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 |
|---|---|---|---|---|---|---|---|---|
| Molde | Eliteserien | 165 | 323 | 0 | 14.8 | 53% | 5.8 | 1.7 |
| Lillestrøm | Eliteserien | 131 | 192 | 0 | 13.5 | 51% | 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.
| Sarpsborg 08 | Eliteserien | 162 | 249 | 0 | 14 | 50% | 5.3 | 1.7 |
| Vålerenga | Eliteserien | 132 | 203 | 0 | 13.1 | 52% | 5.8 | 1.4 |
| Tromsø | Eliteserien | 150 | 215 | 0 | 11.8 | 49% | 4.1 | 1.5 |
| HamKam | Eliteserien | 121 | 150 | 0 | 12 | 40% | 5 | 1.5 |
| Sandefjord | Eliteserien | 167 | 246 | 0 | 12.6 | 51% | 4.4 | 1.6 |
| Kristiansund | Eliteserien | 126 | 155 | 0 | 12.3 | 44% | 5.3 | 1.7 |
| Haugesund | Eliteserien | 150 | 173 | 0 | 11.2 | 47% | 4.4 | 1.8 |
| Odd | Eliteserien | 120 | 155 | 0 | 11.3 | 49% | 4.1 | 1.7 |
| Strømsgodset | Eliteserien | 161 | 202 | 0 | 13.2 | 47% | 5.5 | 1.5 |
| Rosenborg | Eliteserien | 166 | 285 | 0 | 13.2 | 51% | 5.2 | 1.8 |
| Viking | Eliteserien | 151 | 306 | 0 | 15.3 | 52% | 6.6 | 1.8 |
| Bodø / Glimt | Eliteserien | 160 | 390 | 0 | 17 | 61% | 6.6 | 1.1 |
| Aalesund | Eliteserien | 70 | 60 | 0 | 12.4 | 45% | 4.9 | 1.6 |
| Jerv | Eliteserien | 30 | 30 | 0 | 11.5 | 44% | 4.9 | 2 |
| KFUM | Eliteserien | 61 | 79 | — | 10.6 | 50% | 4.8 | 1.8 |
| Stabæk | Eliteserien | 66 | 67 | — | 11.2 | 47% | 4 | 1.3 |
| Ranheim | Eliteserien | 4 | 4 | — | 11.3 | 56% | 6.8 | 1.8 |
| Mjøndalen | Eliteserien | 36 | 41 | — | 10.7 | 44% | 3.8 | 1.7 |
| Fredrikstad | Eliteserien | 61 | 77 | — | 11.1 | 44% | 4.7 | 1.5 |
| Brann | Eliteserien | 124 | 207 | — | 15 | 59% | 7 | 1.5 |
| Start | Eliteserien | 7 | 5 | — | 10.5 | 50% | 5.7 | 1.4 |
| Bryne | Eliteserien | 30 | 37 | — | 11.2 | 45% | 4 | 1.8 |
| Sogndal | Eliteserien | 6 | 5 | — | 13 | 39% | 5.2 | 0.5 |