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 |
|---|---|---|---|---|---|---|---|---|
| Western Sydney Wanderers W | A-League Women | 93 | 97 | — | 12 | 47% | 3.7 | 1.1 |
| Brisbane Roar W | A-League Women | 91 | 164 | — | 15.1 | 51% | 5.3 |
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.
| 1.1 |
| Canberra United W | A-League Women | 95 | 141 | — | 13.1 | 47% | 4.3 | 0.9 |
| Melbourne Victory W | A-League Women | 102 | 189 | — | 14.3 | 52% | 5 | 1.4 |
| Melbourne City W | A-League Women | 100 | 195 | — | 13.9 | 58% | 4.7 | 1 |
| Newcastle Jets W | A-League Women | 95 | 129 | — | 14.6 | 49% | 4.2 | 1.5 |
| Perth Glory W | A-League Women | 92 | 102 | — | 10.3 | 47% | 4 | 1.4 |
| Adelaide United W | A-League Women | 95 | 152 | — | 13.8 | 51% | 4.5 | 0.8 |
| Sydney FC W | A-League Women | 99 | 155 | — | 15.1 | 48% | 5.5 | 1.2 |
| Central Coast Mariners W | A-League Women | 68 | 94 | — | 13.4 | 50% | 3.5 | 1.4 |
| Wellington Phoenix W | A-League Women | 77 | 110 | — | 13.5 | 50% | 4.5 | 1.3 |
| Western United W | A-League Women | 47 | 78 | — | 16.7 | 50% | 5.9 | 0.9 |