Aggregated team performance data including expected goals, shots, possession, corners, and discipline stats.
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.
| Brazil U23 | Olympic Games | 5 | 10 | — | 16.4 | 53% | 7.8 | 2 |
| Honduras U23 | Olympic Games | 3 | 3 | — | 15.3 | 47% | 3.7 | 2.3 |
| Korea Republic U23 | Olympic Games | 3 | 10 | — | 17.3 | 61% | 5.7 | 0.7 |
| Morocco U23 | Olympic Games | 6 | 13 | — | 13.2 | 56% | 6 | 2 |
| Spain U23 | Olympic Games | 12 | 23 | — | 15.4 | 63% | 5.7 | 2.7 |
| New Zealand U23 | Olympic Games | 7 | 6 | — | 8.1 | 47% | 3.9 | 1.9 |
| Germany U23 | Olympic Games | 3 | 6 | — | 12.7 | 47% | 4.7 | 3 |
| Iraq U23 | Olympic Games | 3 | 1 | — | 5.5 | 34% | 2.3 | 1.7 |
| Argentina U23 | Olympic Games | 7 | 8 | — | 12.3 | 55% | 5.4 | 3.4 |
| Uzbekistan U23 | Olympic Games | 3 | 1 | — | 13.3 | 51% | 6 | 1.7 |
| Australia U23 | Olympic Games | 3 | 2 | — | 8.7 | 40% | 2.7 | 4 |
| Saudi Arabia U23 | Olympic Games | 3 | 4 | — | 9.7 | 54% | 5.3 | 2 |
| United States U23 | Olympic Games | 4 | 7 | — | 11.5 | 44% | 4.3 | 1.3 |
| Mali U23 | Olympic Games | 3 | 1 | — | 16.3 | 59% | 5.7 | 1.7 |
| Côte d'Ivoire U23 | Olympic Games | 4 | 5 | — | 10 | 41% | 3.5 | 2.5 |
| Guinea U23 | Olympic Games | 3 | 1 | — | 15 | 55% | 6 | 1.3 |
| Paraguay U23 | Olympic Games | 4 | 6 | — | 19.3 | 37% | 4.8 | 3.5 |
| Dominican Republic U23 | Olympic Games | 3 | 2 | — | 10 | 44% | 3 | 1.3 |
| France U23 | Olympic Games | 8 | 17 | — | 19 | 53% | 9 | 2.4 |
| Romania U23 | Olympic Games | 3 | 1 | — | 8 | 43% | 2.7 | 3 |
| Ukraine U23 | Olympic Games | 3 | 2 | — | 12.5 | 46% | 3.7 | 3.7 |
| Israel U23 | Olympic Games | 3 | 3 | — | 11 | 49% | 2 | 1 |