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 |
|---|---|---|---|---|---|---|---|---|
| Mladá Boleslav | Chance Liga | 39 | 55 | — | 12.1 | 55% | 4.9 | 1.9 |
| Slavia Praha | Chance Liga | 39 | 88 | — | 17.8 | 57% | 7.1 | 2.1 |
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.
| Slovácko | Chance Liga | 39 | 35 | — | 12.1 | 51% | 4.5 | 2.1 |
| Slovan Liberec | Chance Liga | 37 | 47 | — | 13.6 | 48% | 5 | 2.7 |
| Karviná | Chance Liga | 36 | 48 | — | 13.9 | 54% | 4.8 | 1.6 |
| Teplice | Chance Liga | 39 | 47 | — | 10.8 | 42% | 4 | 2.2 |
| Zlín | Chance Liga | 35 | 43 | — | 10.7 | 40% | 4 | 2.1 |
| Bohemians 1905 | Chance Liga | 36 | 34 | — | 12.1 | 46% | 5 | 2 |
| Sparta Praha | Chance Liga | 39 | 73 | — | 15.1 | 63% | 6.1 | 2.4 |
| Sigma Olomouc | Chance Liga | 38 | 47 | — | 11.9 | 50% | 4.7 | 2.3 |
| Baník Ostrava | Chance Liga | 39 | 37 | — | 13.3 | 52% | 5.6 | 2.2 |
| České Budějovice | Chance Liga | 4 | 1 | — | 10.3 | 50% | 3.5 | 1.3 |
| Dukla Praha | Chance Liga | 39 | 32 | — | 10.4 | 44% | 3.7 | 2.3 |
| Viktoria Plzeň | Chance Liga | 39 | 70 | — | 16.5 | 55% | 6.1 | 1.8 |
| Hradec Králové | Chance Liga | 39 | 57 | — | 10.9 | 45% | 4.1 | 1.6 |
| Jablonec | Chance Liga | 39 | 56 | — | 12.1 | 50% | 4.9 | 1.8 |
| Pardubice | Chance Liga | 36 | 45 | — | 10.3 | 47% | 4.4 | 2.3 |