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 |
|---|---|---|---|---|---|---|---|---|
| Borussia Dortmund | DFB Pokal | 3 | 2 | — | 15.3 | 58% | 7.7 | 2.3 |
| RB Leipzig | DFB Pokal | 4 | 11 | — | 17.3 | 56% | 5.8 | 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.
| St. Pauli | DFB Pokal | 4 | 4 | — | 20.3 | 53% | 9.3 | 2.3 |
| Darmstadt 98 | DFB Pokal | 3 | 6 | — | 14.7 | 55% | 6.3 | 1.7 |
| FC Bayern München | DFB Pokal | 6 | 17 | — | 14.5 | 63% | 4.3 | 1.7 |
| Borussia Mönchengladbach | DFB Pokal | 3 | 7 | — | 21.7 | 54% | 6.7 | 0.7 |
| VfL Bochum 1848 | DFB Pokal | 3 | 4 | — | 19 | 44% | 6.3 | 2.7 |
| FC Union Berlin | DFB Pokal | 3 | 9 | — | 20.7 | 47% | 5.7 | 2 |
| Kaiserslautern | DFB Pokal | 3 | 9 | — | 12.3 | 60% | 5.7 | 3.7 |
| Hamburger SV | DFB Pokal | 3 | 4 | — | 17.7 | 63% | 8.3 | 1.7 |
| DSC Arminia Bielefeld | DFB Pokal | 3 | 4 | — | 16 | 52% | 3.7 | 2 |
| Hertha BSC | DFB Pokal | 4 | 10 | — | 11.5 | 43% | 3 | 2 |
| VfB Stuttgart | DFB Pokal | 7 | 17 | — | 16.4 | 56% | 5.4 | 2.4 |
| Bayer 04 Leverkusen | DFB Pokal | 5 | 12 | — | 13.4 | 57% | 3.6 | 1.8 |
| Magdeburg | DFB Pokal | 3 | 7 | — | 14.7 | 59% | 5.7 | 2 |
| SC Freiburg | DFB Pokal | 5 | 9 | — | 16.6 | 51% | 3.4 | 2.2 |
| Holstein Kiel | DFB Pokal | 4 | 4 | — | 17.3 | 49% | 8.8 | 3 |