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 |
|---|---|---|---|---|---|---|---|---|
| Australia W | Women's Olympic Qualifying | 9 | 39 | — | 27 | 65% | 8.1 | 0.4 |
| China W | Women's Olympic Qualifying | 8 | 21 | — | 0 | 53% | 4.7 | 0.6 |
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.
| North Korea W | Women's Olympic Qualifying | 5 | 10 | — | 2.6 | 52% | 3 | 1 |
| South Korea W | Women's Olympic Qualifying | 7 | 17 | — | 0 | 65% | 2 | 0.3 |
| Japan W | Women's Olympic Qualifying | 5 | 13 | — | 10.8 | 68% | 6 | 0 |
| Thailand W | Women's Olympic Qualifying | 6 | 8 | — | 0 | 47% | 8.7 | 0.5 |
| Jordan W | Women's Olympic Qualifying | 5 | 3 | — | 7.8 | 48% | 4.5 | 1 |
| Chinese Taipei W | Women's Olympic Qualifying | 8 | 11 | — | 8.4 | 42% | 2.9 | 0.1 |
| Myanmar W | Women's Olympic Qualifying | 5 | 17 | — | 14.7 | 48% | 6.6 | 1.4 |
| Hong Kong W | Women's Olympic Qualifying | 4 | 2 | — | 5.3 | 37% | 1.3 | 0 |
| Philippines W | Women's Olympic Qualifying | 7 | 20 | — | 12.7 | 51% | 5.7 | 0.7 |
| Vietnam W | Women's Olympic Qualifying | 9 | 9 | — | 12.8 | 48% | 4.3 | 0.7 |
| India W | Women's Olympic Qualifying | 7 | 14 | — | 7.4 | 42% | 4.3 | 0.4 |
| Uzbekistan W | Women's Olympic Qualifying | 7 | 13 | — | 6 | 39% | 5.4 | 0.6 |
| Iran W | Women's Olympic Qualifying | 5 | 2 | — | 7.2 | 47% | 4.2 | 1 |