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 |
|---|---|---|---|---|---|---|---|---|
| Antwerp | Pro League | 44 | 48 | — | 12 | 50% | 4.6 | 2.2 |
| Club Brugge | Pro League | 44 | 101 | — | 17.5 | 60% | 6.4 | 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.
| Sint-Truiden | Pro League | 42 | 65 | — | 13.8 | 54% | 5.6 | 1.5 |
| Westerlo | Pro League | 44 | 59 | — | 14.4 | 49% | 5.4 | 1.9 |
| Standard Liège | Pro League | 44 | 44 | — | 9.7 | 44% | 4.1 | 2.2 |
| Sporting Charleroi | Pro League | 44 | 58 | — | 13 | 50% | 5.6 | 2.2 |
| Zulte-Waregem | Pro League | 36 | 53 | — | 12.7 | 48% | 4.7 | 1.5 |
| OH Leuven | Pro League | 44 | 43 | — | 12.7 | 47% | 4.9 | 2.8 |
| Gent | Pro League | 45 | 57 | — | 12.5 | 49% | 4.6 | 1.8 |
| Anderlecht | Pro League | 44 | 64 | — | 14.4 | 52% | 5.5 | 2.6 |
| Cercle Brugge | Pro League | 38 | 55 | — | 14.6 | 48% | 5.2 | 2 |
| Genk | Pro League | 45 | 64 | — | 15.6 | 57% | 5.4 | 1.6 |
| Dender | Pro League | 40 | 40 | — | 10.9 | 48% | 3.8 | 2.2 |
| Mechelen | Pro League | 44 | 53 | — | 10.3 | 50% | 4.7 | 2.1 |
| Union Saint-Gilloise | Pro League | 44 | 75 | — | 14.9 | 51% | 5.6 | 2.4 |
| La Louvière | Pro League | 36 | 35 | — | 10.7 | 39% | 5 | 1.9 |