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 |
|---|---|---|---|---|---|---|---|---|
| FC København | Superliga | 175 | 319 | — | 14.7 | 54% | 4.9 | 1.9 |
| Silkeborg IF | Superliga | 120 | 177 | — | 12.4 | 53% | 4.5 | 1.2 |
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.
| Horsens | Superliga | 42 | 42 | — | 10.5 | 42% | 4.4 | 1.9 |
| Brøndby IF | Superliga | 151 | 247 | — | 12.9 | 53% | 5 | 1.8 |
| Sønderjyske Fodbold | Superliga | 118 | 154 | — | 11.8 | 46% | 4.3 | 2 |
| FC Midtjylland | Superliga | 151 | 300 | — | 15 | 51% | 5.3 | 1.9 |
| Aalborg BK | Superliga | 112 | 149 | — | 11.5 | 51% | 5.5 | 1.9 |
| Hobro | Superliga | 14 | 5 | — | 7.5 | 43% | 3.1 | 1.5 |
| Odense BK | Superliga | 142 | 175 | — | 11.7 | 48% | 4.8 | 1.8 |
| Randers FC | Superliga | 153 | 201 | — | 12.5 | 48% | 5.2 | 1.9 |
| Nordsjælland | Superliga | 151 | 240 | — | 13.2 | 56% | 5.1 | 1.8 |
| Viborg FF | Superliga | 120 | 179 | — | 13.6 | 49% | 5.2 | 2 |
| Lyngby Boldklub | Superliga | 106 | 109 | — | 11.4 | 50% | 5.2 | 2 |
| AGF | Superliga | 152 | 222 | — | 13.1 | 50% | 5.2 | 2.1 |
| Fredericia | Superliga | 23 | 30 | — | 11 | 43% | 4.4 | 1.3 |
| Vejle Boldklub | Superliga | 150 | 168 | — | 11.5 | 44% | 4.3 | 2.2 |
| Hvidovre | Superliga | 32 | 27 | — | 10.7 | 51% | 4.4 | 2.1 |