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 | 119 | 143 | — | 8.3 | 49% | 4 | 2 |
| Quảng Ninh | V-League | 12 | 12 | — | 6.3 | 51% | 3.3 | 1.7 |
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.
| Sai Gon | V-League | 11 | 6 | — | 4.7 | 42% | 2.8 | 2.1 |
| Becamex Hồ Chí Minh U19 | V-League | 108 | 128 | — | 8.9 | 50% | 4 | 1.6 |
| Hà Nội | V-League | 108 | 184 | — | 11 | 52% | 5.3 | 1.6 |
| Quảng Nam | V-League | 50 | 59 | — | 8.7 | 49% | 4.2 | 1.6 |
| Sanna Khanh Hoa | V-League | 44 | 37 | — | 8.7 | 49% | 3.8 | 1.5 |
| Đông Á Thanh Hoá | V-League | 111 | 144 | — | 10.7 | 49% | 4.5 | 1.8 |
| Hồng Lĩnh Hà Tĩnh | V-League | 105 | 109 | — | 8.8 | 47% | 4.2 | 1.9 |
| Công an TP.Hồ Chí Minh | V-League | 106 | 115 | — | 8.4 | 48% | 4.1 | 2.1 |
| SHB Đà Nẵng | V-League | 83 | 79 | — | 8.3 | 47% | 3.9 | 2 |
| Hải Phòng | V-League | 118 | 147 | — | 10.2 | 51% | 5 | 1.4 |
| Sông Lam Nghệ An | V-League | 121 | 117 | — | 9.1 | 45% | 4.1 | 1.7 |
| Quy Nhơn United | V-League | 82 | 99 | — | 9.3 | 46% | 3.9 | 1.6 |
| Thép Xanh Nam Định | V-League | 107 | 178 | — | 10 | 51% | 4.3 | 1.6 |
| Thể Công-Viettel | V-League | 109 | 147 | — | 11.1 | 50% | 5.2 | 2 |
| PVF-CAND | V-League | 16 | 11 | — | 9 | 48% | 3.7 | 1.8 |
| Ninh Bình | V-League | 16 | 33 | — | 11.7 | 50% | 4.3 | 1.7 |
| Công an Hà Nội | V-League | 86 | 156 | — | 12.2 | 56% | 5 | 1.7 |