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 | 2. Bundesliga | 37 | 51 | — | 14 | 46% | 4.9 | 2.3 |
| Darmstadt 98 | 2. Bundesliga | 37 | 61 | — | 14.2 | 52% | 5 | 2 |
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.
| Ulm | 2. Bundesliga | 3 | 4 | — | 15 | 57% | 4.3 | 1.7 |
| Nürnberg | 2. Bundesliga | 37 | 53 | — | 12.8 | 50% | 5 | 2.6 |
| VfL Bochum 1848 | 2. Bundesliga | 34 | 49 | — | 14 | 46% | 5 | 3.1 |
| Dynamo Dresden | 2. Bundesliga | 34 | 52 | — | 13 | 50% | 5 | 1.9 |
| Fortuna Düsseldorf | 2. Bundesliga | 37 | 39 | — | 12.5 | 50% | 4.4 | 2.7 |
| Kaiserslautern | 2. Bundesliga | 37 | 56 | — | 12.9 | 48% | 4.7 | 2.5 |
| Hannover 96 | 2. Bundesliga | 37 | 64 | — | 13.9 | 56% | 5.9 | 2.2 |
| Paderborn | 2. Bundesliga | 37 | 63 | — | 15.5 | 53% | 5.2 | 2 |
| Hamburger SV | 2. Bundesliga | 3 | 12 | — | 13.3 | 49% | 5 | 2.3 |
| DSC Arminia Bielefeld | 2. Bundesliga | 34 | 53 | — | 15 | 52% | 6.1 | 2 |
| Karlsruher SC | 2. Bundesliga | 37 | 59 | — | 12.4 | 49% | 4.6 | 2.6 |
| Hertha BSC | 2. Bundesliga | 37 | 49 | — | 12.6 | 49% | 4.8 | 2.4 |
| FC Köln | 2. Bundesliga | 3 | 7 | — | 22.3 | 53% | 7.3 | 0.3 |
| SpVgg Greuther Fürth | 2. Bundesliga | 37 | 53 | — | 12 | 47% | 4.5 | 2 |
| Jahn Regensburg | 2. Bundesliga | 3 | 4 | — | 13.7 | 39% | 4.3 | 0.3 |
| Magdeburg | 2. Bundesliga | 37 | 57 | — | 15.1 | 54% | 5.2 | 2.5 |
| Eintracht Braunschweig | 2. Bundesliga | 37 | 39 | — | 12.2 | 43% | 4.6 | 2.4 |
| Elversberg | 2. Bundesliga | 37 | 72 | — | 14.7 | 54% | 5.9 | 1.8 |
| Holstein Kiel | 2. Bundesliga | 34 | 44 | — | 12 | 52% | 5.2 | 3.1 |
| Preußen Münster | 2. Bundesliga | 37 | 47 | — | 11.9 | 50% | 4.1 | 3 |