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 |
|---|---|---|---|---|---|---|---|---|
| Widzew Lodz | Ekstraklasa | 97 | 114 | 0 | 13 | 50% | 4.8 | 2.4 |
| Piast Gliwice | Ekstraklasa | 189 | 207 | 0 | 11.9 | 50% | 4.8 | 1.8 |
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.
| Wisła Płock | Ekstraklasa | 127 | 160 | 0 | 10.3 | 48% | 5 | 2.2 |
| Raków Częstochowa | Ekstraklasa | 167 | 253 | 0 | 13.3 | 52% | 5.6 | 2.2 |
| Wisła Kraków | Ekstraklasa | 64 | 76 | — | 10.9 | 51% | 5.6 | 2.4 |
| Legia Warszawa | Ekstraklasa | 167 | 238 | — | 13.8 | 55% | 6 | 2.4 |
| Pogoń Szczecin | Ekstraklasa | 161 | 245 | — | 14.1 | 55% | 6.1 | 2.1 |
| Jagiellonia Białystok | Ekstraklasa | 191 | 280 | — | 12.4 | 52% | 5 | 2.1 |
| Cracovia Kraków | Ekstraklasa | 175 | 218 | — | 12 | 47% | 4.6 | 2.2 |
| Arka Gdynia | Ekstraklasa | 36 | 36 | — | 9.9 | 47% | 4.8 | 2.5 |
| Zagłębie Lubin | Ekstraklasa | 185 | 226 | — | 12.4 | 47% | 4.9 | 2.1 |
| Nieciecza | Ekstraklasa | 66 | 68 | — | 12.4 | 48% | 4.6 | 2.2 |
| Ruch Chorzów | Ekstraklasa | 34 | 40 | — | 12.7 | 47% | 4.4 | 2.3 |
| Podbeskidzie | Ekstraklasa | 30 | 29 | — | 7.2 | 48% | 4.6 | 2 |
| Katowice | Ekstraklasa | 58 | 77 | — | 13.6 | 47% | 4.6 | 1.6 |
| Radomiak Radom | Ekstraklasa | 128 | 166 | — | 14.1 | 49% | 5.2 | 2.5 |
| Korona Kielce | Ekstraklasa | 100 | 115 | — | 13.4 | 48% | 5 | 2.1 |
| Górnik Zabrze | Ekstraklasa | 162 | 213 | — | 13 | 51% | 5.3 | 2 |
| Śląsk Wrocław | Ekstraklasa | 132 | 165 | — | 12.4 | 47% | 4.4 | 2.1 |
| Stal Mielec | Ekstraklasa | 132 | 147 | — | 10.7 | 47% | 4.4 | 2 |
| Motor Lublin | Ekstraklasa | 59 | 82 | — | 12.5 | 49% | 5.1 | 2.2 |
| Puszcza Niepołomice | Ekstraklasa | 68 | 72 | — | 11 | 39% | 4.1 | 2.7 |
| Lechia Gdańsk | Ekstraklasa | 141 | 208 | — | 11.7 | 48% | 4.9 | 2.2 |
| ŁKS Łódź | Ekstraklasa | 34 | 34 | — | 11.4 | 49% | 4.1 | 2.6 |
| Warta Poznań | Ekstraklasa | 98 | 101 | — | 9.2 | 43% | 4.1 | 1.9 |
| Górnik Łęczna | Ekstraklasa | 34 | 29 | — | 10 | 45% | 4.1 | 2.3 |
| Lech Poznań | Ekstraklasa | 161 | 267 | — | 14.5 | 58% | 6 | 1.8 |