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 | 131 | 190 | — | 11 | 56% | 5.4 | 2.1 |
| Wydad Casablanca | Botola Pro | 141 | 216 | — | 10.5 | 53% | 5.2 | 2.3 |
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 | 127 | 156 | — | 9.8 | 50% | 4.4 | 2.2 |
| Kawkab Marrakech | Botola Pro | 26 | 20 | — | 9.6 | 45% | 4.1 | 2.1 |
| OC Khouribga | Botola Pro | 27 | 29 | — | 6.9 | 45% | 3.6 | 2 |
| Moghreb Tétouan | Botola Pro | 90 | 88 | — | 8.6 | 49% | 3.3 | 2.2 |
| RSB Berkane | Botola Pro | 148 | 191 | — | 10.2 | 53% | 5.1 | 2.1 |
| Mouloudia Oujda | Botola Pro | 87 | 89 | — | 8 | 46% | 3.8 | 2.5 |
| Difaâ El Jadida | Botola Pro | 111 | 109 | — | 8.7 | 46% | 4 | 2.4 |
| UTS Rabat | Botola Pro | 77 | 80 | — | 9.2 | 47% | 4.2 | 2.6 |
| Olympic Safi | Botola Pro | 135 | 144 | — | 8.4 | 46% | 4.3 | 2.6 |
| Hassania Agadir | Botola Pro | 145 | 145 | — | 8 | 46% | 4.1 | 2.3 |
| FAR Rabat | Botola Pro | 144 | 222 | — | 10.6 | 56% | 5.3 | 1.9 |
| Ittihad Tanger | Botola Pro | 125 | 126 | — | 9.8 | 50% | 4.3 | 2.6 |
| Maghreb Fès | Botola Pro | 126 | 136 | — | 8.9 | 50% | 4.1 | 2.3 |
| Youssoufia Berrechid | Botola Pro | 89 | 68 | — | 8.6 | 43% | 3.8 | 2.1 |
| Olympique Dcheïra | Botola Pro | 14 | 11 | — | 9.8 | 45% | 3.6 | 2.4 |
| Rapide Oued Zem | Botola Pro | 60 | 47 | — | 7.1 | 40% | 3.5 | 2.4 |
| CR Khemis Zemamra | Botola Pro | 106 | 111 | — | 9.3 | 47% | 3.8 | 2.2 |
| JS Soualem | Botola Pro | 82 | 74 | — | 7 | 46% | 3.7 | 2.3 |
| Chabab Mohammédia | Botola Pro | 116 | 81 | — | 7.4 | 47% | 3.9 | 1.8 |
| CODM Meknès | Botola Pro | 45 | 38 | — | 9.5 | 46% | 3 | 2.4 |
| Yacoub El Mansour | Botola Pro | 14 | 12 | — | 11.5 | 54% | 4.6 | 2.7 |