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 | 136 | 318 | — | 7.6 | 48% | 6.2 | 2.2 |
| Rostov U19 | Youth League | 136 | 268 | — | 5.9 | 43% | 5.9 | 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 | 136 | 236 | — | 6.3 | 46% | 6.3 | 1.7 |
| Krasnodar U19 | Youth League | 134 | 295 | — | 7.3 | 43% | 6.4 | 2 |
| Lokomotiv Moskva U19 | Youth League | 134 | 280 | — | 6.5 | 50% | 6.1 | 2 |
| Zenit U19 | Youth League | 134 | 351 | — | 7.2 | 48% | 6.6 | 1.9 |
| Chertanovo U20 | Youth League | 102 | 169 | — | 5 | 41% | 5.6 | 1.9 |
| Rotor Volgograd U20 | Youth League | 27 | 45 | — | 8 | 45% | 3.8 | 2.3 |
| Khimki U20 | Youth League | 55 | 86 | — | 5.1 | 31% | 4.6 | 2.2 |
| Akademiya Konoplev U20 | Youth League | 132 | 172 | — | 4.9 | 38% | 4.5 | 1.7 |
| UOR №5 U20 | Youth League | 102 | 141 | — | 4 | 36% | 4.7 | 2 |
| Strogino U20 | Youth League | 83 | 138 | — | 5.9 | 35% | 5.8 | 2.4 |
| Arsenal Tula U19 | Youth League | 53 | 96 | — | 6.3 | 33% | 5.3 | 2.2 |
| Dinamo Moskva U19 | Youth League | 135 | 234 | — | 5.8 | 43% | 5.2 | 2.1 |
| Tambov U19 | Youth League | 27 | 33 | — | — | 20% | 3.9 | 1.6 |
| Sochi U19 | Youth League | 132 | 170 | — | 5.2 | 41% | 4.8 | 2.2 |
| Akhmat Grozny U19 | Youth League | 132 | 155 | — | 4.6 | 39% | 4.5 | 2.2 |
| Rubin Kazan U19 | Youth League | 132 | 179 | — | 4.8 | 45% | 4.4 | 1.9 |
| Ural U19 | Youth League | 133 | 164 | — | 5 | 39% | 3.8 | 2.1 |
| Ufa U19 | Youth League | 55 | 68 | — | 5.1 | 35% | 4.7 | 2.3 |
| Nizhny Novgorod U19 | Youth League | 108 | 129 | — | 3.7 | 38% | 4.4 | 2 |
| Krylya Sovetov U19 | Youth League | 107 | 160 | — | 5.7 | 40% | 4.8 | 2 |
| Fakel U19 | Youth League | 77 | 105 | — | 4.2 | 47% | 3.7 | 1.7 |
| Orenburg U19 | Youth League | 47 | 41 | — | 2.9 | 41% | 3.6 | 2.5 |
| Baltika U19 | Youth League | 77 | 97 | — | 4.5 | 43% | 3.9 | 1.5 |
| FShM U19 | Youth League | 19 | 24 | — | 0 | 45% | 5 | 1.8 |
| Dinamo Makhachkala U19 | Youth League | 3 | 4 | — | — | 48% | 8 | 1.7 |