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 |
|---|---|---|---|---|---|---|---|---|
| Sporting CP | Liga Portugal | 189 | 420 | — | 16.2 | 59% | 6.2 | 2.5 |
| Gil Vicente | Liga Portugal | 169 | 195 | — | 11.3 | 50% | 4.8 | 2.4 |
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.
| Belenenses | Liga Portugal | 68 | 48 | — | 9.6 | 45% | 4.1 | 3.3 |
| Benfica | Liga Portugal | 180 | 378 | — | 16 | 60% | 6.8 | 2.1 |
| Porto | Liga Portugal | 165 | 316 | — | 15.4 | 61% | 6.2 | 2.5 |
| Vitória Guimarães | Liga Portugal | 172 | 236 | — | 12.9 | 51% | 5.7 | 2.8 |
| Sporting Braga | Liga Portugal | 177 | 294 | — | 13.9 | 56% | 5.5 | 2.3 |
| Boavista | Liga Portugal | 136 | 140 | — | 10.8 | 44% | 4.1 | 3.2 |
| Chaves | Liga Portugal | 34 | 31 | — | 11.2 | 43% | 4.5 | 3.1 |
| Moreirense | Liga Portugal | 170 | 187 | — | 10.6 | 47% | 4 | 2.7 |
| Paços de Ferreira | Liga Portugal | 68 | 67 | — | 10.8 | 47% | 4.8 | 2.7 |
| Estoril | Liga Portugal | 136 | 185 | — | 10.9 | 49% | 4.4 | 2.6 |
| Portimonense SAD | Liga Portugal | 102 | 104 | — | 11.3 | 48% | 4.3 | 2.9 |
| Santa Clara | Liga Portugal | 134 | 145 | — | 11.1 | 47% | 4.4 | 3.3 |
| Famalicão | Liga Portugal | 168 | 204 | — | 12.1 | 50% | 4.8 | 2.9 |
| Tondela | Liga Portugal | 113 | 121 | — | 9.5 | 44% | 3.8 | 2.8 |
| Arouca | Liga Portugal | 140 | 153 | — | 11.9 | 51% | 4.4 | 2.8 |
| Marítimo | Liga Portugal | 68 | 68 | — | 10.3 | 47% | 4.1 | 3.2 |
| Rio Ave | Liga Portugal | 138 | 144 | — | 10.7 | 49% | 4.3 | 2.6 |
| Nacional | Liga Portugal | 108 | 94 | — | 12.3 | 48% | 4.6 | 2.8 |
| Farense | Liga Portugal | 102 | 101 | — | 11.3 | 42% | 5 | 2.6 |
| Vizela | Liga Portugal | 68 | 73 | — | 12.8 | 47% | 5.1 | 2.8 |
| Casa Pia | Liga Portugal | 95 | 105 | — | 9.6 | 45% | 4.1 | 2.7 |
| Estrela Amadora | Liga Portugal | 94 | 86 | — | 10.4 | 47% | 4.2 | 2.7 |
| Alverca | Liga Portugal | 26 | 26 | — | 9.7 | 45% | 4.1 | 2.5 |
| AVS | Liga Portugal | 60 | 43 | — | 10.4 | 42% | 3.5 | 2.9 |