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 | Olympic Games Women | 8 | 16 | — | 11.5 | 51% | 4.9 | 0.8 |
| United States W | Olympic Games Women | 11 | 23 | — | 15.4 | 55% | 5.5 | 0.8 |
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.
| Sweden W | Olympic Games Women | 6 | 14 | — | 14.3 | 49% | 7.3 | 0.3 |
| China W | Olympic Games Women | 3 | 6 | — | 10 | 43% | 4 | 1.3 |
| Germany W | Olympic Games Women | 6 | 9 | — | 14.2 | 49% | 5 | 1.2 |
| Canada W | Olympic Games Women | 10 | 11 | — | 12.3 | 47% | 3.7 | 0.9 |
| France W | Olympic Games Women | 4 | 6 | — | 12.8 | 64% | 4.5 | 1 |
| Brazil W | Olympic Games Women | 10 | 16 | — | 11.3 | 49% | 4.9 | 1.6 |
| New Zealand W | Olympic Games Women | 5 | 3 | — | 5.2 | 38% | 2.6 | 0.4 |
| Colombia W | Olympic Games Women | 4 | 4 | — | 14.3 | 50% | 4 | 1.5 |
| Netherlands W | Olympic Games Women | 4 | 23 | — | 17.7 | 55% | 6.5 | 1 |
| Spain W | Olympic Games Women | 6 | 7 | — | 21.4 | 73% | 10.2 | 1 |
| Japan W | Olympic Games Women | 7 | 9 | — | 15 | 46% | 4.4 | 0.9 |
| Nigeria W | Olympic Games Women | 3 | 1 | — | 9.3 | 42% | 2.7 | 0.7 |
| Chile W | Olympic Games Women | 3 | 1 | — | 3.5 | 39% | 1.7 | 0.7 |
| Zambia W | Olympic Games Women | 6 | 13 | — | 13 | 34% | 2.7 | 1.3 |
| Great Britain W | Olympic Games Women | 4 | 7 | — | 11 | 59% | 6 | 0.5 |