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 | Kings Cup | 8 | 4 | — | 8 | 51% | 4.4 | 3.1 |
| Al Ittihad | Kings Cup | 20 | 40 | — | 15.2 | 55% | 6.3 | 1.8 |
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 | Kings Cup | 15 | 29 | — | 12.3 | 52% | 3.4 | 2.8 |
| Al Nassr | Kings Cup | 17 | 34 | — | 17.8 | 61% | 5.8 | 2.1 |
| Al-Wehda | Kings Cup | 10 | 9 | — | 11.5 | 45% | 5.5 | 2.8 |
| Abha | Kings Cup | 9 | 13 | — | 15.4 | 44% | 5 | 3.2 |
| Al Fateh | Kings Cup | 12 | 21 | — | 14.5 | 51% | 6.7 | 2.4 |
| Al Faisaly | Kings Cup | 10 | 13 | — | 9 | 38% | 3.7 | 2.3 |
| Al Hilal | Kings Cup | 20 | 38 | — | 15.4 | 63% | 6.9 | 1.9 |
| Al Ahli | Kings Cup | 9 | 19 | — | 15.9 | 58% | 6.4 | 1.6 |
| Al Ettifaq | Kings Cup | 8 | 9 | — | 11 | 44% | 5.3 | 2.3 |
| Al Batin | Kings Cup | 10 | 11 | — | 9 | 39% | 4.4 | 2.5 |
| Al Tai | Kings Cup | 6 | 10 | — | 11.7 | 46% | 4.5 | 2.3 |
| Al Adalah | Kings Cup | 4 | 0 | — | 6.5 | 35% | 1.8 | 1.8 |
| Al Khaleej | Kings Cup | 9 | 18 | — | 12 | 50% | 4.1 | 1.9 |
| Al Najma | Kings Cup | 5 | 6 | — | 9.2 | 49% | 2.6 | 3.6 |
| Al-Qadsiah | Kings Cup | 11 | 22 | — | 14.5 | 52% | 5.1 | 1.7 |
| Al Shabab | Kings Cup | 15 | 28 | — | 15.3 | 51% | 5.4 | 2.5 |
| Al-Orobah FC | Kings Cup | 3 | 2 | — | 7.7 | 47% | 2.7 | 2.7 |
| Al Hazm | Kings Cup | 6 | 7 | — | 8.8 | 42% | 2.2 | 2 |
| Al Riyadh | Kings Cup | 5 | 5 | — | 8 | 48% | 3 | 2.2 |
| Damac FC | Kings Cup | 7 | 5 | — | 11 | 52% | 6 | 2.7 |
| Al Jabalain | Kings Cup | 5 | 7 | — | 8.8 | 36% | 2.4 | 1.8 |
| Al-Fayha | Kings Cup | 11 | 20 | — | 13.7 | 46% | 5 | 2.5 |
| Jeddah | Kings Cup | 3 | 0 | — | 5.3 | 38% | 2.7 | 0 |
| Al-Ain | Kings Cup | 4 | 4 | — | 11.5 | 45% | 5 | 2.8 |
| Al Akhdoud | Kings Cup | 4 | 3 | — | 10 | 48% | 4.8 | 2.8 |
| Al Kholood | Kings Cup | 5 | 9 | — | 12 | 42% | 6.6 | 2 |
| Al Arabi Saudi | Kings Cup | 3 | 3 | — | 7 | 36% | 3 | 2.7 |