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 |
|---|---|---|---|---|---|---|---|---|
| Westerlo | Challenger Pro League | 61 | 91 | — | 11.2 | 54% | 5.4 | 2.1 |
| Zulte-Waregem | Challenger Pro League | 61 | 109 | — | 15.9 | 54% | 6 | 1.9 |
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.
| KV Kortrijk | Challenger Pro League | 27 | 50 | — | 14 | 51% | 6.7 | 2 |
| SK Beveren | Challenger Pro League | 122 | 201 | — | 13.4 | 54% | 5.5 | 2.5 |
| RFC Seraing | Challenger Pro League | 114 | 150 | — | 11.6 | 47% | 4.9 | 2.8 |
| OH Leuven | Challenger Pro League | 12 | 14 | — | 7.5 | 49% | 4.8 | 1.5 |
| Deinze | Challenger Pro League | 101 | 160 | — | 12.7 | 51% | 5.2 | 2.4 |
| KAS Eupen | Challenger Pro League | 56 | 77 | — | 10.7 | 51% | 4.3 | 2.5 |
| K. Beerschot V.A. | Challenger Pro League | 65 | 99 | — | 15.9 | 57% | 6.3 | 2.3 |
| KV Oostende | Challenger Pro League | 30 | 32 | — | 11.6 | 45% | 5 | 2.3 |
| Lommel SK | Challenger Pro League | 141 | 222 | — | 12.5 | 55% | 5 | 2.1 |
| Dender | Challenger Pro League | 30 | 55 | — | 15.6 | 50% | 5.5 | 2.4 |
| Royal Excel Mouscron | Challenger Pro League | 24 | 27 | — | 9.4 | 46% | 3.9 | 2.7 |
| Patro Eisden Maasmechelen | Challenger Pro League | 94 | 143 | — | 14 | 38% | 4.9 | 2.9 |
| RFC Liège | Challenger Pro League | 87 | 128 | — | 13.4 | 45% | 5.6 | 2.3 |
| Union Saint-Gilloise | Challenger Pro League | 26 | 66 | — | 12.6 | 52% | 6.2 | 2.8 |
| Royal Francs Borains | Challenger Pro League | 87 | 95 | — | 12.3 | 47% | 4.7 | 2.8 |
| La Louvière | Challenger Pro League | 29 | 52 | — | 13.8 | 42% | 5.3 | 2.8 |
| Excelsior Virton | Challenger Pro League | 26 | 24 | — | 7.4 | 42% | 3 | 3 |
| OC Charleroi | Challenger Pro League | 28 | 24 | — | 11.8 | 47% | 3.9 | 2.4 |
| RWDM Brussels | Challenger Pro League | 111 | 172 | — | 12 | 51% | 5 | 2.5 |
| Koninklijke Lierse Sportkring | Challenger Pro League | 143 | 178 | — | 11.4 | 48% | 4.7 | 2.5 |
| Club NXT U23 | Challenger Pro League | 124 | 150 | — | 10.2 | 53% | 4.1 | 1.9 |
| KSC Lokeren | Challenger Pro League | 61 | 78 | — | 12.3 | 52% | 6.3 | 3 |
| Jong KRC Genk U23 | Challenger Pro League | 91 | 116 | — | 13.1 | 51% | 4.8 | 2.1 |
| RSCA Futures U23 | Challenger Pro League | 91 | 121 | — | 12.5 | 51% | 4.5 | 2.1 |
| Standard Liège II | Challenger Pro League | 30 | 25 | — | 11 | 47% | 5.1 | 3.2 |
| Jong Gent | Challenger Pro League | 28 | 38 | — | 12.2 | 57% | 4.4 | 1.8 |