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 |
|---|---|---|---|---|---|---|---|---|
| Sydney | A-League Men | 161 | 260 | — | 15.7 | 53% | 5.8 | 1.8 |
| Perth Glory | A-League Men | 149 | 189 | — | 12.2 | 46% | 4.5 | 1.9 |
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.
| Melbourne City | A-League Men | 133 | 234 | — | 15.6 | 55% | 6.3 | 2.1 |
| Central Coast Mariners | A-League Men | 128 | 206 | — | 12.5 | 47% | 4.5 | 1.9 |
| Newcastle Jets | A-League Men | 147 | 219 | — | 13.9 | 50% | 5.4 | 1.6 |
| Melbourne Victory | A-League Men | 167 | 235 | — | 14.8 | 50% | 5.8 | 1.9 |
| Brisbane Roar | A-League Men | 152 | 191 | — | 14.6 | 49% | 5.3 | 2 |
| Western Sydney Wanderers | A-League Men | 175 | 254 | — | 15.5 | 51% | 5.7 | 1.7 |
| Wellington Phoenix | A-League Men | 178 | 222 | — | 12.4 | 49% | 4.6 | 1.7 |
| Adelaide United | A-League Men | 161 | 264 | — | 14.9 | 52% | 6.2 | 1.9 |
| Western United | A-League Men | 110 | 171 | — | 13.6 | 48% | 5.4 | 2.1 |
| Macarthur | A-League Men | 134 | 200 | — | 13.9 | 49% | 5.8 | 2.3 |
| Auckland | A-League Men | 49 | 86 | — | 15.5 | 48% | 5 | 1.6 |