· Thai League 1
Chonburi FC
ATT
5/10
DEF
5/10
vs
BG Pathum United FC
5/10
ATT
8/10
DEF
Scheduled· Mon 2 Nov · 12:00
Best Pick
BTTS
53%
Correct Score
Top
1-1
12.4%
8.04
Top
0-1
9.9%
10.09
1-0
9%
11.08
1-2
8.6%
11.68
2-1
7.8%
12.82
0-0
7.2%
13.90
0-2
6.8%
14.66
2-0
5.7%
17.65
134%
2.93
X26%
3.82
240%
2.51TOP
1X60%
X266%
1274%
Probable XI4-4-2
Mendoza
Lemdee
Rueangthanarot
Sanmahung
Sattham
Ritter
Promsrikaew
Veerachart
Tapsuvanavon
Kraisorn
Murillo
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Chonburi FC30 players
48.5/ 99Average
Top Rated
1R. DarkoMID84
2Oege van LingenFWD74
3Chaowat VeerachartMID69
BG Pathum United FC36 players
51.6/ 99Average
Top Rated
1Pongrawit JantawongMID76
2N. JamesDEF70
3Tomoyuki DoiFWD70
Chonburi FC46
PlayerOverall RatingImpactAggressionDiscipline
R. Darko84829669
Oege van Lingen74821683
Chaowat Veerachart69676960
Chatmongkol Rueangthanarot68607956
Nattanan Biesamrit63539949
Wellington Priori63527362
Jakkrapong Sanmahung59584267
Rachata Moraksa56706832
Sittichok Paso54623950
Santipharp Channgom53323891
Jorge Fellipe52644640
Nattapong Sayriya51593355
Yotsakorn Burapha51477447
BG Pathum United FC51
PlayerOverall RatingImpactAggressionDiscipline
Pongrawit Jantawong76596091
N. James70526778
Tomoyuki Doi7099241
Chonnapat Buaphan69766053
Ilhan Fandi68734561
Chanathip Songkrasin66683578
Patrik Gustavsson60457191
Edin Osmanovič59516359
Elias Dolah59486262
Waris Choolthong59325289
Kittapak Seangsawat57515561
Chanon Tamma55277470
Pawee Tanthatemee55625742
Player Rankings
Snipers
Top goal threats
1
Tomoyuki DoiBG Pathum United FC
99
2
Oege van LingenChonburi FC
82
3
Ilhan FandiBG Pathum United FC
73
4
João MagnoBG Pathum United FC
69
5
RanielBG Pathum United FC
56
Wall
Top defenders
1
Chonnapat BuaphanBG Pathum United FC
76
2
Jorge FellipeChonburi FC
64
3
Pawee TanthatemeeBG Pathum United FC
62
4
Chatmongkol RueangthanarotChonburi FC
60
5
Kike LinaresChonburi FC
59
Control
Top playmakers
1
R. DarkoChonburi FC
82
2
Kritsada KamanBG Pathum United FC
79
3
Sarach YooyenBG Pathum United FC
78
4
Rachata MoraksaChonburi FC
70
5
Chanathip SongkrasinBG Pathum United FC
68
Bad Boys
Most likely to get booked
1
Kike LinaresChonburi FC
1
2
Channarong PromsrikaewChonburi FC
1
3
Saharat SontisawatChonburi FC
1
4
Tomoyuki DoiBG Pathum United FC
1
5
Stênio JuniorChonburi FC
4
Power Rankings
Strongest overall
1
R. DarkoChonburi FC
84
2
Pongrawit JantawongBG Pathum United FC
76
3
Oege van LingenChonburi FC
74
4
N. JamesBG Pathum United FC
70
5
Tomoyuki DoiBG Pathum United FC
70

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Chonburi FC vs BG Pathum United 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 Chonburi FC vs BG Pathum United 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 Chonburi 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 Chonburi FC vs BG Pathum United 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 Chonburi 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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