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 |
|---|---|---|---|---|---|---|---|---|
| Dinamo Zagreb | 1. HNL | 208 | 416 | — | 15.8 | 58% | 6.1 | 1.7 |
| Hajduk Split | 1. HNL | 210 | 300 | — | 13.2 | 54% | 5.2 | 2 |
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.
| Slaven Koprivnica | 1. HNL | 192 | 205 | — | 11.3 | 46% | 4 | 2.5 |
| Rijeka | 1. HNL | 188 | 317 | — | 14.2 | 52% | 5.4 | 2.1 |
| Šibenik | 1. HNL | 107 | 105 | — | 10.8 | 45% | 4.2 | 2.3 |
| Istra 1961 | 1. HNL | 199 | 192 | — | 9.4 | 48% | 3.7 | 2.6 |
| Lokomotiva Zagreb | 1. HNL | 198 | 239 | — | 11.9 | 48% | 4.7 | 2.4 |
| Hrvatski Dragovoljac | 1. HNL | 36 | 31 | — | 9.5 | 39% | 3.3 | 2.4 |
| Vukovar | 1. HNL | 26 | 26 | — | 10.1 | 43% | 3.5 | 2.5 |
| Rudeš | 1. HNL | 36 | 22 | — | 9.6 | 45% | 3.7 | 2.1 |
| Varaždin | 1. HNL | 141 | 135 | — | 11.2 | 47% | 4.2 | 2.3 |
| Osijek | 1. HNL | 192 | 259 | — | 13.2 | 51% | 5.2 | 2.4 |
| Gorica | 1. HNL | 185 | 211 | — | 11.1 | 46% | 4.2 | 2.4 |