· AFC Champions League
Buriram United
ATT
5/10
DEF
5/10
vs
Beijing Guoan
5/10
ATT
5/10
DEF
Scheduled· Tue 13 Oct · 12:15
Best Pick
BTTS
53%
Correct Score
Top
1-1
11.8%
8.45
Top
1-0
10.8%
9.29
2-1
9.5%
10.57
2-0
8.6%
11.62
0-1
7.4%
13.51
0-0
6.7%
14.86
1-2
6.5%
15.36
2-2
5.2%
19.21
149%
2.04TOP
X25%
4.01
226%
3.82
1X74%
X251%
1275%
Probable XI4-4-2
Bootprom
Caju
Good
Mancha
Praisuwan
Darlan
Buranajutanon
Causic
Dougall
Tudorie
Yodsangwal
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Buriram United22 players
48.9/ 99Average
Top Rated
1Phitiwat SookjitthammakulMID75
2Pansa HemviboonDEF59
3Supachai Chaided FWD59
Beijing Guoan16 players
50.6/ 99Average
Top Rated
1Li LeiDEF64
2Chadrac AkoloFWD60
3Gui RamosDEF57
Buriram United40
PlayerOverall RatingImpactAggressionDiscipline
Phitiwat Sookjitthammakul75689667
Pansa Hemviboon59495567
Supachai Chaided 59496472
Nathan Pelae57525261
Vukan Savicevic56485661
Neil Etheridge55564950
Eduardo Mancha55594156
Curtis Good 54583858
Uros Radakovic54525156
Darlan54525256
Stefan Schimmer53564550
Paulinho50504950
Theerathon Bunmathan48494550
Beijing Guoan39
PlayerOverall RatingImpactAggressionDiscipline
Li Lei64457967
Chadrac Akolo60654550
Gui Ramos57565556
Tze-Nam Yue 57465567
Béni Nkololo54515261
Jiefu Deng51465356
Dawhan51494756
Serginho51525150
Hou Sen50504550
Abduhamit Abdugheni48455250
J. Dudziak48475050
Yang Liyu48454956
Zhang Xizhe47484550
Player Rankings
Snipers
Top goal threats
1
Chadrac AkoloBeijing Guoan
65
2
BissoliBuriram United
59
3
Stefan SchimmerBuriram United
56
4
Béni NkololoBeijing Guoan
51
5
Supachai Chaided Buriram United
49
Wall
Top defenders
1
Eduardo ManchaBuriram United
59
2
Curtis Good Buriram United
58
3
Gui RamosBeijing Guoan
56
4
Nathan PelaeBuriram United
52
5
Uros RadakovicBuriram United
52
Control
Top playmakers
1
Phitiwat SookjitthammakulBuriram United
68
2
Peter ZuljBuriram United
63
3
DarlanBuriram United
52
4
SerginhoBeijing Guoan
52
5
PaulinhoBuriram United
50
Bad Boys
Most likely to get booked
1
Goran Causic Buriram United
1
2
Peter ZuljBuriram United
1
3
BissoliBuriram United
1
4
Rubén SánchezBuriram United
1
5
Kenny DougallBuriram United
23
Power Rankings
Strongest overall
1
Phitiwat SookjitthammakulBuriram United
75
2
Li LeiBeijing Guoan
64
3
Chadrac AkoloBeijing Guoan
60
4
Pansa HemviboonBuriram United
59
5
Supachai Chaided Buriram United
59

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Buriram United vs Beijing Guoan 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 Buriram United vs Beijing Guoan 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 Buriram 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 Buriram United vs Beijing Guoan 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 Buriram 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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