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 |
|---|---|---|---|---|---|---|---|---|
| Chelsea W | UEFA Women's Champions League | 8 | 22 | — | 20.3 | 60% | 5.9 | 0.6 |
| FC Twente W | UEFA Women's Champions League | 10 | 20 | — | 15.2 | 55% | 6.5 |
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.
| 1.1 |
| Arsenal W | UEFA Women's Champions League | 13 | 25 | — | 14.3 | 52% | 4.8 | 1.2 |
| OL Lyonnes W | UEFA Women's Champions League | 11 | 26 | — | 22.8 | 62% | 9.2 | 1.1 |
| Barcelona W | UEFA Women's Champions League | 12 | 41 | — | 23.1 | 69% | 8.8 | 0.8 |
| Atletico Madrid W | UEFA Women's Champions League | 10 | 16 | — | 16.5 | 56% | 5.3 | 1.7 |
| Wolfsburg W | UEFA Women's Champions League | 10 | 18 | — | 12.5 | 52% | 3.7 | 1.5 |
| Bayern Munich W | UEFA Women's Champions League | 10 | 22 | — | 13.9 | 52% | 5.2 | 0.8 |
| Fortuna Hjorring W | UEFA Women's Champions League | 4 | 5 | — | 6.5 | 49% | 2.5 | 1.5 |
| St. Polten W | UEFA Women's Champions League | 8 | 8 | — | 8.9 | 39% | 1.8 | 1.4 |
| Paris SG W | UEFA Women's Champions League | 6 | 4 | — | 17 | 50% | 5.7 | 1.7 |
| GKS Katowice W | UEFA Women's Champions League | 4 | 5 | — | 13.5 | 47% | 3.8 | 1.5 |
| Manchester United W | UEFA Women's Champions League | 14 | 23 | — | 11.1 | 50% | 3.9 | 1.2 |
| Juventus W | UEFA Women's Champions League | 8 | 15 | — | 13.5 | 42% | 5.3 | 1.4 |
| Roma W | UEFA Women's Champions League | 10 | 19 | — | 14.6 | 52% | 5.3 | 1 |
| OH Leuven W | UEFA Women's Champions League | 12 | 11 | — | 9.9 | 43% | 4.5 | 1.4 |
| Paris W | UEFA Women's Champions League | 10 | 10 | — | 10 | 49% | 3.9 | 0.7 |
| Vålerenga W | UEFA Women's Champions League | 10 | 14 | — | 11.4 | 45% | 4.2 | 0.7 |
| Ferencvárosi W | UEFA Women's Champions League | 4 | 8 | — | 3.3 | 42% | 2.3 | 1.3 |
| SL Benfica W | UEFA Women's Champions League | 6 | 4 | — | 9.5 | 39% | 2.3 | 2.5 |
| Athlone Town WFC W | UEFA Women's Champions League | 4 | 10 | — | 10 | 53% | 5.5 | 0.5 |
| Real Madrid W | UEFA Women's Champions League | 12 | 25 | — | 13.4 | 49% | 4.4 | 1.9 |
| Racing W | UEFA Women's Champions League | 4 | 5 | — | 9 | 45% | 1 | 1.8 |
| Brann W | UEFA Women's Champions League | 4 | 4 | — | 8 | 53% | 4 | 1.3 |
| Spartak Myjava W | UEFA Women's Champions League | 4 | 7 | — | 9 | 53% | 3.3 | 1.8 |
| Fomget Gençlik W | UEFA Women's Champions League | 4 | 6 | — | 15 | 58% | 3.7 | 1.8 |
| Ljuboten W | UEFA Women's Champions League | 4 | 8 | — | 11 | 42% | 2.3 | 2 |
| Austria Wien W | UEFA Women's Champions League | 4 | 5 | — | 6.3 | 46% | 5.3 | 1 |
| Vorskla Poltava W | UEFA Women's Champions League | 4 | 9 | — | 9.3 | 49% | 6.8 | 2.8 |