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 |
|---|---|---|---|---|---|---|---|---|
| Zob Ahan | Persian Gulf Pro League | 24 | 20 | — | 9.8 | 48% | 3.2 | 2.3 |
| Persepolis | Persian Gulf Pro League | 25 | 29 | — | 7.4 | 54% | 5.4 | 1.7 |
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.
| Tractor Sazi | Persian Gulf Pro League | 25 | 36 | — | 10.2 | 57% | 3.7 | 1.9 |
| Sepahan | Persian Gulf Pro League | 25 | 29 | — | 10 | 52% | 5.6 | 2.4 |
| Paykan | Persian Gulf Pro League | 23 | 15 | — | 8.7 | 51% | 5.2 | 2 |
| Esteghlal | Persian Gulf Pro League | 25 | 32 | — | 9.6 | 49% | 6.9 | 1.8 |
| Foolad | Persian Gulf Pro League | 24 | 25 | — | 8.1 | 52% | 4 | 1.8 |
| Esteghlal Khuzestan | Persian Gulf Pro League | 24 | 18 | — | 6.5 | 46% | 2.3 | 2.3 |
| Nassaji Mazandaran | Persian Gulf Pro League | 3 | 3 | — | 3 | 39% | 2 | 1.3 |
| Gol Gohar | Persian Gulf Pro League | 26 | 28 | — | 7.2 | 55% | 4.3 | 1.9 |
| Mes Rafsanjan | Persian Gulf Pro League | 25 | 12 | — | 7 | 49% | 2.7 | 2.1 |
| Fajr Sepasi | Persian Gulf Pro League | 23 | 25 | — | 5.6 | 49% | 2.2 | 1.8 |
| Aluminium Arak | Persian Gulf Pro League | 25 | 18 | — | 5.4 | 51% | 3.8 | 1.6 |
| Malavan | Persian Gulf Pro League | 25 | 18 | — | 5.8 | 48% | 2.9 | 1.7 |
| Kheybar Khorramabad | Persian Gulf Pro League | 26 | 23 | — | 8.8 | 50% | 4.7 | 2.2 |
| Shams Azar Qazvin | Persian Gulf Pro League | 25 | 16 | — | 7.7 | 48% | 3.3 | 2.2 |
| Havadar | Persian Gulf Pro League | 3 | 1 | — | 3.5 | 49% | 1.5 | 1.7 |
| Chadormalu SC | Persian Gulf Pro League | 24 | 25 | — | 5.7 | 46% | 2.1 | 3.1 |