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 |
|---|---|---|---|---|---|---|---|---|
| Schalke 04 | Bundesliga | 77 | 66 | 0 | 10.8 | 46% | 4.2 | 2 |
| Borussia Dortmund | Bundesliga | 221 | 481 | 0 | 14.7 | 59% | 5.5 | 1.8 |
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.
| Werder Bremen | Bundesliga | 179 | 238 | 0 | 12 | 48% | 4 | 2.3 |
| FC Augsburg | Bundesliga | 218 | 254 | 0 | 11.1 | 42% | 4.3 | 2.4 |
| RB Leipzig | Bundesliga | 208 | 389 | 0 | 14.5 | 56% | 5.2 | 1.8 |
| Borussia Mönchengladbach | Bundesliga | 209 | 325 | 0 | 12.7 | 50% | 4.6 | 1.8 |
| FSV Mainz 05 | Bundesliga | 221 | 293 | 0 | 12.7 | 46% | 4.9 | 2.2 |
| VfL Bochum 1848 | Bundesliga | 136 | 152 | 0 | 13.1 | 45% | 4.3 | 2.2 |
| FC Union Berlin | Bundesliga | 200 | 255 | 0 | 11.9 | 42% | 4.5 | 2 |
| TSG Hoffenheim | Bundesliga | 212 | 337 | 0 | 13.2 | 50% | 5 | 2.2 |
| Hertha BSC | Bundesliga | 102 | 119 | 0 | 11.1 | 45% | 4.2 | 2.1 |
| VfB Stuttgart | Bundesliga | 210 | 344 | 0 | 14 | 54% | 4.8 | 2 |
| FC Köln | Bundesliga | 182 | 212 | 0 | 12.4 | 48% | 5 | 2 |
| Bayer 04 Leverkusen | Bundesliga | 234 | 445 | 0 | 14.3 | 57% | 5.5 | 2 |
| SC Freiburg | Bundesliga | 211 | 301 | 0 | 12.1 | 48% | 4.2 | 1.7 |
| Eintracht Frankfurt | Bundesliga | 196 | 339 | 0 | 12.7 | 52% | 5.2 | 2 |
| FC Bayern München | Bundesliga | 224 | 614 | 0 | 18.4 | 64% | 6.5 | 1.4 |
| VfL Wolfsburg | Bundesliga | 223 | 318 | 0 | 12.5 | 49% | 4.5 | 2.1 |
| St. Pauli | Bundesliga | 60 | 51 | — | 10.8 | 44% | 4.5 | 1.6 |
| Fortuna Düsseldorf | Bundesliga | 3 | 3 | — | 15.5 | 68% | 5.5 | 1.7 |
| Darmstadt 98 | Bundesliga | 34 | 30 | — | 12 | 46% | 3.4 | 2.6 |
| Hannover 96 | Bundesliga | 9 | 3 | — | 14 | 48% | 4.4 | 1.6 |
| Hamburger SV | Bundesliga | 56 | 48 | — | 11.5 | 47% | 3.9 | 2.5 |
| Holstein Kiel | Bundesliga | 34 | 49 | — | 11 | 44% | 3.5 | 2.4 |
| Heidenheim | Bundesliga | 94 | 111 | — | 11.6 | 42% | 4.7 | 1.7 |
| Nürnberg | Bundesliga | 3 | 6 | — | 8 | 32% | 3.5 | 1.7 |
| DSC Arminia Bielefeld | Bundesliga | 68 | 55 | — | 10.2 | 41% | 3.9 | 1.6 |
| SpVgg Greuther Fürth | Bundesliga | 34 | 28 | — | 9.2 | 44% | 4.1 | 1.8 |