Team Classification
TEAM_GOALS_CLASSIFICATION
TEAM_GOALS_CLASSIFICATIONA numeric metric that classifies a team's overall goal-scoring profile by combining their attack and defense power relative to the league average.
Scope: team
Locations: home, away
Value
An integer from 0 to 3:
| Value | meta.classification | Label |
|---|---|---|
0 | Weak | Weak Team |
1 | Average | Average Team |
2 | Strong | Strong Team |
3 | Top | Top Team |
How It Works
Classification is derived from two underlying values calculated against the league baseline (1.0):
attack_power— how prolific the team is at scoring goals vs. league averagedefense_power— how well the team concedes vs. league average (lower = better)
Both values come from the team's overall goals stats (combining home and away).
Attack categories:
| Range | Category |
|---|---|
| > 1.20 | Top Attack |
| 1.05 – 1.20 | Strong Attack |
| 0.90 – 1.05 | Average Attack |
| < 0.90 | Weak Attack |
Defense categories:
| Range | Category |
|---|---|
| < 0.80 | Top Defense |
| 0.80 – 0.95 | Strong Defense |
| 0.95 – 1.10 | Average Defense |
| > 1.10 | Weak Defense |
The two categories are combined to produce the final classification. For example, Top Attack + Top Defense → Top Team (3); Weak Attack + Weak Defense → Weak Team (0).
Example Response
{
"team_id": 50366,
"type_id": 277,
"developer_name": "TEAM_GOALS_CLASSIFICATION",
"value": 3,
"meta": {
"classification": "Top",
"classification_type": "Team Goals Classification"
},
"location": "home"
}Usage
Use meta.classification for display labels and value for numeric comparisons.
Pre-match strength display — Show a badge (Weak / Average / Strong / Top) next to each team name before a fixture to give users an instant read on the matchup.
Mismatch detection — Compare value between home and away teams. A difference of 2+ (e.g. 3 vs 1) indicates a clear favourite and can be surfaced as a confidence signal.
Fixture filtering — Let users filter or sort fixtures by team strength. For example, show only fixtures where both teams have value >= 2 to focus on high-quality matches.
Prediction models — Use value as a numerical feature in goal or outcome prediction pipelines alongside other attack and defense power metrics.