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 |
|---|---|---|---|---|---|---|---|---|
| Juventude | Serie A | 32 | 28 | — | 10.9 | 43% | 4.3 | 3.2 |
| Corinthians | Serie A | 50 | 54 | — | 11.8 | 55% | 4.8 | 2.9 |
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.
| Bahia | Serie A | 49 | 69 | — | 14.1 | 55% | 5.1 | 2.5 |
| Vasco da Gama | Serie A | 50 | 71 | — | 14.7 | 55% | 4.7 | 2.2 |
| Chapecoense | Serie A | 17 | 17 | — | 12.8 | 43% | 3.6 | 2.4 |
| Flamengo | Serie A | 49 | 94 | — | 15.2 | 60% | 5.5 | 2.6 |
| Fluminense | Serie A | 50 | 72 | — | 13.2 | 55% | 4.8 | 2.3 |
| Sport Recife | Serie A | 32 | 25 | — | 12.1 | 46% | 5.5 | 2.7 |
| Internacional | Serie A | 50 | 57 | — | 14.3 | 51% | 5.7 | 2.5 |
| Botafogo | Serie A | 49 | 83 | — | 13.6 | 50% | 4.4 | 2.8 |
| Grêmio | Serie A | 50 | 62 | — | 12 | 47% | 3.9 | 2.7 |
| Cruzeiro | Serie A | 50 | 72 | — | 14 | 51% | 5.8 | 2.7 |
| Palmeiras | Serie A | 50 | 89 | — | 15 | 52% | 6.3 | 2.1 |
| Atlético Mineiro | Serie A | 50 | 59 | — | 13.7 | 51% | 5 | 2.4 |
| Athletico PR | Serie A | 18 | 24 | — | 12.2 | 49% | 5.2 | 2.3 |
| Vitória | Serie A | 49 | 49 | — | 11.5 | 42% | 4.2 | 3.1 |
| São Paulo | Serie A | 50 | 60 | — | 11.8 | 53% | 5.5 | 2.3 |
| Fortaleza | Serie A | 32 | 38 | — | 13.7 | 44% | 5.8 | 3.1 |
| Santos | Serie A | 50 | 64 | — | 12.7 | 49% | 4.9 | 2.9 |
| Coritiba | Serie A | 18 | 24 | — | 9.7 | 39% | 3.2 | 2.1 |
| Bragantino | Serie A | 50 | 62 | — | 13.3 | 49% | 4.8 | 3.2 |
| Mirassol | Serie A | 49 | 70 | — | 13.6 | 51% | 5.5 | 2.4 |
| Remo | Serie A | 18 | 21 | — | 12.1 | 43% | 4.4 | 2.2 |
| Ceará | Serie A | 32 | 26 | — | 12.2 | 43% | 4.7 | 2.3 |