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 |
|---|---|---|---|---|---|---|---|---|
| Nagoya Grampus | J1 League | 149 | 173 | 0 | 11.1 | 47% | 4.2 | 1.3 |
| Shimizu S-Pulse | J1 League | 77 | 80 | 0 | 11.6 | 47% | 4.4 | 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.
| Sagan Tosu | J1 League | 110 | 134 | — | 11 | 51% | 4.4 | 1.2 |
| Shonan Bellmare | J1 League | 148 | 165 | — | 11.4 | 49% | 5 | 1.4 |
| Yokohama | J1 League | 110 | 90 | — | 10.1 | 44% | 4.1 | 1.3 |
| Consadole Sapporo | J1 League | 110 | 147 | — | 14.1 | 55% | 5.1 | 1.4 |
| Tokyo Verdy | J1 League | 76 | 74 | — | 10.9 | 46% | 4.5 | 1.4 |
| Yokohama F. Marinos | J1 League | 148 | 252 | — | 13.9 | 57% | 5.2 | 1.4 |
| Avispa Fukuoka | J1 League | 148 | 146 | — | 11.4 | 43% | 4 | 1.6 |
| FC Tokyo | J1 League | 148 | 185 | — | 11.6 | 49% | 4.6 | 1.5 |
| Kyoto Sanga | J1 League | 110 | 145 | — | 12.5 | 46% | 5 | 1.6 |
| Albirex Niigata | J1 League | 110 | 116 | — | 12.6 | 56% | 4.7 | 1.1 |
| Cerezo Osaka | J1 League | 148 | 189 | — | 12.9 | 53% | 5.2 | 0.9 |
| Kashima Antlers | J1 League | 148 | 223 | — | 13 | 50% | 4.9 | 1.3 |
| Vissel Kobe | J1 League | 148 | 229 | — | 12.8 | 52% | 5.2 | 1 |
| Kawasaki Frontale | J1 League | 148 | 265 | — | 13.8 | 55% | 5 | 1.2 |
| Tokushima Vortis | J1 League | 38 | 34 | — | 10 | 55% | 3.9 | 1.1 |
| Fagiano Okayama | J1 League | 38 | 34 | — | 10.9 | 42% | 4.9 | 1.1 |
| Júbilo Iwata | J1 League | 38 | 47 | — | 11.5 | 42% | 4.4 | 1.4 |
| Machida Zelvia | J1 League | 76 | 106 | — | 12.6 | 44% | 4.8 | 1.4 |
| Vegalta Sendai | J1 League | 38 | 31 | — | 10.7 | 44% | 3.6 | 1.1 |
| Sanfrecce Hiroshima | J1 League | 148 | 204 | — | 15.7 | 52% | 6 | 0.9 |
| Oita Trinita | J1 League | 38 | 31 | — | 7.7 | 47% | 3.6 | 0.6 |
| Urawa Reds | J1 League | 148 | 183 | — | 12 | 53% | 4.7 | 1 |
| Gamba Osaka | J1 League | 148 | 173 | — | 12.3 | 51% | 4.4 | 1.2 |
| Kashiwa Reysol | J1 League | 148 | 169 | — | 12.7 | 50% | 5.3 | 1.3 |