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 |
|---|---|---|---|---|---|---|---|---|
| Boca Juniors | Liga Profesional de Fútbol | 165 | 209 | 0 | 11.8 | 57% | 5.1 | 2.6 |
| Tigre | Liga Profesional de Fútbol | 113 | 111 | 0 | 12.5 | 49% | 4.1 | 2.1 |
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.
| Aldosivi | Liga Profesional de Fútbol | 100 | 94 | 0 | 10.6 | 50% | 4.3 | 2.8 |
| Vélez Sarsfield | Liga Profesional de Fútbol | 164 | 178 | 0 | 11.5 | 54% | 4.7 | 2.4 |
| Banfield | Liga Profesional de Fútbol | 163 | 155 | — | 12.1 | 46% | 4.7 | 2.6 |
| Huracán | Liga Profesional de Fútbol | 157 | 141 | — | 11.1 | 49% | 4.5 | 2.6 |
| Rosario Central | Liga Profesional de Fútbol | 160 | 182 | — | 11.9 | 52% | 5 | 2.8 |
| Argentinos Juniors | Liga Profesional de Fútbol | 157 | 163 | — | 13.5 | 58% | 5.3 | 2.5 |
| Racing Club | Liga Profesional de Fútbol | 163 | 203 | — | 13.1 | 56% | 5.2 | 2.6 |
| Club Atletico Colón | Liga Profesional de Fútbol | 79 | 90 | — | 11.5 | 47% | 4.7 | 2.6 |
| Arsenal de Sarandí | Liga Profesional de Fútbol | 76 | 56 | — | 9.9 | 45% | 4.3 | 2.4 |
| Belgrano | Liga Profesional de Fútbol | 100 | 87 | — | 10.8 | 49% | 4.4 | 2.6 |
| Patronato | Liga Profesional de Fútbol | 49 | 40 | — | 11.3 | 44% | 4.6 | 2.6 |
| Unión Santa Fe | Liga Profesional de Fútbol | 151 | 148 | — | 13 | 49% | 4.9 | 2.3 |
| Barracas Central | Liga Profesional de Fútbol | 99 | 88 | — | 10 | 44% | 3.2 | 2.3 |
| Estudiantes | Liga Profesional de Fútbol | 160 | 186 | — | 13.4 | 52% | 4.7 | 2.6 |
| Defensa y Justicia | Liga Profesional de Fútbol | 147 | 177 | — | 12 | 53% | 4.3 | 2.3 |
| Lanús | Liga Profesional de Fútbol | 155 | 178 | — | 12.1 | 49% | 4.4 | 2.5 |
| Platense | Liga Profesional de Fútbol | 141 | 121 | — | 11.1 | 44% | 4.1 | 2.5 |
| Sarmiento | Liga Profesional de Fútbol | 138 | 99 | — | 10.9 | 42% | 4.2 | 2.8 |
| Instituto | Liga Profesional de Fútbol | 97 | 77 | — | 12 | 49% | 4.8 | 2.6 |
| Talleres Córdoba | Liga Profesional de Fútbol | 146 | 170 | — | 13.4 | 54% | 4.8 | 2.8 |
| River Plate | Liga Profesional de Fútbol | 155 | 241 | — | 15.5 | 63% | 5.9 | 2.6 |
| San Martín San Juan | Liga Profesional de Fútbol | 32 | 18 | — | 10.7 | 45% | 4.8 | 2.8 |
| Atlético Tucumán | Liga Profesional de Fútbol | 155 | 170 | — | 12.5 | 47% | 4.5 | 2.7 |
| Independiente | Liga Profesional de Fútbol | 155 | 161 | — | 11.4 | 52% | 4.6 | 2.8 |
| Godoy Cruz | Liga Profesional de Fútbol | 135 | 130 | — | 12.7 | 50% | 4.8 | 2.6 |
| Estudiantes de Río Cuarto | Liga Profesional de Fútbol | 10 | 3 | — | 10.7 | 45% | 4.3 | 2.6 |
| Central Cordoba SdE | Liga Profesional de Fútbol | 148 | 141 | — | 11.7 | 47% | 4.3 | 2.2 |
| Gimnasia y Esgrima Mendoza | Liga Profesional de Fútbol | 9 | 5 | — | 10.3 | 44% | 3.3 | 1.9 |
| Newell's Old Boys | Liga Profesional de Fútbol | 146 | 133 | — | 11.1 | 50% | 4.4 | 2.8 |
| Deportivo Riestra | Liga Profesional de Fútbol | 70 | 48 | — | 9 | 33% | 3.3 | 3.1 |
| Gimnasia La Plata | Liga Profesional de Fútbol | 154 | 138 | — | 11.2 | 47% | 4.8 | 2.7 |
| San Lorenzo | Liga Profesional de Fútbol | 155 | 130 | — | 10.9 | 49% | 4.6 | 2.8 |
| Independiente Rivadavia | Liga Profesional de Fútbol | 68 | 67 | — | 10.9 | 45% | 4 | 2.7 |