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 |
|---|---|---|---|---|---|---|---|---|
| Lokomotiv Plovdiv | First League | 44 | 47 | — | 9.8 | 45% | 3.7 | 2.6 |
| CSKA Sofia | First League | 41 | 64 | — | 15.2 | 58% | 5.8 | 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.
| Botev Plovdiv | First League | 41 | 56 | — | 12.3 | 50% | 4.9 | 2.3 |
| Montana | First League | 37 | 21 | — | 8.8 | 44% | 3.9 | 2.1 |
| Slavia Sofia | First League | 43 | 46 | — | 11.1 | 48% | 4.9 | 1.8 |
| Ludogorets | First League | 42 | 64 | — | 14.5 | 60% | 6.8 | 1.8 |
| Levski Sofia | First League | 41 | 79 | — | 14.5 | 61% | 6.1 | 2.2 |
| Botev Vratsa | First League | 44 | 46 | — | 9.5 | 45% | 3.5 | 2.2 |
| Spartak Varna | First League | 42 | 41 | — | 9.2 | 43% | 3.7 | 2.6 |
| Cherno More | First League | 41 | 43 | — | 10 | 52% | 4.5 | 2.2 |
| Beroe | First League | 42 | 34 | — | 9.8 | 45% | 4 | 2.4 |
| Lokomotiv Sofia 1929 | First League | 43 | 62 | — | 10.8 | 50% | 4.9 | 2.4 |
| Septemvri Sofia | First League | 45 | 44 | — | 9.7 | 49% | 3.6 | 2.4 |
| Dobrudzha 1919 | First League | 37 | 28 | — | 10.2 | 49% | 5 | 2.6 |
| Arda | First League | 41 | 46 | — | 10.4 | 52% | 4.1 | 2.3 |
| Hebar 1918 | First League | 6 | 3 | — | 9.2 | 47% | 3.5 | 2.2 |
| CSKA 1948 Sofia | First League | 43 | 61 | — | 11.7 | 52% | 5.3 | 2.2 |
| Levski Krumovgrad | First League | 6 | 4 | — | 8.5 | 46% | 2.8 | 2.2 |