Over/Under Stats by Teams — Rankings 2025/26
Find teams that consistently land over or under the goal line — every threshold from 1.5 to 3.5, full match and per half.
How Over/Under goal stats work
Over/Under percentages show how often a team's matches cleared specific goal thresholds — 1.5, 2.5 and 3.5 — the three goal-line stats most studied in football analytics. Higher percentages mean a team's matches produce more goals on average. The average-goals-per-match column anchors each percentage to the underlying scoring rate, separating consistent high-scorers from variance-driven outliers.
About this ranking
This Over/Under goals ranking surfaces every team by how often their matches clear the 1.5, 2.5 and 3.5 goal lines — the three goal-line stats most studied in football analytics. Sort by season hit rate or recent form (last 5/10/20 matches) and use the average-goals-per-match column to separate genuinely high-scoring sides from variance-driven outliers. Filter by sub-market line to focus on a single threshold, and split by home/away to find venue effects on scoring. Pair with our BTTS team ranking to spot teams that not only score over the line but also concede.
Frequently asked questions
What does Over 2.5 goals mean and which teams hit it most?
Over 2.5 means 3 or more goals were scored in a match. We track Over 1.5, 2.5 and 3.5 thresholds — the three goal-line stats most studied in football analytics. Teams ranked highest combine attacking productivity with defensive fragility, producing high-scoring matches consistently.
How does Over/Under interact with average goals per match?
Average goals per match is the underlying signal; Over/Under percentages are how often that average crosses specific thresholds. A team averaging 3.2 goals/match with consistent results will have higher Over 2.5 hit rate than a team averaging 3.5 with high variance. Use both columns together to separate consistency from outliers.
Why do home/away Over/Under splits matter?
Home teams typically score more goals per match than away teams, so home Over rates run higher. Some teams have dramatically different scoring profiles at home vs away — open at home, cautious away. The home/away filter surfaces these venue-dependent patterns.
