Aggregated team performance data including expected goals, shots, possession, corners, and discipline stats.
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.
| Zob Ahan | Persian Gulf Pro League | 149 | 130 | — | 5.4 | 40% | 3.5 | 1.8 |
| Persepolis | Persian Gulf Pro League | 134 | 187 | — | 8.1 | 47% | 5.2 | 1.5 |
| Tractor Sazi | Persian Gulf Pro League | 136 | 172 | — | 7.6 | 44% | 3.8 | 1.9 |
| Sepahan | Persian Gulf Pro League | 146 | 222 | — | 7.1 | 44% | 5.1 | 2 |
| Paykan | Persian Gulf Pro League | 105 | 92 | — | 5.1 | 37% | 3.9 | 1.4 |
| Padideh Khorasan | Persian Gulf Pro League | 59 | 43 | — | 5.1 | 37% | 3.1 | 1.7 |
| Esteghlal | Persian Gulf Pro League | 135 | 158 | — | 7.5 | 45% | 5 | 1.7 |
| Esteghlal Khuzestan | Persian Gulf Pro League | 75 | 54 | — | 6 | 46% | 3.3 | 2.1 |
| Machine Sazi | Persian Gulf Pro League | 30 | 19 | — | 6.8 | 36% | 3.4 | 1.7 |
| Nassaji Mazandaran | Persian Gulf Pro League | 113 | 84 | — | 5.1 | 37% | 3 | 1.7 |
| Mes Rafsanjan | Persian Gulf Pro League | 133 | 116 | — | 6.4 | 42% | 4.2 | 1.7 |
| Fajr Sepasi | Persian Gulf Pro League | 53 | 35 | — | 3 | 27% | 3 | 1.2 |
| Aluminium Arak | Persian Gulf Pro League | 134 | 107 | — | 5.2 | 42% | 3.4 | 1.4 |
| Naft Masjed Soleyman | Persian Gulf Pro League | 59 | 35 | — | 3.4 | 30% | 3 | 1.3 |
| Malavan | Persian Gulf Pro League | 70 | 65 | — | 6 | 49% | 3.2 | 1.6 |
| Kheybar Khorramabad | Persian Gulf Pro League | 51 | 43 | — | 8.5 | 49% | 4.7 | 2.1 |
| Shams Azar Qazvin | Persian Gulf Pro League | 80 | 71 | — | 8.3 | 50% | 4.8 | 2 |
| Havadar | Persian Gulf Pro League | 73 | 56 | — | 4.5 | 41% | 3.5 | 1.7 |
| Saipa | Persian Gulf Pro League | 30 | 19 | — | 6.7 | 46% | 4.7 | 1.8 |
| Chadormalu SC | Persian Gulf Pro League | 50 | 45 | — | 5.6 | 46% | 2.1 | 2.3 |
| Sanat Naft | Persian Gulf Pro League | 80 | 68 | — | 5.1 | 35% | 3.5 | 1.6 |