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 |
|---|---|---|---|---|---|---|---|---|
| Fremad Amager | First Division | 65 | 90 | 0 | 7.6 | 49% | 4.2 | 1.9 |
| Fredericia | First Division | 128 | 205 | 0 | 12.6 | 52% | 5.6 | 1.5 |
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.
| Nykøbing FC | First Division | 32 | 50 | 0 | 7.7 | 52% | 4.4 | 1.5 |
| Skive | First Division | 32 | 28 | — | 10.1 | 45% | 4.8 | 1.6 |
| Aalborg BK | First Division | 53 | 98 | — | 13.1 | 52% | 5.9 | 2.1 |
| B 93 | First Division | 85 | 113 | — | 11.5 | 52% | 4.1 | 1.7 |
| Esbjerg | First Division | 120 | 162 | — | 11.5 | 48% | 5 | 1.9 |
| Hobro | First Division | 148 | 187 | — | 11 | 48% | 5.2 | 1.5 |
| Odense BK | First Division | 32 | 69 | — | 15.7 | 54% | 6.5 | 1.4 |
| Næstved | First Division | 32 | 33 | — | 10.6 | 47% | 4.5 | 2.5 |
| Viborg FF | First Division | 32 | 71 | — | 16.1 | 54% | 5.6 | 2.2 |
| Lyngby Boldklub | First Division | 54 | 109 | — | 11.5 | 53% | 5.4 | 1.9 |
| Vendsyssel | First Division | 128 | 152 | — | 9.7 | 49% | 4.6 | 1.9 |
| Roskilde | First Division | 32 | 37 | — | 9.8 | 49% | 4.2 | 1.3 |
| HB Køge | First Division | 152 | 179 | — | 10 | 46% | 3.9 | 2.1 |
| Kolding IF | First Division | 120 | 149 | — | 11.5 | 48% | 5.1 | 2.2 |
| FC Helsingør | First Division | 95 | 145 | — | 12 | 51% | 4.6 | 1.8 |
| Hvidovre | First Division | 120 | 156 | — | 10.7 | 52% | 5.1 | 1.7 |
| Jammerbugt | First Division | 32 | 32 | — | 6.4 | 47% | 4.1 | 2.3 |
| Aarhus Fremad | First Division | 21 | 32 | — | 13.9 | 56% | 5.3 | 1.2 |
| Middelfart | First Division | 21 | 21 | — | 10 | 47% | 4.1 | 1.9 |
| Silkeborg IF | First Division | 32 | 76 | — | 14.6 | 53% | 5.4 | 1.4 |
| Hillerød | First Division | 85 | 146 | — | 13.7 | 53% | 4.5 | 1.4 |
| Horsens | First Division | 118 | 169 | — | 12.4 | 48% | 5.1 | 1.9 |
| Sønderjyske Fodbold | First Division | 32 | 71 | — | 14.1 | 56% | 5.1 | 1.6 |