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 |
|---|---|---|---|---|---|---|---|---|
| Vitória BA W | Brasileiro Women | 11 | 7 | — | 6.3 | 41% | 2.5 | 2.5 |
| Santos W | Brasileiro Women | 12 | 12 | — | 8.3 | 52% | 4.8 | 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.
| Corinthians W | Brasileiro Women | 26 | 63 | — | 18.9 | 59% | 7.3 | 2 |
| Ferroviária W | Brasileiro Women | 22 | 27 | — | 16.6 | 56% | 5.5 | 1.9 |
| Flamengo W | Brasileiro Women | 22 | 39 | — | 14.8 | 53% | 4.8 | 2.3 |
| Internacional RS W | Brasileiro Women | 21 | 24 | — | 12.4 | 48% | 5 | 1.8 |
| Sport Recife W | Brasileiro Women | 8 | 4 | — | 9.6 | 46% | 2.9 | 2.4 |
| São Paulo W | Brasileiro Women | 24 | 38 | — | 13.6 | 54% | 5.2 | 1.9 |
| Palmeiras W | Brasileiro Women | 24 | 57 | — | 18.2 | 61% | 7.1 | 1.7 |
| Grêmio W | Brasileiro Women | 21 | 31 | — | 13.5 | 51% | 5.3 | 2.7 |
| Cruzeiro W | Brasileiro Women | 26 | 48 | — | 12.8 | 52% | 5.5 | 1.7 |
| RB Bragantino W | Brasileiro Women | 22 | 30 | — | 14.3 | 50% | 4.4 | 1.5 |
| Botafogo W | Brasileiro Women | 11 | 7 | — | 9.7 | 48% | 4.2 | 2.6 |
| Real Brasília W | Brasileiro Women | 8 | 4 | — | 6.1 | 41% | 1.9 | 2.3 |
| Bahia W | Brasileiro Women | 22 | 36 | — | 11.6 | 49% | 4.7 | 2.6 |
| América Mineiro W | Brasileiro Women | 20 | 16 | — | 8.8 | 44% | 3.3 | 1.7 |
| Fluminense W | Brasileiro Women | 20 | 24 | — | 11.6 | 47% | 5 | 2.4 |
| Atlético Mineiro W | Brasileiro Women | 12 | 14 | — | 9.6 | 48% | 3.6 | 3 |
| Mixto W | Brasileiro Women | 12 | 8 | — | 7.8 | 42% | 2.9 | 1.6 |
| Juventude W | Brasileiro Women | 20 | 13 | — | 8.9 | 38% | 2.8 | 2.1 |
| 3B da Amazônia W | Brasileiro Women | 8 | 8 | — | 9.6 | 44% | 3 | 2.5 |