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 |
|---|---|---|---|---|---|---|---|---|
| Brazil U23 | CONMEBOL Pre-Olympic Tournament | 7 | 8 | — | 6.6 | 54% | 3.8 | 2.1 |
| Uruguay U23 | CONMEBOL Pre-Olympic Tournament | 4 | 9 | — | 13.3 | 59% | 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.
| 1.5 |
| Colombia U23 | CONMEBOL Pre-Olympic Tournament | 4 | 0 | — | 4.3 | 51% | 5 | 1.5 |
| Argentina U23 | CONMEBOL Pre-Olympic Tournament | 7 | 17 | — | 8.4 | 58% | 7.3 | 1 |
| Chile U23 | CONMEBOL Pre-Olympic Tournament | 4 | 3 | — | 6.8 | 50% | 7 | 0.8 |
| Venezuela U23 | CONMEBOL Pre-Olympic Tournament | 7 | 11 | — | 3.9 | 46% | 2 | 0.9 |
| Peru U23 | CONMEBOL Pre-Olympic Tournament | 4 | 1 | — | 3.3 | 42% | 1 | 2.8 |
| Bolivia U23 | CONMEBOL Pre-Olympic Tournament | 4 | 5 | — | 6.3 | 49% | 6 | 1.3 |
| Ecuador U23 | CONMEBOL Pre-Olympic Tournament | 4 | 7 | — | 4.8 | 47% | 1 | 1.3 |
| Paraguay U23 | CONMEBOL Pre-Olympic Tournament | 7 | 13 | — | 10 | 44% | 5 | 2.1 |