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 |
|---|---|---|---|---|---|---|---|---|
| Dukla Praha | Chance Liga | 70 | 61 | 0 | 9.8 | 44% | 4 | 2.2 |
| Zlín | Chance Liga | 148 | 163 | 0 | 9.8 | 33% | 3.9 | 2.3 |
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.
| Slovácko | Chance Liga | 179 | 226 | — | 10.3 | 39% | 4.6 | 2.2 |
| Příbram | Chance Liga | 33 | 26 | — | 5 | 11% | 4.2 | 2 |
| Slovan Liberec | Chance Liga | 165 | 220 | — | 11.4 | 38% | 4.7 | 2.3 |
| Karviná | Chance Liga | 168 | 194 | — | 10.7 | 39% | 4.4 | 2.2 |
| Teplice | Chance Liga | 172 | 182 | — | 9.8 | 35% | 4.4 | 2.4 |
| Bohemians 1905 | Chance Liga | 176 | 191 | — | 10.5 | 37% | 5.2 | 2.1 |
| Sparta Praha | Chance Liga | 185 | 377 | — | 13.4 | 46% | 5.6 | 2.2 |
| Sigma Olomouc | Chance Liga | 172 | 224 | — | 9.3 | 39% | 4.8 | 2.2 |
| Baník Ostrava | Chance Liga | 176 | 263 | — | 12.4 | 42% | 5.6 | 2.1 |
| Zbrojovka Brno | Chance Liga | 34 | 33 | — | 6.8 | 5% | 5.5 | 2 |
| České Budějovice | Chance Liga | 135 | 131 | — | 9.7 | 35% | 4.8 | 2 |
| Viktoria Plzeň | Chance Liga | 179 | 338 | — | 12.7 | 43% | 6.2 | 1.9 |
| Hradec Králové | Chance Liga | 136 | 167 | — | 10.7 | 43% | 5.1 | 2.2 |
| Opava | Chance Liga | 34 | 23 | — | 6.2 | 13% | 4 | 2.9 |
| Jablonec | Chance Liga | 176 | 242 | — | 10.8 | 40% | 5.3 | 2.1 |
| Mladá Boleslav | Chance Liga | 170 | 246 | — | 11.1 | 40% | 5.4 | 2 |
| Pardubice | Chance Liga | 167 | 179 | — | 9 | 37% | 4.3 | 2.1 |
| Slavia Praha | Chance Liga | 183 | 412 | — | 14.6 | 47% | 6.8 | 2 |