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 |
|---|---|---|---|---|---|---|---|---|
| Charlton Athletic | League One | 4 | 5 | — | 9.8 | 50% | 2.8 | 1.5 |
| Bolton Wanderers | League One | 50 | 77 | — | 16.4 | 59% | 6.1 | 1.6 |
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.
| Wigan Athletic | League One | 48 | 51 | — | 10.3 | 46% | 5.3 | 1.8 |
| Reading | League One | 47 | 66 | — | 10.9 | 50% | 3.9 | 2.1 |
| Blackpool | League One | 48 | 58 | — | 9.9 | 46% | 4.2 | 2.1 |
| Barnsley | League One | 47 | 72 | — | 11.7 | 52% | 4.9 | 2.1 |
| Cardiff City | League One | 46 | 90 | — | 15.9 | 63% | 6.2 | 1.8 |
| Plymouth Argyle | League One | 46 | 75 | — | 13.8 | 48% | 5.6 | 2.7 |
| Luton Town | League One | 46 | 68 | — | 13.3 | 56% | 6.1 | 1.6 |
| Bradford City | League One | 48 | 58 | — | 12.6 | 50% | 5.6 | 2.2 |
| Rotherham United | League One | 47 | 43 | — | 10.6 | 45% | 4.5 | 2.1 |
| Burton Albion | League One | 48 | 52 | — | 11.4 | 46% | 5.2 | 1.5 |
| Wycombe Wanderers | League One | 49 | 70 | — | 12.9 | 53% | 5.4 | 1.7 |
| Exeter City | League One | 47 | 52 | — | 10.6 | 51% | 4.9 | 1.9 |
| Northampton Town | League One | 47 | 40 | — | 9.1 | 45% | 4.7 | 1.6 |
| Mansfield Town | League One | 48 | 69 | — | 11.6 | 46% | 4.7 | 1.6 |
| Doncaster Rovers | League One | 46 | 50 | — | 12.6 | 50% | 4.3 | 2 |
| Stockport County | League One | 52 | 81 | — | 13 | 55% | 5.3 | 1.9 |
| Huddersfield Town | League One | 47 | 75 | — | 13 | 50% | 5.9 | 1.9 |
| Peterborough United | League One | 48 | 67 | — | 12 | 55% | 4.4 | 2.1 |
| Leyton Orient | League One | 50 | 66 | — | 11.5 | 52% | 4.7 | 2.3 |
| AFC Wimbledon | League One | 46 | 51 | — | 10.5 | 47% | 4.7 | 1.6 |
| Lincoln City | League One | 47 | 89 | — | 12.6 | 43% | 4.3 | 2.2 |
| Port Vale | League One | 46 | 36 | — | 11.1 | 46% | 4.9 | 1.7 |
| Stevenage | League One | 49 | 50 | — | 9.9 | 45% | 4.3 | 2.1 |