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 |
|---|---|---|---|---|---|---|---|---|
| Servette | Super League | 202 | 296 | 0 | 14.8 | 51% | 5.8 | 2.2 |
| Lausanne Sport | Super League | 214 | 272 | 0 | 12.9 | 48% | 4.8 | 2.3 |
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.
| Thun | Super League | 76 | 133 | 0 | 13.6 | 48% | 5.5 | 1.6 |
| Basel | Super League | 248 | 431 | — | 14.3 | 54% | 5.3 | 1.9 |
| Young Boys | Super League | 216 | 421 | — | 16.2 | 55% | 6.5 | 2.1 |
| Grasshopper | Super League | 202 | 213 | — | 11.1 | 47% | 4.7 | 2.2 |
| Luzern | Super League | 230 | 347 | — | 14.1 | 49% | 5.6 | 2.3 |
| Winterthur | Super League | 118 | 146 | — | 12 | 45% | 4 | 2.4 |
| Yverdon Sport | Super League | 76 | 86 | — | 11.1 | 45% | 3.9 | 2.7 |
| Stade Lausanne-Ouchy | Super League | 38 | 40 | — | 9.6 | 51% | 4.4 | 2.9 |
| Vaduz | Super League | 36 | 36 | — | 10.1 | 46% | 4.2 | 2.3 |
| St. Gallen | Super League | 256 | 373 | — | 14.5 | 50% | 5.6 | 2.2 |
| Lugano | Super League | 245 | 338 | — | 12.2 | 52% | 4.3 | 2.5 |
| Zürich | Super League | 223 | 326 | — | 13.5 | 49% | 5.1 | 2.3 |
| Sion | Super League | 192 | 235 | — | 11.9 | 48% | 4.5 | 2.2 |