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 Raed | Pro League | 128 | 161 | — | 10.3 | 46% | 4.1 | 2.3 |
| Al Ittihad | Pro League | 164 | 298 | — | 13 | 55% | 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 Taawoun | Pro League | 165 | 238 | — | 12.4 | 52% | 4.6 | 2.3 |
| Al Nassr | Pro League | 160 | 367 | — | 16.6 | 59% | 6.1 | 2.1 |
| Al-Wehda | Pro League | 98 | 125 | — | 11.2 | 46% | 4 | 2.2 |
| Abha | Pro League | 94 | 107 | — | 10.7 | 44% | 4 | 2.1 |
| Al Fateh | Pro League | 176 | 257 | — | 12.4 | 48% | 5.2 | 2.3 |
| Al Faisaly | Pro League | 60 | 70 | — | 11.4 | 50% | 4.3 | 2.4 |
| Al Hilal | Pro League | 165 | 409 | — | 16.6 | 63% | 7 | 1.8 |
| Al Ahli | Pro League | 166 | 293 | — | 14 | 52% | 5.2 | 2.3 |
| Al Ettifaq | Pro League | 172 | 237 | — | 12.1 | 51% | 4.5 | 1.9 |
| Al Batin | Pro League | 60 | 74 | — | 10.2 | 45% | 3.7 | 2.2 |
| Al Tai | Pro League | 64 | 67 | — | 10.3 | 48% | 4.6 | 2.3 |
| Al Khaleej | Pro League | 100 | 124 | — | 11.1 | 48% | 4.3 | 2.2 |
| Al Najma | Pro League | 26 | 23 | — | 9.3 | 47% | 3.8 | 2 |
| Al-Qadsiah | Pro League | 92 | 156 | — | 13.7 | 53% | 5.6 | 2.1 |
| Al Shabab | Pro League | 163 | 277 | — | 13.9 | 54% | 5 | 2.3 |
| Al-Orobah FC | Pro League | 34 | 33 | — | 10.1 | 40% | 3.4 | 2.4 |
| Al Hazm | Pro League | 90 | 86 | — | 11.4 | 46% | 4.3 | 2.3 |
| Al Riyadh | Pro League | 92 | 93 | — | 9.9 | 43% | 4.2 | 2.1 |
| Damac FC | Pro League | 154 | 186 | — | 11 | 46% | 4.2 | 2.3 |
| Al-Fayha | Pro League | 131 | 130 | — | 10.2 | 45% | 4.1 | 2 |
| NEOM SC | Pro League | 26 | 31 | — | 12.1 | 52% | 4.4 | 2.1 |
| Al-Ain | Pro League | 30 | 34 | — | 12 | 52% | 4.2 | 2.1 |
| Al Akhdoud | Pro League | 94 | 88 | — | 11.1 | 43% | 4.7 | 2.5 |
| Al Kholood | Pro League | 60 | 76 | — | 11.9 | 44% | 4.6 | 1.8 |