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 |
|---|---|---|---|---|---|---|---|---|
| OFK Pirin | First League | 66 | 63 | — | 7.4 | 45% | 3.3 | 2.9 |
| Lokomotiv Plovdiv | First League | 203 | 234 | — | 8.6 | 49% | 4 | 2.9 |
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.
| CSKA Sofia | First League | 186 | 269 | — | 13.1 | 56% | 6.1 | 2.6 |
| Botev Plovdiv | First League | 175 | 220 | — | 10.9 | 50% | 5 | 2.5 |
| Montana | First League | 64 | 40 | — | 7.8 | 44% | 4.1 | 2.5 |
| Slavia Sofia | First League | 179 | 197 | — | 9.1 | 50% | 5 | 2.5 |
| Ludogorets | First League | 218 | 434 | — | 12.5 | 57% | 5.7 | 2.2 |
| Levski Sofia | First League | 213 | 292 | — | 12 | 55% | 5.7 | 2.3 |
| Etar | First League | 66 | 47 | — | 7.5 | 44% | 3.8 | 2.9 |
| Botev Vratsa | First League | 179 | 160 | — | 8 | 44% | 3.6 | 2.8 |
| Spartak Varna | First League | 66 | 73 | — | 9.1 | 44% | 4 | 2.6 |
| Cherno More | First League | 177 | 224 | — | 11.1 | 52% | 5 | 2.4 |
| Beroe | First League | 189 | 194 | — | 9.1 | 47% | 4.4 | 2.7 |
| Lokomotiv Sofia 1929 | First League | 135 | 135 | — | 9.3 | 46% | 4.2 | 2.7 |
| Tsarsko selo | First League | 62 | 52 | — | 7.4 | 49% | 4.2 | 2.8 |
| Septemvri Sofia | First League | 86 | 95 | — | 8.1 | 47% | 3.5 | 2.6 |
| Dobrudzha 1919 | First League | 26 | 21 | — | 10.2 | 48% | 4.7 | 2.9 |
| Arda | First League | 170 | 206 | — | 9.5 | 51% | 4.5 | 2.6 |
| Hebar 1918 | First League | 72 | 63 | — | 8.3 | 48% | 4 | 2.6 |
| CSKA 1948 Sofia | First League | 170 | 227 | — | 10.7 | 51% | 4.9 | 2.4 |
| Levski Krumovgrad | First League | 72 | 65 | — | 8.7 | 44% | 3.3 | 2.5 |