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 |
|---|---|---|---|---|---|---|---|---|
| Horsens | First Division | 36 | 46 | — | 11.8 | 47% | 4.5 | 2.4 |
| Aalborg BK | First Division | 32 | 50 | — | 13.1 | 53% | 5.2 | 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.
| B 93 | First Division | 36 | 49 | — | 13.5 | 55% | 4.9 | 1.6 |
| Esbjerg | First Division | 36 | 50 | — | 13.7 | 47% | 5.7 | 1.5 |
| Hobro | First Division | 36 | 43 | — | 11.7 | 47% | 4.4 | 1.6 |
| Odense BK | First Division | 4 | 8 | — | 15.5 | 54% | 6.8 | 1.5 |
| Lyngby Boldklub | First Division | 32 | 69 | — | 14 | 54% | 5.9 | 1.9 |
| Vendsyssel | First Division | 4 | 5 | — | 13 | 51% | 4.5 | 2.5 |
| Fredericia | First Division | 4 | 11 | — | 13.5 | 48% | 4.5 | 0.8 |
| Roskilde | First Division | 4 | 10 | — | 13.5 | 50% | 4 | 0.3 |
| HB Køge | First Division | 36 | 50 | — | 12.5 | 44% | 3.8 | 1.8 |
| Kolding IF | First Division | 36 | 41 | — | 13 | 52% | 5.3 | 1.8 |
| Hvidovre | First Division | 36 | 45 | — | 12.3 | 49% | 5 | 1.7 |
| Aarhus Fremad | First Division | 32 | 52 | — | 14.4 | 56% | 6 | 1.3 |
| Middelfart | First Division | 32 | 32 | — | 9.8 | 47% | 4.1 | 1.6 |
| Hillerød | First Division | 36 | 52 | — | 11.4 | 50% | 4.5 | 1.3 |