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 | 114 | 122 | — | 9.1 | 42% | 4.6 | 1.8 |
| Golden Arrows | Premier League | 144 | 156 | — | 7.8 | 43% | 3.9 | 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.
| Cape Town Spurs | Premier League | 30 | 23 | — | 9.5 | 43% | 3.9 | 1.9 |
| Kaizer Chiefs | Premier League | 139 | 138 | — | 11.1 | 52% | 4.8 | 2 |
| Maritzburg United | Premier League | 56 | 47 | — | 5.7 | 38% | 4.1 | 1.3 |
| Cape Town City | Premier League | 115 | 114 | — | 8.7 | 46% | 4.1 | 1.9 |
| Stellenbosch | Premier League | 137 | 145 | — | 9 | 44% | 4.1 | 1.8 |
| Bloemfontein Celtic | Premier League | 24 | 25 | — | 5 | 26% | 2.2 | 1.5 |
| Moroka Swallows | Premier League | 86 | 73 | — | 7 | 39% | 4.2 | 1.5 |
| Mamelodi Sundowns | Premier League | 151 | 276 | — | 11 | 62% | 4.9 | 1.8 |
| AmaZulu | Premier League | 141 | 139 | — | 8.2 | 44% | 4.3 | 1.9 |
| Orlando Pirates | Premier League | 148 | 204 | — | 11.7 | 54% | 5.8 | 1.9 |
| Baroka | Premier League | 57 | 48 | — | 4.3 | 25% | 2.3 | 1.7 |
| Polokwane City | Premier League | 87 | 65 | — | 11 | 44% | 4.2 | 1.5 |
| Chippa United | Premier League | 137 | 110 | — | 7.5 | 42% | 3.7 | 1.6 |
| Black Leopards | Premier League | 27 | 22 | — | 4.6 | 27% | 2.9 | 1.2 |
| Magesi | Premier League | 49 | 32 | — | 9.4 | 42% | 4 | 2.1 |
| Royal AM | Premier League | 69 | 74 | — | 7.3 | 45% | 3.6 | 1.7 |
| Richards Bay | Premier League | 86 | 61 | — | 10.7 | 44% | 4.7 | 1.6 |
| Marumo Gallants FC | Premier League | 111 | 85 | — | 8.2 | 39% | 3.2 | 1.7 |
| TS Galaxy | Premier League | 138 | 132 | — | 8.1 | 43% | 3.3 | 1.9 |
| Sekhukhune United | Premier League | 108 | 111 | — | 9.6 | 43% | 4 | 1.7 |
| Orbit College | Premier League | 21 | 16 | — | 8 | 41% | 3.6 | 2 |
| Durban City | Premier League | 21 | 19 | — | 10.8 | 47% | 4 | 1.4 |
| Siwelele | Premier League | 20 | 11 | — | 9.2 | 49% | 4.2 | 2.1 |