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 |
|---|---|---|---|---|---|---|---|---|
| Athletic Club | La Liga | 252 | 304 | 0 | 12.4 | 49% | 5.5 | 2.2 |
| Real Sociedad | La Liga | 242 | 301 | 0 | 11.5 | 54% | 5 | 2.2 |
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.
| Almería | La Liga | 77 | 92 | 0 | 12.2 | 45% | 4.2 | 2.5 |
| Mallorca | La Liga | 185 | 181 | 0 | 10.4 | 44% | 4 | 2.7 |
| Sevilla | La Liga | 241 | 298 | 0 | 12.2 | 53% | 4.8 | 2.7 |
| Elche | La Liga | 149 | 140 | 0 | 9.3 | 49% | 4.2 | 2.6 |
| Real Madrid | La Liga | 248 | 491 | 0 | 16.4 | 60% | 6.1 | 1.7 |
| Villarreal | La Liga | 231 | 381 | 0 | 12.6 | 52% | 4.9 | 2.3 |
| Cádiz | La Liga | 150 | 127 | 0 | 10 | 40% | 4.3 | 2.7 |
| Atlético Madrid | La Liga | 258 | 417 | 0 | 12.4 | 51% | 5.1 | 2.4 |
| Celta de Vigo | La Liga | 243 | 306 | 0 | 11.2 | 51% | 4.3 | 2.3 |
| FC Barcelona | La Liga | 264 | 550 | 0 | 16 | 66% | 6.2 | 2 |
| Getafe | La Liga | 251 | 206 | 0 | 10.4 | 42% | 3.8 | 3.1 |
| Valencia | La Liga | 226 | 269 | 0 | 10.9 | 47% | 4.7 | 2.4 |
| Girona | La Liga | 150 | 227 | 0 | 11.4 | 54% | 4.2 | 2.2 |
| Real Valladolid | La Liga | 120 | 98 | 0 | 10.3 | 46% | 4.1 | 2.5 |
| Rayo Vallecano | La Liga | 190 | 183 | 0 | 13.4 | 51% | 5 | 2.7 |
| Osasuna | La Liga | 220 | 242 | 0 | 11.1 | 46% | 4.1 | 2.3 |
| Real Betis | La Liga | 239 | 325 | 0 | 12.8 | 52% | 4.8 | 2.4 |
| Espanyol | La Liga | 146 | 173 | 0 | 10.9 | 43% | 4.3 | 2.6 |
| SD Eibar | La Liga | 46 | 40 | — | 11.6 | 50% | 5.2 | 1.9 |
| Leganés | La Liga | 50 | 51 | — | 9.4 | 43% | 3.1 | 2.4 |
| Real Oviedo | La Liga | 28 | 18 | — | 9.9 | 44% | 3.9 | 2.5 |
| Granada | La Liga | 120 | 132 | — | 10.4 | 44% | 3.9 | 2.7 |
| Deportivo La Coruña | La Liga | 6 | 1 | — | 13.3 | 50% | 5.7 | 2.7 |
| Málaga | La Liga | 3 | 0 | — | 10 | 50% | 5.3 | 0 |
| Levante | La Liga | 108 | 132 | — | 11 | 47% | 4.3 | 2.5 |
| Las Palmas | La Liga | 76 | 73 | — | 10.6 | 55% | 4.2 | 2.6 |
| Huesca | La Liga | 38 | 33 | — | 10.7 | 48% | 4 | 1.9 |
| Deportivo Alavés | La Liga | 196 | 189 | — | 10.8 | 44% | 4.3 | 2.6 |