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 |
|---|---|---|---|---|---|---|---|---|
| KV Kortrijk | Challenger Pro League | 31 | 56 | — | 14.5 | 51% | 6.7 | 1.9 |
| SK Beveren | Challenger Pro League | 32 | 70 | — | 15.7 | 56% | 6 | 1.7 |
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.
| RFC Seraing | Challenger Pro League | 31 | 37 | — | 12.3 | 42% | 4.6 | 2.9 |
| KAS Eupen | Challenger Pro League | 32 | 44 | — | 10.5 | 52% | 3.9 | 2.1 |
| K. Beerschot V.A. | Challenger Pro League | 36 | 58 | — | 15.8 | 55% | 5.8 | 2.1 |
| Lommel SK | Challenger Pro League | 36 | 70 | — | 14.8 | 54% | 5.2 | 1.7 |
| Patro Eisden Maasmechelen | Challenger Pro League | 36 | 50 | — | 14.4 | 37% | 3.9 | 3.2 |
| RFC Liège | Challenger Pro League | 34 | 45 | — | 11.4 | 46% | 4.9 | 1.8 |
| Royal Francs Borains | Challenger Pro League | 32 | 33 | — | 12.8 | 47% | 5 | 2.4 |
| OC Charleroi | Challenger Pro League | 32 | 26 | — | 12 | 47% | 4 | 2.3 |
| RWDM Brussels | Challenger Pro League | 32 | 50 | — | 11.5 | 48% | 3.4 | 2.7 |
| Koninklijke Lierse Sportkring | Challenger Pro League | 32 | 32 | — | 12.1 | 48% | 4.3 | 2.4 |
| Club NXT U23 | Challenger Pro League | 32 | 31 | — | 11.6 | 56% | 3.9 | 1.8 |
| KSC Lokeren | Challenger Pro League | 34 | 50 | — | 13.1 | 54% | 6.9 | 3 |
| Jong KRC Genk U23 | Challenger Pro League | 32 | 42 | — | 12.8 | 50% | 4.1 | 1.9 |
| RSCA Futures U23 | Challenger Pro League | 32 | 45 | — | 13.7 | 52% | 4.6 | 2.1 |
| Jong Gent | Challenger Pro League | 32 | 42 | — | 12.4 | 58% | 4.4 | 1.9 |