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 |
|---|---|---|---|---|---|---|---|---|
| Raja Casablanca | Botola Pro | 28 | 40 | — | 10.1 | 58% | 4.2 | 2.7 |
| Wydad Casablanca | Botola Pro | 27 | 41 | — | 10.9 | 51% | 4.6 | 2.5 |
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.
| FUS Rabat | Botola Pro | 28 | 38 | — | 9.8 | 49% | 3.5 | 2.5 |
| Kawkab Marrakech | Botola Pro | 25 | 23 | — | 10.6 | 44% | 3.3 | 2.6 |
| Moghreb Tétouan | Botola Pro | 3 | 4 | — | 10.7 | 55% | 4.7 | 2.3 |
| RSB Berkane | Botola Pro | 29 | 43 | — | 10.2 | 55% | 3.9 | 1.7 |
| Difaâ El Jadida | Botola Pro | 29 | 28 | — | 8 | 45% | 3.4 | 2.6 |
| UTS Rabat | Botola Pro | 28 | 26 | — | 9.3 | 47% | 3.9 | 3.8 |
| Olympic Safi | Botola Pro | 28 | 27 | — | 9.3 | 50% | 3.9 | 2.6 |
| Hassania Agadir | Botola Pro | 28 | 27 | — | 8 | 45% | 4.5 | 2.4 |
| FAR Rabat | Botola Pro | 28 | 43 | — | 11 | 58% | 5.2 | 2.1 |
| Ittihad Tanger | Botola Pro | 28 | 28 | — | 10.5 | 51% | 4 | 2.5 |
| Maghreb Fès | Botola Pro | 28 | 39 | — | 9.1 | 53% | 3.3 | 2.5 |
| Olympique Dcheïra | Botola Pro | 25 | 21 | — | 9.1 | 44% | 3.3 | 2.4 |
| CR Khemis Zemamra | Botola Pro | 28 | 28 | — | 9.9 | 49% | 4.3 | 2.5 |
| Chabab Mohammédia | Botola Pro | 3 | 1 | — | 13.7 | 36% | 3.3 | 2 |
| CODM Meknès | Botola Pro | 28 | 17 | — | 8.5 | 47% | 3.3 | 2.6 |
| Yacoub El Mansour | Botola Pro | 25 | 27 | — | 9.8 | 54% | 4.2 | 2.6 |