FC
70
2
Nehar SadikiBhayangkara Presisi Indonesia FC
65
3
Diego Luiz LandisArema FC
64
4
Walisson MaiaArema FC
57
5
Frengky Deaner MissaBhayangkara Presisi Indonesia FC
51
Kontrola
Nejlepší tvůrci hry
1
Robi DarwisArema FC
99
2
Gustavo FrançaArema FC
74
3
Hanif SjahbandiArema FC
73
4
Ryo Matsumura Bhayangkara Presisi Indonesia FC
68
5
Arkhan FikriArema FC
56
Zlobivci
Nejpravděpodobnější žlutá karta
1
Salim Akbar TuhareaArema FC
1
2
AllanoBhayangkara Presisi Indonesia FC
1
3
Teuku IchsanBhayangkara Presisi Indonesia FC
7
4
Andhika WijayaBhayangkara Presisi Indonesia FC
14
5
Ryan KurniaBhayangkara Presisi Indonesia FC
25
Žebříček síly
Nejsilnější celkově
1
Teuku Razzaa Fachrezi AzizArema FC
83
2
Nehar SadikiBhayangkara Presisi Indonesia FC
75
3
Robi DarwisArema FC
73
4
Moussa SidibéBhayangkara Presisi Indonesia FC
71
5
Frengky Deaner MissaBhayangkara Presisi Indonesia FC
68

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Arema FC vs Bhayangkara Presisi Indonesia 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 Arema FC vs Bhayangkara Presisi Indonesia 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 Arema FC 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 Arema FC vs Bhayangkara Presisi Indonesia 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 Arema FC 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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