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 |
|---|---|---|---|---|---|---|---|---|
| Magenta | OFC Champions League | 5 | 12 | — | — | — | — | 0 |
| Auckland City | OFC Champions League | 19 | 46 | — | 5.2 | 59% | 6.1 | 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.
| Pirae | OFC Champions League | 14 | 34 | — | 2.4 | — | 3.7 | 1.1 |
| Rewa | OFC Champions League | 10 | 14 | — | 8 | — | 2.5 | 1.5 |
| Suva | OFC Champions League | 5 | 11 | — | 0 | — | — | 1 |
| Lupe Ole Soaga | OFC Champions League | 3 | 8 | — | 0 | — | — | 0 |
| Tupapa Maraerenga | OFC Champions League | 9 | 18 | — | 6.7 | — | 3.3 | 0.8 |
| Solomon Warriors | OFC Champions League | 8 | 8 | — | 0 | — | — | 0.1 |
| Hekari United | OFC Champions League | 13 | 26 | — | 10.1 | — | 5 | 0.8 |
| Veitongo | OFC Champions League | 3 | 15 | — | — | — | — | 0 |
| Pago Youth | OFC Champions League | 3 | 0 | — | — | — | — | 0.3 |
| Central Coast | OFC Champions League | 5 | 9 | — | 23.3 | — | 5 | 1.4 |
| Wellington Olympic | OFC Champions League | 4 | 7 | — | 6.8 | 41% | 1.8 | 2.8 |
| Tiga Sport | OFC Champions League | 7 | 6 | — | 4.8 | — | 5.3 | 1 |
| Ifira Black Bird | OFC Champions League | 13 | 20 | — | 7 | — | 3.3 | 1 |
| Vaiala Tongan | OFC Champions League | 3 | 0 | — | — | — | — | 0 |
| Vaivase-Tai | OFC Champions League | 9 | 19 | — | — | — | — | 0.4 |