· Premier League
Fakirerpool
ATT
3/10
DEF
1/10
vs
Rahmatgonj MFS
3/10
ATT
3/10
DEF
Scheduled· Fri 23 Oct · 08:45
Best Pick
BTTS
54%
Correct Score
Top
1-1
12%
8.30
Top
1-0
10.5%
9.55
2-1
9.2%
10.81
2-0
8%
12.44
0-1
7.8%
12.76
1-2
6.9%
14.44
0-0
6.8%
14.67
2-2
5.3%
18.81
146%
2.17TOP
X25%
3.95
228%
3.51
1X71%
X253%
1274%
Probable XI4-4-2
Mia
Topu
Mia
Ganto
Shanto
Sidik
Hossain
Al-Amin
Hossain
Roy
Ouattara
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Fakirerpool12 players
38.8/ 99Developing
Top Rated
1Md Sabbir HossainMID52
2Rubel MiaGK46
3Shiblal TuduMID46
Rahmatgonj MFS16 players
45.8/ 99Average
Top Rated
1Parvej AhmedDEF89
2A. AppauDEF84
3A. HossainMID72
Fakirerpool20
PlayerOverall RatingImpactAggressionDiscipline
Md Sabbir Hossain52416456
Rubel Mia46454550
Shiblal Tudu46454550
Hafijur Rahman Topu45454550
Md Mosharaf Hossain Shanto45454550
Suleiman Sillah42423450
Mohamed Fofana41393950
Yeasin Mia41393950
Md Ganto36343450
Sagor Hossain32282850
Ben Ibrahim Ouattara2523950
Raficul Islam159950
Tamim Ahammed————
Rahmatgonj MFS34
PlayerOverall RatingImpactAggressionDiscipline
Parvej Ahmed89919961
A. Appau84829963
A. Hossain72659956
Mamun Alif7075950
S. Hossain53279967
Abhishek Limbu5174950
Tias Das49642249
Ariful Islam41393950
Md Faizullah41393950
Sakib Bepari36343450
Amit Hasan31282850
Anik Hossain Siam31282850
Alfaj Miah27232350
Player Rankings
Snipers
Top goal threats
1
Suleiman SillahFakirerpool
42
2
Sakib BepariRahmatgonj MFS
34
3
Anik Hossain SiamRahmatgonj MFS
28
4
Ben Ibrahim OuattaraFakirerpool
23
5
Fahim Nur TohaRahmatgonj MFS
23
Wall
Top defenders
1
Parvej AhmedRahmatgonj MFS
91
2
A. AppauRahmatgonj MFS
82
3
Abhishek LimbuRahmatgonj MFS
74
4
Tias DasRahmatgonj MFS
64
5
Hafijur Rahman TopuFakirerpool
45
Control
Top playmakers
1
A. HossainRahmatgonj MFS
65
2
Shiblal TuduFakirerpool
45
3
Md Sabbir HossainFakirerpool
41
4
Md FaizullahRahmatgonj MFS
39
5
Sagor HossainFakirerpool
28
Bad Boys
Most likely to get booked
1
Tias DasRahmatgonj MFS
49
2
Rubel MiaFakirerpool
50
3
Hafijur Rahman TopuFakirerpool
50
4
Md GantoFakirerpool
50
5
Md Mosharaf Hossain ShantoFakirerpool
50
Power Rankings
Strongest overall
1
Parvej AhmedRahmatgonj MFS
89
2
A. AppauRahmatgonj MFS
84
3
A. HossainRahmatgonj MFS
72
4
Mamun AlifRahmatgonj MFS
70
5
S. HossainRahmatgonj MFS
53

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Fakirerpool vs Rahmatgonj MFS 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 Fakirerpool vs Rahmatgonj MFS 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 Fakirerpool 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 Fakirerpool vs Rahmatgonj MFS 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 Fakirerpool 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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