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 |
|---|---|---|---|---|---|---|---|---|
| Newport County | League Two | 47 | 49 | — | 10.7 | 47% | 3.9 | 2.1 |
| Colchester United | League Two | 47 | 62 | — | 12.4 | 54% | 4.7 | 1.9 |
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.
| Walsall | League Two | 50 | 61 | — | 11.1 | 41% | 4.6 | 1.7 |
| Crewe Alexandra | League Two | 47 | 64 | — | 11.9 | 50% | 4.9 | 1.8 |
| Notts County | League Two | 52 | 79 | — | 11.2 | 55% | 4.7 | 2.3 |
| Salford City | League Two | 50 | 67 | — | 13.5 | 50% | 5.6 | 2.1 |
| Shrewsbury Town | League Two | 46 | 42 | — | 11.3 | 44% | 4 | 2.2 |
| Grimsby Town | League Two | 49 | 77 | — | 13.7 | 56% | 6 | 1.5 |
| Tranmere Rovers | League Two | 47 | 58 | — | 11.9 | 49% | 4.2 | 2 |
| Crawley Town | League Two | 46 | 44 | — | 13.4 | 57% | 5.5 | 2.3 |
| Oldham Athletic | League Two | 46 | 60 | — | 13.1 | 48% | 5.2 | 1.9 |
| Bristol Rovers | League Two | 46 | 56 | — | 11.6 | 50% | 4.6 | 2.6 |
| Cambridge United | League Two | 46 | 66 | — | 11.6 | 51% | 5.8 | 1.4 |
| Chesterfield | League Two | 51 | 73 | — | 12 | 59% | 5.5 | 1.9 |
| Swindon Town | League Two | 47 | 70 | — | 11.6 | 52% | 5.2 | 1.9 |
| Bromley | League Two | 47 | 74 | — | 13.3 | 45% | 5.3 | 1.9 |
| AFC Wimbledon | League Two | 4 | 4 | — | 10.3 | 42% | 3.5 | 2.3 |
| Gillingham | League Two | 47 | 54 | — | 12.5 | 47% | 5 | 2.3 |
| Fleetwood Town | League Two | 47 | 57 | — | 12.2 | 49% | 4.7 | 2.1 |
| Barnet | League Two | 46 | 70 | — | 14.2 | 56% | 6.9 | 1.7 |
| Milton Keynes Dons | League Two | 47 | 86 | — | 12.4 | 49% | 4.4 | 2 |
| Cheltenham Town | League Two | 47 | 53 | — | 10.2 | 49% | 4.2 | 1.9 |
| Barrow | League Two | 47 | 45 | — | 10.7 | 46% | 4.3 | 2.1 |
| Harrogate Town | League Two | 47 | 41 | — | 11.5 | 47% | 4.4 | 1.7 |
| Accrington Stanley | League Two | 47 | 47 | — | 11.7 | 48% | 4.1 | 2.2 |