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 |
|---|---|---|---|---|---|---|---|---|
| Dynamo Kyiv | Premier League | 121 | 265 | — | 13.1 | 56% | 6.4 | 1.8 |
| Shakhtar Donetsk | Premier League | 144 | 314 | — | 13.7 | 61% | 5.9 | 1.7 |
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.
| Oleksandria | Premier League | 142 | 160 | — | 9.2 | 48% | 4.3 | 2.4 |
| Veres | Premier League | 98 | 95 | — | 8.9 | 43% | 4.1 | 2.2 |
| Inhulets | Premier League | 71 | 57 | — | 7.4 | 45% | 3.7 | 2.4 |
| Chornomorets | Premier League | 77 | 82 | — | 9.1 | 49% | 3.6 | 2.1 |
| Zorya Luhansk | Premier League | 130 | 179 | — | 10.8 | 50% | 4.8 | 2.3 |
| Kryvbas Kryvyi Rih | Premier League | 80 | 115 | — | 11.6 | 51% | 5.1 | 1.9 |
| Desna | Premier League | 42 | 57 | — | 9.1 | 52% | 5.9 | 2.5 |
| Mariupol | Premier League | 44 | 52 | — | 7 | 46% | 4.2 | 3 |
| Karpaty | Premier League | 49 | 67 | — | 12.1 | 52% | 4.4 | 2 |
| Obolon'-Brovar | Premier League | 79 | 50 | — | 10.1 | 44% | 4.3 | 2.1 |
| Rukh Vynnyky | Premier League | 125 | 133 | — | 9.3 | 49% | 4.4 | 2.3 |
| Kolos Kovalivka | Premier League | 128 | 110 | — | 9.2 | 48% | 4.6 | 2.4 |
| Olimpik Donetsk | Premier League | 24 | 28 | — | 8 | 50% | 5.7 | 2.7 |
| Vorskla | Premier League | 103 | 119 | — | 8.5 | 51% | 4.4 | 2.5 |
| Lviv | Premier League | 42 | 39 | — | 8.1 | 43% | 4.5 | 3.3 |
| Dnipro-1 | Premier League | 73 | 111 | — | 10.4 | 53% | 5.3 | 2.5 |
| Metalist 1925 Kharkiv | Premier League | 72 | 72 | — | 10.2 | 50% | 4.3 | 2.1 |
| Polissya Zhytomyr | Premier League | 80 | 111 | — | 11.8 | 52% | 5.3 | 2.3 |
| Minaj | Premier League | 74 | 63 | — | 7.5 | 45% | 3.8 | 2.7 |
| Epitsentr Dunayivtsi | Premier League | 20 | 24 | — | 11.3 | 48% | 4.5 | 1.7 |
| Livyi Bereh | Premier League | 30 | 18 | — | 6.6 | 43% | 3.9 | 2.2 |
| LNZ Cherkasy | Premier League | 80 | 84 | — | 10.8 | 48% | 4.9 | 2.5 |
| SK Poltava | Premier League | 20 | 16 | — | 8.2 | 43% | 3.8 | 1.9 |
| Kudrivka | Premier League | 20 | 24 | — | 9.7 | 50% | 4.1 | 1.9 |