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 |
|---|---|---|---|---|---|---|---|---|
| Shabab Al Ahli Dubai | AFC Champions League Elite | 11 | 20 | — | 17 | 51% | 4.5 | 1.5 |
| Al Duhail | AFC Champions League Elite | 10 | 19 | — | 13.3 | 48% | 3.7 |
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.
| 2.3 |
| Melbourne City | AFC Champions League Elite | 10 | 10 | — | 9.4 | 51% | 4.6 | 1.6 |
| Al Gharafa | AFC Champions League Elite | 8 | 7 | — | 10.6 | 42% | 3.9 | 2.1 |
| Al Ittihad | AFC Champions League Elite | 10 | 23 | — | 13.1 | 59% | 4.8 | 2.1 |
| Seoul | AFC Champions League Elite | 10 | 11 | — | 10.4 | 54% | 3.5 | 1.7 |
| Johor Darul Ta'zim | AFC Champions League Elite | 11 | 12 | — | 17.8 | 51% | 6.1 | 2.5 |
| Tractor Sazi | AFC Champions League Elite | 9 | 12 | — | 12.1 | 48% | 3.7 | 1.7 |
| Vissel Kobe | AFC Champions League Elite | 12 | 21 | — | 11.6 | 54% | 5.3 | 1.6 |
| Shanghai Port | AFC Champions League Elite | 8 | 2 | — | 7.1 | 44% | 3.4 | 1.4 |
| Ulsan HD | AFC Champions League Elite | 8 | 6 | — | 12.3 | 54% | 5.3 | 1.1 |
| Nasaf | AFC Champions League Elite | 8 | 9 | — | 8.8 | 42% | 2.5 | 2 |
| Buriram United | AFC Champions League Elite | 11 | 13 | — | 8.9 | 46% | 4.8 | 3.5 |
| Al Hilal | AFC Champions League Elite | 10 | 21 | — | 17.1 | 59% | 6.6 | 2.2 |
| Gangwon | AFC Champions League Elite | 10 | 9 | — | 11.2 | 59% | 5.3 | 1.7 |
| Al Sadd | AFC Champions League Elite | 10 | 16 | — | 16.2 | 52% | 5 | 1.6 |
| Machida Zelvia | AFC Champions League Elite | 13 | 18 | — | 11.8 | 44% | 5.4 | 1 |
| Shanghai Shenhua | AFC Champions League Elite | 8 | 5 | — | 10.9 | 38% | 3.1 | 2.3 |
| Al Sharjah | AFC Champions League Elite | 8 | 8 | — | 9.3 | 44% | 3.3 | 2.6 |
| Al Shorta | AFC Champions League Elite | 8 | 6 | — | 14.1 | 55% | 6.3 | 1.3 |
| Al Ahli | AFC Champions League Elite | 14 | 32 | — | 13.6 | 52% | 4.3 | 2.7 |
| Sanfrecce Hiroshima | AFC Champions League Elite | 10 | 12 | — | 14.1 | 55% | 7.6 | 2.1 |
| Al Wahda | AFC Champions League Elite | 9 | 11 | — | 13.6 | 44% | 4.8 | 2.3 |
| Chengdu Rongcheng | AFC Champions League Elite | 9 | 10 | — | 7.9 | 45% | 3.7 | 2.7 |