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 | 100 | 267 | — | 17.2 | 59% | 7.2 | 0.8 |
| Manchester City W | Women's Super League | 104 | 280 | — | 17.2 | 62% | 6.8 | 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.
| Bristol City W | Women's Super League | 37 | 33 | — | 7.1 | 35% | 2.8 | 1.1 |
| Arsenal W | Women's Super League | 99 | 261 | — | 17 | 61% | 7.6 | 1.1 |
| Reading W | Women's Super League | 37 | 33 | — | 7.1 | 46% | 3.6 | 1.1 |
| Birmingham W | Women's Super League | 37 | 26 | — | 4.5 | 36% | 2.3 | 1 |
| Tottenham W | Women's Super League | 105 | 125 | — | 11.2 | 49% | 4.5 | 1.6 |
| Aston Villa W | Women's Super League | 101 | 103 | — | 9.7 | 48% | 4.4 | 1.4 |
| Brighton W | Women's Super League | 96 | 120 | — | 9.5 | 47% | 4.1 | 1.5 |
| London City Lionesses W | Women's Super League | 17 | 18 | — | 10.3 | 49% | 5 | 1.8 |
| Everton W | Women's Super League | 105 | 125 | — | 8.4 | 47% | 3.9 | 1.2 |
| Liverpool W | Women's Super League | 63 | 75 | — | 10.7 | 48% | 4.1 | 1.6 |
| West Ham W | Women's Super League | 101 | 110 | — | 9.4 | 44% | 3.3 | 1.3 |
| Crystal Palace W | Women's Super League | 22 | 20 | — | 8.1 | 40% | 3.2 | 1.5 |
| Leicester W | Women's Super League | 81 | 72 | — | 9.1 | 41% | 3.3 | 1.5 |
| Manchester United W | Women's Super League | 99 | 204 | — | 13.5 | 54% | 5.5 | 1.2 |