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 |
|---|---|---|---|---|---|---|---|---|
| St. Gallen | Super League | 42 | 76 | — | 15.4 | 46% | 5.5 | 3 |
| Zürich | Super League | 42 | 61 | — | 13.4 | 52% | 4.8 | 2.7 |
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.
| Sion | Super League | 42 | 67 | — | 13.7 | 51% | 5.2 | 2.2 |
| Basel | Super League | 41 | 74 | — | 15.7 | 55% | 5.9 | 2 |
| Young Boys | Super League | 41 | 87 | — | 15 | 54% | 5 | 2 |
| Servette | Super League | 42 | 78 | — | 13.8 | 51% | 5.8 | 2.5 |
| Lausanne Sport | Super League | 42 | 60 | — | 14.5 | 50% | 5.2 | 2.6 |
| Grasshopper | Super League | 42 | 55 | — | 12.7 | 46% | 4.8 | 2.8 |
| Luzern | Super League | 42 | 79 | — | 14.5 | 50% | 5.5 | 2.3 |
| Winterthur | Super League | 42 | 52 | — | 12 | 44% | 4 | 2.4 |
| Yverdon Sport | Super League | 5 | 7 | — | 10 | 44% | 4 | 3 |
| Thun | Super League | 37 | 80 | — | 17.2 | 46% | 5.8 | 2.6 |
| Lugano | Super League | 42 | 65 | — | 13.1 | 56% | 3.9 | 2.5 |