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 |
|---|---|---|---|---|---|---|---|---|
| Zulte-Waregem | Pro League | 131 | 155 | 0 | 10.7 | 45% | 4.3 | 1.9 |
| Westerlo | Pro League | 122 | 163 | 0 | 13.8 | 47% | 5.1 | 2.2 |
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.
| Sint-Truiden | Pro League | 191 | 242 | — | 12.1 | 50% | 4.9 | 2.1 |
| Standard Liège | Pro League | 207 | 222 | — | 11.4 | 47% | 4.5 | 2.2 |
| Sporting Charleroi | Pro League | 184 | 243 | — | 13.1 | 51% | 5.1 | 1.9 |
| KV Kortrijk | Pro League | 140 | 156 | — | 11.8 | 46% | 4.9 | 2.3 |
| SK Beveren | Pro League | 34 | 44 | — | 10.5 | 45% | 4.2 | 2.3 |
| RFC Seraing | Pro League | 34 | 30 | — | 10.4 | 49% | 4.6 | 2 |
| OH Leuven | Pro League | 186 | 219 | — | 12.3 | 46% | 4.7 | 2.3 |
| Gent | Pro League | 222 | 349 | — | 13.3 | 52% | 5.3 | 1.8 |
| KAS Eupen | Pro League | 104 | 110 | — | 11.8 | 47% | 4.8 | 2.1 |
| K. Beerschot V.A. | Pro League | 104 | 127 | — | 11.2 | 46% | 4.6 | 2.2 |
| KV Oostende | Pro League | 74 | 98 | — | 12.8 | 47% | 5.6 | 2.4 |
| Anderlecht | Pro League | 227 | 340 | — | 13.3 | 54% | 5.2 | 1.9 |
| Cercle Brugge | Pro League | 179 | 229 | — | 13.3 | 46% | 5.1 | 2.2 |
| Genk | Pro League | 206 | 347 | — | 14.6 | 56% | 5.2 | 1.7 |
| Dender | Pro League | 69 | 76 | — | 10.7 | 45% | 3.9 | 2.1 |
| Royal Excel Mouscron | Pro League | 34 | 30 | — | 11.3 | 42% | 4.2 | 2.4 |
| Mechelen | Pro League | 215 | 311 | — | 12.4 | 50% | 4.9 | 1.9 |
| Union Saint-Gilloise | Pro League | 153 | 283 | — | 14.6 | 50% | 5.7 | 2.5 |
| La Louvière | Pro League | 29 | 25 | — | 10 | 37% | 4.8 | 2 |
| Antwerp | Pro League | 199 | 284 | — | 12.2 | 52% | 5 | 2.2 |
| RWDM Brussels | Pro League | 36 | 39 | — | 10.1 | 45% | 3.7 | 2.1 |
| Club Brugge | Pro League | 222 | 413 | — | 15.5 | 57% | 6.1 | 1.6 |