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 |
|---|---|---|---|---|---|---|---|---|
| CSKA Moscow U19 | Youth League | 34 | 83 | — | 11.4 | 57% | 6.4 | 2.1 |
| Rostov U19 | Youth League | 34 | 53 | — | 7.4 | 53% | 5.8 | 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.
| Spartak Moskva U19 | Youth League | 34 | 58 | — | 9.9 | 55% | 5.9 | 1.4 |
| Krasnodar U19 | Youth League | 34 | 107 | — | 11.2 | 51% | 6 | 2 |
| Lokomotiv Moskva U19 | Youth League | 34 | 72 | — | 11.6 | 55% | 7 | 2.5 |
| Zenit U19 | Youth League | 34 | 103 | — | 13.9 | 53% | 7 | 1.8 |
| Akademiya Konoplev U20 | Youth League | 34 | 48 | — | 7.8 | 50% | 5.3 | 1.6 |
| Dinamo Moskva U19 | Youth League | 34 | 51 | — | 7.4 | 48% | 4.3 | 1.8 |
| Sochi U19 | Youth League | 24 | 23 | — | 6 | 49% | 4 | 2 |
| Akhmat Grozny U19 | Youth League | 23 | 21 | — | 6.1 | 45% | 3.5 | 2.8 |
| Rubin Kazan U19 | Youth League | 32 | 44 | — | 7 | 47% | 3.7 | 1.9 |
| Ural U19 | Youth League | 33 | 44 | — | 6.6 | 44% | 3.1 | 2.2 |
| Nizhny Novgorod U19 | Youth League | 34 | 44 | — | 7.2 | 50% | 4 | 1.7 |
| Krylya Sovetov U19 | Youth League | 25 | 33 | — | 9.7 | 49% | 4.8 | 2 |
| Fakel U19 | Youth League | 32 | 40 | — | 5.1 | 46% | 3 | 1.8 |
| Baltika U19 | Youth League | 23 | 15 | — | 5.3 | 43% | 4.4 | 1.5 |
| Dinamo Makhachkala U19 | Youth League | 12 | 20 | — | 6.9 | 46% | 4.3 | 2.2 |
| Rodina Moskva U19 | Youth League | 11 | 18 | — | 10.6 | 50% | 7.8 | 1.7 |
| Chertanovo U19 | Youth League | 10 | 18 | — | 9.2 | 49% | 5 | 2 |
| Almaz-Antey U19 | Youth League | 11 | 13 | — | 9.1 | 52% | 6.4 | 1.1 |