Aggregated team performance data including expected goals, shots, possession, corners, and discipline stats.
| Team | League | MP | Goals | Avg xG | Shots/G | Poss% | Corners/G | Cards/G |
|---|---|---|---|---|---|---|---|---|
| Hoàng Anh Gia Lai | V-League | 32 | 35 | — | 9 | 43% | 3.4 | 2 |
| Becamex Hồ Chí Minh U19 | V-League | 32 | 38 | — | 9.9 | 48% | 3.8 | 1.4 |
Expected Goals (xG) is the single most important advanced metric in modern football analytics. It measures the quality of scoring chances by assigning each shot a probability of being scored, based on factors like distance, angle, assist type, and whether it was a header. A penalty is worth about 0.76 xG, while a long-range shot might be just 0.03.
The Avg xG column shows how many goals a team is expected to score per match based on their shot quality. Teams with high xG but lower actual goals are likely due a positive regression — they are creating chances but not converting them yet. The reverse (low xG, high goals) suggests a team overperforming that may regress. Read our detailed xG explainer for a deeper dive.
Shots per game indicates offensive intent — teams averaging 15+ shots are creating volume. But shots alone do not tell you about quality, which is why xG is the more reliable predictor. A team with 10 shots and 1.8 xG is more dangerous than one with 16 shots and 1.2 xG.
Possession (highlighted green above 55%) reflects control but not necessarily dominance. Some of Europe's best counter-attacking teams thrive on low possession. For betting, possession is most useful in predicting corner counts (high-possession teams win more) and tempo (high-possession games tend to be lower scoring).
The Cards/G column tracks total cards per match per team. This is useful for the cards/bookings market, which some bookmakers offer as over/under on total cards or total booking points. Teams with aggressive pressing styles or those frequently defending deep tend to commit more fouls and receive more cards. Cross-reference with form — teams on losing streaks often accumulate more cards from frustrated tackles. Check our corner data alongside cards, as set-piece-heavy teams with high card counts create chaotic matches ideal for Over bets.
| Hà Nội | V-League | 32 | 62 | — | 14.3 | 52% | 5.8 | 1.9 |
| Quảng Nam | V-League | 6 | 5 | — | 11.3 | 52% | 5.7 | 1.3 |
| Đông Á Thanh Hoá | V-League | 32 | 31 | — | 9.6 | 47% | 2.8 | 1.9 |
| Hồng Lĩnh Hà Tĩnh | V-League | 32 | 20 | — | 9.9 | 49% | 4.2 | 2.1 |
| Công an TP.Hồ Chí Minh | V-League | 32 | 31 | — | 9.9 | 49% | 4.2 | 1.8 |
| SHB Đà Nẵng | V-League | 32 | 40 | — | 9.2 | 47% | 3.1 | 2.1 |
| Hải Phòng | V-League | 32 | 44 | — | 13 | 54% | 5.2 | 1.3 |
| Sông Lam Nghệ An | V-League | 32 | 34 | — | 9.3 | 45% | 3.4 | 1.9 |
| Quy Nhơn United | V-League | 7 | 9 | — | 9.1 | 43% | 2.9 | 1.6 |
| Thép Xanh Nam Định | V-League | 32 | 48 | — | 12.5 | 57% | 4.7 | 1.4 |
| Thể Công-Viettel | V-League | 32 | 54 | — | 13.8 | 53% | 5.1 | 1.9 |
| PVF-CAND | V-League | 26 | 22 | — | 9.2 | 46% | 3.7 | 1.8 |
| Ninh Bình | V-League | 26 | 53 | — | 11.5 | 51% | 4.8 | 1.7 |
| Công an Hà Nội | V-League | 33 | 73 | — | 15 | 59% | 4.6 | 1.8 |