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 | Women's Super League | 25 | 47 | — | 17.2 | 60% | 7.4 | 0.9 |
| Manchester City W | Women's Super League | 24 | 69 | — | 18.7 | 57% | 7.5 | 0.7 |
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.
| Arsenal W | Women's Super League | 25 | 61 | — | 18.3 | 60% | 7.6 | 0.9 |
| Tottenham W | Women's Super League | 24 | 36 | — | 12.6 | 52% | 4.5 | 1.8 |
| Aston Villa W | Women's Super League | 25 | 36 | — | 11.3 | 48% | 4.2 | 1.4 |
| Brighton W | Women's Super League | 24 | 32 | — | 10.7 | 48% | 3.9 | 1.8 |
| London City Lionesses W | Women's Super League | 22 | 28 | — | 11 | 50% | 5.4 | 1.5 |
| Everton W | Women's Super League | 24 | 28 | — | 8.5 | 45% | 3.9 | 1.6 |
| Liverpool W | Women's Super League | 24 | 21 | — | 9 | 48% | 3.8 | 1.5 |
| West Ham W | Women's Super League | 24 | 24 | — | 10 | 42% | 3.3 | 1.3 |
| Leicester W | Women's Super League | 24 | 17 | — | 7.6 | 37% | 2.8 | 1.4 |
| Manchester United W | Women's Super League | 25 | 43 | — | 13.6 | 53% | 4.7 | 1.2 |