China · Super League · Sun 3 Aug 2025 12:35 UTC · All tips 3 Aug

Beijing Guoan Beijing Guoan
2 – 2
Full time
Tianjin Jinmen Tiger Tianjin Jinmen Tiger

Beijing Guoan vs Tianjin Jinmen Tiger betting tips

Beijing Guoan vs Tianjin Jinmen Tiger finished 2-2. Moderate historical data available. Strong lean toward Home Over 0.5 based on this side's recent goal averages.

What are the tips for Beijing Guoan vs Tianjin Jinmen Tiger?

Home to Score
Pick: Home Over 0.5 · Prob 85%
VIP Won
Away Over/Under 1.5
Pick: Away Under 1.5 · Prob 77%
VIP Lost
Home Over/Under 1.5
Pick: Home Over 1.5 · Prob 64%
VIP Won
Over/Under 2.5
Pick: Over 2.5 · Odds 1.43 · Prob 59%
VIP Won
1X2
Pick: Final Time 1 · Odds 1.39 · Prob 56%
VIP Lost
Over/Under 3.5
Pick: Over 3.5 · Odds 2.11 · Prob 41%
VIP Won

Match insights: form, goals and model lean

Model lean

Home Over 0.5 85% Other 15%

Head to head (4)

2Beijing Guoan
2Draws
0Tianjin Jinmen Tiger

Recent form

Beijing Guoan
L W W W D W W W D
20 pts / 9
Tianjin Jinmen Tiger
L W W L W W W L W L
18 pts / 10

Goals (avg last matches)

2.2 / 0.9 Beijing Guoan scored / conceded
1.5 / 1.7 Tianjin Jinmen Tiger scored / conceded

Tip analysis

Moderate historical data available. Strong lean toward Home Over 0.5 based on this side's recent goal averages.

Previous meetings and recent results

Who will win Beijing Guoan vs Tianjin Jinmen Tiger?

45 community votes

44% Beijing Guoan
18% Draw
38% Tianjin Jinmen Tiger

Voting closed

Frequently asked questions

Who will win Beijing Guoan vs Tianjin Jinmen Tiger?

Our model currently leans Home Over 0.5 with about 85% confidence. See free and VIP markets below.

What are the odds for Beijing Guoan vs Tianjin Jinmen Tiger?

Odds appear next to unlocked free tips. VIP odds unlock in the app or after full time.

Where can I see H2H and recent form?

Scroll to Insights and H2H on this page for head-to-head results, recent W/D/L form, and goal averages when enough history is available.

Last updated 8 Oct 2026 21:00 UTC · Insights built from Betting Tips historical predictions · Data quality: thin