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 | 114 | 119 | — | 11.6 | 45% | 4.1 | 2.9 |
| Corinthians | Serie A | 187 | 213 | — | 12.2 | 53% | 5.2 | 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.
| Bahia | Serie A | 172 | 211 | — | 13.4 | 52% | 5.1 | 2.4 |
| Vasco da Gama | Serie A | 130 | 151 | — | 12.4 | 51% | 4.7 | 2.5 |
| Chapecoense | Serie A | 55 | 45 | — | 10.7 | 42% | 4.5 | 1.9 |
| Flamengo | Serie A | 186 | 303 | — | 14.5 | 58% | 5.8 | 2.1 |
| Fluminense | Serie A | 175 | 197 | — | 12.6 | 54% | 4.8 | 2.6 |
| Sport Recife | Serie A | 76 | 52 | — | 12.3 | 46% | 4.9 | 2.6 |
| Internacional | Serie A | 174 | 218 | — | 13.1 | 51% | 5 | 2.7 |
| Botafogo | Serie A | 133 | 193 | — | 14 | 50% | 4.9 | 2.5 |
| Grêmio | Serie A | 160 | 210 | — | 13 | 48% | 5.2 | 2.7 |
| Cruzeiro | Serie A | 134 | 145 | — | 13.4 | 51% | 5.8 | 2.6 |
| Palmeiras | Serie A | 168 | 274 | — | 15.6 | 52% | 6.1 | 2.3 |
| Atlético Mineiro | Serie A | 176 | 230 | — | 13.8 | 55% | 5.6 | 2.6 |
| Athletico PR | Serie A | 135 | 156 | — | 14.2 | 48% | 5.5 | 2.4 |
| Vitória | Serie A | 98 | 100 | — | 11.9 | 43% | 4.6 | 2.9 |
| São Paulo | Serie A | 170 | 190 | — | 12.4 | 56% | 5.3 | 2.5 |
| Fortaleza | Serie A | 152 | 185 | — | 13.4 | 46% | 5 | 2.7 |
| Santos | Serie A | 137 | 137 | — | 12.6 | 49% | 4.9 | 2.5 |
| Cuiabá | Serie A | 114 | 103 | — | 10.9 | 44% | 4.1 | 2.5 |
| Criciúma | Serie A | 38 | 42 | — | 13.2 | 43% | 4.7 | 2.7 |
| Coritiba | Serie A | 57 | 56 | — | 10.9 | 43% | 4.4 | 2.7 |
| América Mineiro | Serie A | 76 | 83 | — | 14.4 | 46% | 4.8 | 2.1 |
| Bragantino | Serie A | 160 | 200 | — | 14 | 51% | 5.4 | 2.9 |
| Atlético GO | Serie A | 76 | 62 | — | 12.4 | 48% | 5 | 2.8 |
| Goiás | Serie A | 36 | 34 | — | 12.9 | 45% | 5.3 | 2.9 |
| Mirassol | Serie A | 43 | 71 | — | 14 | 52% | 5.3 | 2.4 |
| Remo | Serie A | 6 | 6 | — | 14.2 | 45% | 4.8 | 3 |
| Ceará | Serie A | 76 | 73 | — | 12.5 | 45% | 4.8 | 2.4 |