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 |
|---|---|---|---|---|---|---|---|---|
| Al Shamal SC | Stars League | 88 | 135 | 0 | 11.8 | 46% | 4.3 | 2.1 |
| Al Sailiya | Stars League | 65 | 58 | 0 | 9.5 | 44% | 4.5 | 2.2 |
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.
| Al Gharafa | Stars League | 114 | 219 | — | 13.3 | 52% | 5.1 | 2.2 |
| Al Kharitiyath | Stars League | 20 | 19 | — | 7.9 | 47% | 3.9 | 2.1 |
| Al Wakrah | Stars League | 107 | 146 | — | 12.5 | 53% | 5.4 | 2.2 |
| Al-Ahli Doha | Stars League | 116 | 171 | — | 9.8 | 50% | 4.4 | 2 |
| Al Mu'aidar | Stars League | 22 | 34 | — | 13.7 | 41% | 4 | 2.1 |
| Al Khor | Stars League | 65 | 61 | — | 8.6 | 43% | 4.4 | 1.7 |
| Al-Arabi SC | Stars League | 125 | 192 | — | 11.7 | 52% | 5 | 2.3 |
| Al Sadd | Stars League | 124 | 384 | — | 16.1 | 59% | 6.2 | 1.8 |
| Al Shahaniya | Stars League | 46 | 53 | — | 10.8 | 47% | 3.7 | 2.3 |
| Umm-Salal SC | Stars League | 116 | 147 | — | 10.1 | 41% | 4.3 | 2.2 |
| Al Rayyan | Stars League | 111 | 202 | — | 13 | 54% | 5.2 | 2.2 |
| Qatar SC | Stars League | 111 | 155 | — | 11.6 | 47% | 4.4 | 2.3 |
| Al Markhiya | Stars League | 22 | 20 | — | 11.5 | 43% | 4.8 | 2.3 |
| Al Duhail | Stars League | 110 | 240 | — | 13.3 | 58% | 5.9 | 2 |