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 | 170 | 188 | — | 11.9 | 46% | 4.3 | 2.4 |
| Ufa | Premier League | 60 | 55 | — | 10.7 | 44% | 4.2 | 3.1 |
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.
| Krylya Sovetov | Premier League | 115 | 151 | — | 11.7 | 50% | 4.6 | 2.1 |
| Lokomotiv Moskva | Premier League | 181 | 270 | — | 13.2 | 52% | 5 | 2.5 |
| CSKA Moskva | Premier League | 170 | 249 | — | 14.4 | 53% | 5.4 | 1.9 |
| Dinamo Moskva | Premier League | 183 | 277 | — | 15.6 | 51% | 5.4 | 2.1 |
| Krasnodar | Premier League | 175 | 269 | — | 14.5 | 57% | 4.7 | 2.3 |
| Ural | Premier League | 100 | 90 | — | 11.3 | 49% | 4.6 | 2.4 |
| Spartak Moskva | Premier League | 171 | 258 | — | 14.9 | 57% | 5.5 | 2.5 |
| Zenit | Premier League | 165 | 343 | — | 15.1 | 60% | 6.1 | 1.6 |
| Orenburg | Premier League | 91 | 91 | — | 13.6 | 50% | 5.6 | 2.4 |
| Akhmat Grozny | Premier League | 157 | 166 | — | 13.1 | 46% | 4.8 | 2.6 |
| Arsenal Tula | Premier League | 80 | 79 | — | 11.5 | 47% | 4.1 | 2.9 |
| Sochi | Premier League | 117 | 164 | — | 12.4 | 49% | 4.8 | 2.4 |
| Baltika Kaliningrad | Premier League | 51 | 58 | — | 12.1 | 46% | 4.3 | 2.3 |
| Rotor Volgograd | Premier League | 30 | 15 | — | 9 | 44% | 3.7 | 3.1 |
| Tambov | Premier League | 30 | 19 | — | 8.3 | 39% | 2.7 | 2.5 |
| Rostov | Premier League | 155 | 196 | — | 13.3 | 51% | 4.7 | 2.3 |
| Khimki | Premier League | 90 | 104 | — | 11.2 | 46% | 4.5 | 2.6 |
| Fakel | Premier League | 60 | 36 | — | 10.5 | 43% | 4.8 | 2.3 |
| FK Nizjni Novgorod | Premier League | 115 | 105 | — | 9.8 | 41% | 3.7 | 2.4 |
| Akron | Premier League | 51 | 65 | — | 11.8 | 46% | 4.5 | 2 |
| Dynamo Makhachkala | Premier League | 51 | 37 | — | 10.4 | 46% | 4.4 | 2.3 |