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 |
|---|---|---|---|---|---|---|---|---|
| SuperSport United | Premier League | 5 | 4 | — | 9.8 | 43% | 4.6 | 1.2 |
| Golden Arrows | Premier League | 36 | 39 | — | 10.9 | 51% | 4.3 | 1.6 |
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.
| Kaizer Chiefs | Premier League | 33 | 35 | — | 13.5 | 58% | 4.6 | 2 |
| Cape Town City | Premier League | 3 | 2 | — | 8 | 40% | 4.3 | 3 |
| Stellenbosch | Premier League | 35 | 32 | — | 9.9 | 49% | 4.8 | 2.1 |
| Mamelodi Sundowns | Premier League | 36 | 73 | — | 13.7 | 71% | 4.3 | 1.8 |
| AmaZulu | Premier League | 32 | 34 | — | 8 | 42% | 3.8 | 1.7 |
| Orlando Pirates | Premier League | 38 | 69 | — | 16.4 | 60% | 6.1 | 1.2 |
| Polokwane City | Premier League | 33 | 22 | — | 12.1 | 44% | 4.8 | 1.6 |
| Chippa United | Premier League | 35 | 25 | — | 8.7 | 48% | 4 | 1.4 |
| Magesi | Premier League | 34 | 27 | — | 10.7 | 44% | 3.9 | 2.2 |
| Richards Bay | Premier League | 34 | 26 | — | 10.7 | 45% | 3.9 | 1.3 |
| Marumo Gallants FC | Premier League | 32 | 23 | — | 10.4 | 49% | 4.2 | 1.8 |
| TS Galaxy | Premier League | 32 | 31 | — | 10.9 | 50% | 3.7 | 2.3 |
| Sekhukhune United | Premier League | 34 | 37 | — | 11.5 | 50% | 3.9 | 2 |
| Orbit College | Premier League | 30 | 21 | — | 8.3 | 41% | 3.7 | 2 |
| Durban City | Premier League | 30 | 25 | — | 10.4 | 47% | 4.2 | 1.7 |
| Siwelele | Premier League | 30 | 24 | — | 9.8 | 49% | 4.4 | 2 |