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 |
|---|---|---|---|---|---|---|---|---|
| Hebei CFFC | Super League | 22 | 15 | — | 9.5 | 49% | 4 | 2 |
| Wuhan Yangtze | Super League | 22 | 23 | — | 9 | 49% | 4 | 1.6 |
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.
| Cangzhou | Super League | 82 | 87 | — | 10.2 | 42% | 3.8 | 2 |
| Shandong Taishan | Super League | 128 | 251 | — | 15 | 52% | 5.7 | 2 |
| Tianjin Jinmen Tiger | Super League | 114 | 142 | — | 11.6 | 49% | 4.4 | 1.9 |
| Changchun Yatai | Super League | 112 | 146 | — | 10.9 | 47% | 3.8 | 1.9 |
| Beijing Guoan | Super League | 130 | 233 | — | 13.8 | 57% | 5.5 | 2 |
| Shenzhen | Super League | 52 | 55 | — | 10.7 | 49% | 4.8 | 2 |
| Zhejiang | Super League | 98 | 182 | — | 14.9 | 55% | 5.1 | 2 |
| Shanghai Port | Super League | 117 | 281 | — | 16.1 | 57% | 6.2 | 1.7 |
| Guangzhou Evergrande | Super League | 22 | 47 | — | 14.5 | 59% | 4.9 | 2.1 |
| Dalian Pro | Super League | 52 | 46 | — | 10.9 | 46% | 4.5 | 2.1 |
| Chongqing Dangdai Lifan | Super League | 22 | 21 | — | 9.1 | 43% | 3.3 | 1.6 |
| Henan Songshan Longmen | Super League | 117 | 151 | — | 11.8 | 48% | 5.1 | 2.2 |
| Shanghai Shenhua | Super League | 120 | 220 | — | 15.8 | 53% | 6.2 | 1.7 |
| Guangzhou R&F | Super League | 22 | 32 | — | 9.3 | 50% | 3.6 | 1.8 |
| Meizhou Hakka | Super League | 90 | 105 | — | 11.4 | 46% | 5.2 | 2.3 |
| Qingdao Hainiu | Super League | 92 | 98 | — | 12.1 | 45% | 4 | 2.2 |
| Shenzhen Peng City | Super League | 62 | 62 | — | 11.2 | 46% | 4.2 | 2.3 |
| Qingdao FC | Super League | 22 | 13 | — | 8.5 | 40% | 3.2 | 2 |
| Chengdu Rongcheng | Super League | 92 | 182 | — | 16.1 | 54% | 6.1 | 2.1 |
| Nantong Zhiyun | Super League | 60 | 58 | — | 12.4 | 49% | 5 | 2.3 |
| Wuhan Three Towns | Super League | 94 | 127 | — | 13.9 | 51% | 4.5 | 1.9 |
| Qingdao West Coast | Super League | 62 | 77 | — | 10.7 | 47% | 4.3 | 2.3 |
| Dalian Yingbo | Super League | 32 | 34 | — | 12.5 | 47% | 5.7 | 2 |
| Yunnan Yukun | Super League | 32 | 51 | — | 13.3 | 44% | 4.6 | 1.8 |