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 | UAE Pro League | 30 | 66 | — | 15.5 | 58% | 5 | 2.1 |
| Kalba | UAE Pro League | 30 | 38 | — | 12.3 | 45% | 4.2 | 2.1 |
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.
| Bani Yas | UAE Pro League | 31 | 35 | — | 9.6 | 44% | 4.2 | 2.5 |
| Al Jazira | UAE Pro League | 30 | 52 | — | 11.8 | 54% | 4.2 | 2.8 |
| Al Wasl | UAE Pro League | 30 | 47 | — | 15 | 60% | 6.8 | 1.7 |
| Al Ain | UAE Pro League | 30 | 68 | — | 15.3 | 55% | 5.6 | 1.7 |
| Dibba Al Fujairah | UAE Pro League | 26 | 24 | — | 9.8 | 37% | 3.1 | 2.4 |
| Al Dhafra | UAE Pro League | 26 | 28 | — | 9.6 | 46% | 3.8 | 1.7 |
| Al Sharjah | UAE Pro League | 31 | 40 | — | 12.7 | 54% | 4.9 | 2.5 |
| Ajman | UAE Pro League | 30 | 35 | — | 12.1 | 48% | 4.3 | 2.4 |
| Dibba Al Hisn | UAE Pro League | 4 | 4 | — | 11.8 | 46% | 3.5 | 2.5 |
| Al Nasr | UAE Pro League | 30 | 43 | — | 11.7 | 54% | 4.6 | 2.2 |
| Al Wahda | UAE Pro League | 30 | 47 | — | 13.5 | 54% | 4.4 | 2 |
| Al Urooba | UAE Pro League | 4 | 7 | — | 13 | 47% | 4.3 | 1 |
| Khorfakkan Club | UAE Pro League | 30 | 44 | — | 9.7 | 44% | 3.3 | 2.5 |
| Al Bataeh | UAE Pro League | 30 | 29 | — | 11.1 | 46% | 4.3 | 2.3 |