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 |
|---|---|---|---|---|---|---|---|---|
| Östersunds FK | Allsvenskan | 31 | 25 | 0 | 8.4 | 41% | 4.4 | 2.2 |
| Örebro | Allsvenskan | 31 | 25 | 0 | 11.6 | 48% | 4.6 | 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.
| Kalmar | Allsvenskan | 94 | 114 | — | 11.4 | 54% | 4.4 | 1.5 |
| Djurgården | Allsvenskan | 120 | 184 | — | 14.6 | 53% | 5.9 | 1.7 |
| IFK Göteborg | Allsvenskan | 120 | 149 | — | 12.8 | 49% | 5.9 | 1.7 |
| Halmstad | Allsvenskan | 123 | 107 | — | 10.6 | 41% | 4.4 | 1.6 |
| Elfsborg | Allsvenskan | 119 | 207 | — | 14 | 46% | 5.8 | 2.1 |
| GAIS | Allsvenskan | 60 | 81 | — | 15.1 | 48% | 5.8 | 2.2 |
| Hammarby | Allsvenskan | 120 | 203 | — | 15.2 | 56% | 5.8 | 1.6 |
| Åtvidaberg | Allsvenskan | 4 | 0 | — | 8 | 50% | 2.3 | 0 |
| Häcken | Allsvenskan | 120 | 211 | — | 15.3 | 55% | 5.8 | 1.9 |
| Sirius | Allsvenskan | 120 | 190 | — | 14.4 | 53% | 5.1 | 1.6 |
| Degerfors | Allsvenskan | 89 | 97 | — | 12.4 | 46% | 4.7 | 2 |
| AIK | Allsvenskan | 120 | 165 | — | 13.9 | 50% | 5.4 | 1.9 |
| Öster | Allsvenskan | 31 | 29 | — | 10 | 46% | 4.9 | 2.4 |
| Brommapojkarna | Allsvenskan | 90 | 126 | — | 13.2 | 47% | 4.9 | 1.6 |
| Norrköping | Allsvenskan | 121 | 166 | — | 12.5 | 48% | 4.7 | 1.8 |
| Värnamo | Allsvenskan | 90 | 103 | — | 12.7 | 50% | 5.4 | 1.6 |
| Västerås SK | Allsvenskan | 30 | 26 | — | 15.4 | 53% | 6.2 | 1.3 |
| Malmö FF | Allsvenskan | 118 | 229 | — | 17 | 61% | 6.7 | 1.8 |
| Varberg BoIS | Allsvenskan | 60 | 61 | — | 11.9 | 43% | 4.4 | 2.7 |
| Mjällby | Allsvenskan | 123 | 167 | — | 13 | 49% | 5.3 | 1.7 |