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 |
|---|---|---|---|---|---|---|---|---|
| Otelul | Superliga | 42 | 60 | — | 13.7 | 53% | 4.5 | 2 |
| Dinamo Bucureşti | Superliga | 46 | 60 | — | 14.3 | 59% | 5.6 | 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.
| Botoşani | Superliga | 43 | 56 | — | 13.6 | 54% | 4.7 | 2.2 |
| FCSB | Superliga | 45 | 74 | — | 16.8 | 59% | 6.4 | 2.2 |
| CFR Cluj | Superliga | 45 | 66 | — | 14 | 46% | 4.7 | 2 |
| CSM Iaşi | Superliga | 3 | 0 | — | 14.7 | 51% | 4.3 | 1.7 |
| Rapid Bucuresti | Superliga | 44 | 60 | — | 13.1 | 52% | 5 | 2.1 |
| Unirea Slobozia | Superliga | 42 | 44 | — | 10.7 | 42% | 3.6 | 2.1 |
| UTA Arad | Superliga | 42 | 57 | — | 14 | 46% | 4.9 | 2.1 |
| Universitatea Cluj | Superliga | 44 | 66 | — | 12 | 52% | 4.9 | 2.1 |
| Sepsi | Superliga | 3 | 3 | — | 8.7 | 50% | 3.7 | 3.7 |
| Argeş | Superliga | 40 | 43 | — | 10.8 | 43% | 4.7 | 2.6 |
| Csikszereda | Superliga | 39 | 41 | — | 11.2 | 41% | 2.9 | 2.2 |
| Universitatea Craiova | Superliga | 44 | 70 | — | 14.7 | 58% | 5.2 | 2.2 |
| Metaloglobus | Superliga | 39 | 35 | — | 10.5 | 46% | 4.4 | 2.4 |
| Hermannstadt | Superliga | 42 | 46 | — | 12.1 | 46% | 5 | 2 |
| Petrolul 52 | Superliga | 43 | 36 | — | 12.1 | 47% | 4.8 | 2.3 |
| Farul Constanța | Superliga | 43 | 50 | — | 14.2 | 53% | 5.6 | 2 |
| SCM Gloria Buzau | Superliga | 3 | 0 | — | 7.3 | 43% | 4.7 | 2.3 |