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 |
|---|---|---|---|---|---|---|---|---|
| Rubin Kazan | Premier League | 34 | 37 | — | 10.9 | 44% | 3.9 | 2.3 |
| Krylya Sovetov | Premier League | 34 | 39 | — | 9.1 | 47% | 3.7 | 2.7 |
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.
| Lokomotiv Moskva | Premier League | 34 | 62 | — | 13.5 | 51% | 4.3 | 2.8 |
| CSKA Moskva | Premier League | 34 | 50 | — | 14.4 | 57% | 5.4 | 1.8 |
| Dinamo Moskva | Premier League | 34 | 57 | — | 15.2 | 53% | 5.5 | 2 |
| Krasnodar | Premier League | 34 | 60 | — | 15.4 | 59% | 4.8 | 2.3 |
| Spartak Moskva | Premier League | 34 | 52 | — | 14.1 | 62% | 5.4 | 2.2 |
| Zenit | Premier League | 34 | 60 | — | 13.6 | 59% | 6 | 1.6 |
| Orenburg | Premier League | 34 | 34 | — | 13.2 | 47% | 5.6 | 2.6 |
| Akhmat Grozny | Premier League | 34 | 39 | — | 12.1 | 46% | 4.7 | 2.1 |
| Sochi | Premier League | 30 | 28 | — | 8.7 | 46% | 3.6 | 2.4 |
| Baltika Kaliningrad | Premier League | 30 | 33 | — | 12.7 | 46% | 3.5 | 3.1 |
| Rostov | Premier League | 34 | 27 | — | 11.6 | 49% | 4.2 | 2.6 |
| Khimki | Premier League | 4 | 5 | — | 13 | 47% | 7.8 | 2.3 |
| Fakel | Premier League | 4 | 2 | — | 13.3 | 50% | 7.3 | 2.8 |
| FK Nizjni Novgorod | Premier League | 34 | 30 | — | 11.3 | 42% | 4.4 | 2.4 |
| Akron | Premier League | 34 | 41 | — | 11.5 | 45% | 4.3 | 2.1 |
| Dynamo Makhachkala | Premier League | 34 | 20 | — | 10.1 | 47% | 4.4 | 2.1 |