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 |
|---|---|---|---|---|---|---|---|---|
| Chicago Fire | Major League Soccer | 41 | 82 | — | 14.7 | 51% | 4.9 | 2.3 |
| Toronto | Major League Soccer | 38 | 51 | — | 11.1 | 47% | 4.8 | 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.
| Colorado Rapids | Major League Soccer | 39 | 55 | — | 11.7 | 50% | 5 | 2.4 |
| DC United | Major League Soccer | 39 | 40 | — | 11.1 | 41% | 4 | 2.5 |
| Orlando City | Major League Soccer | 40 | 69 | — | 14.6 | 47% | 4.6 | 2.4 |
| Philadelphia Union | Major League Soccer | 42 | 61 | — | 15.3 | 47% | 6.1 | 2.7 |
| SJ Earthquakes | Major League Soccer | 39 | 74 | — | 14.4 | 49% | 6.6 | 2.7 |
| Vancouver Whitecaps | Major League Soccer | 43 | 90 | — | 15 | 54% | 5.5 | 2.3 |
| Sporting KC | Major League Soccer | 38 | 44 | — | 11.2 | 46% | 4.5 | 1.9 |
| New York RB | Major League Soccer | 39 | 62 | — | 12.5 | 52% | 5.1 | 2.4 |
| LA Galaxy | Major League Soccer | 39 | 60 | — | 12.9 | 53% | 5.6 | 2.3 |
| Houston Dynamo | Major League Soccer | 38 | 52 | — | 13.2 | 47% | 4.8 | 2.7 |
| Columbus Crew | Major League Soccer | 42 | 67 | — | 12.6 | 58% | 5.2 | 1.4 |
| Dallas | Major League Soccer | 41 | 69 | — | 11.2 | 40% | 3.9 | 2.6 |
| Portland Timbers | Major League Soccer | 42 | 49 | — | 12.5 | 50% | 4.9 | 1.9 |
| New England | Major League Soccer | 39 | 59 | — | 11.9 | 50% | 4.5 | 2 |
| Real Salt Lake | Major League Soccer | 39 | 54 | — | 15.1 | 51% | 4.7 | 2.6 |
| Seattle Sounders | Major League Soccer | 40 | 69 | — | 14 | 54% | 5.2 | 1.8 |
| New York City | Major League Soccer | 44 | 69 | — | 12 | 55% | 4.5 | 2.1 |
| Cincinnati | Major League Soccer | 43 | 76 | — | 13.5 | 48% | 4.5 | 2.3 |
| Minnesota United | Major League Soccer | 43 | 67 | — | 11.9 | 42% | 4.6 | 2.1 |
| Atlanta United | Major League Soccer | 38 | 41 | — | 11.9 | 51% | 4.4 | 2.1 |
| CF Montréal | Major League Soccer | 38 | 52 | — | 12.6 | 47% | 5 | 2.4 |
| Los Angeles FC | Major League Soccer | 42 | 82 | — | 14.7 | 50% | 5.4 | 1.9 |
| Nashville SC | Major League Soccer | 41 | 73 | — | 12.3 | 52% | 5.1 | 2.1 |
| Inter Miami | Major League Soccer | 46 | 124 | — | 14.5 | 57% | 4.9 | 2.5 |
| Austin | Major League Soccer | 41 | 51 | — | 10.5 | 48% | 4.2 | 2.1 |
| Charlotte | Major League Soccer | 42 | 64 | — | 10.6 | 48% | 4.3 | 2.2 |
| St. Louis City | Major League Soccer | 38 | 53 | — | 14.9 | 49% | 5.3 | 2.1 |
| San Diego | Major League Soccer | 44 | 88 | — | 12.5 | 62% | 5.1 | 2 |