League Average Types

GET /types?developer_type=league_average

League average types represent per-match averages calculated across all fixtures played in a league season. They are attached to fixtures as metrics and can be used as filter criteria in /fixtures/search.

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Naming Convention

Every developer_name follows a strict three-part pattern:

{SCOPE}_{METRIC}_{AGGREGATION}

Examples:

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Full Type Reference

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Values and the /fixtures/search Endpoint

How values are stored

All league average values are stored as integers scaled by 1000. A value of 2200 represents an average of 2.2 events per match.

The OVERALL ÷ 2 rule

OVERALL_* values equal (HOME + AWAY) / 2. This means they represent the average contribution per side, not the full match total.

When filtering by match total, divide your target value by 2.

Example — find leagues averaging 2.2 total goals per match:

A match with 2.2 total goals has on average 2.2 / 2 = 1.1 goals from each side, so OVERALL_GOALS_SCORED is stored as 1100.

# Leagues with average total goals between 2.0 and 2.5 per match
GET /fixtures/search?where[OVERALL_GOALS_SCORED]=1000:1250

Example — find leagues with high corner counts (10+ total per match):

GET /fixtures/search?where[OVERALL_CORNERS_SCORED]=5000:9999

This rule applies to all OVERALL_* types without exception.

HOME and AWAY filters

HOME_* and AWAY_* values do not require division — they already represent a single side's contribution.

# Leagues where home teams average more than 1.5 goals
GET /fixtures/search?where[HOME_GOALS_SCORED]=1500:9999

# Leagues where away teams average fewer than 1 yellow card
GET /fixtures/search?where[AWAY_YELLOWCARDS_SCORED]=0:999

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Practical Application & Use Cases

1. Find high-scoring leagues (Over 2.5 goals market)

To target leagues where both teams are likely to score, filter for OVERALL_GOALS_SCORED above 1250 (= 2.5 total goals ÷ 2).

GET /fixtures/search?window=48
  &where[OVERALL_GOALS_SCORED]=1250:9999
  &include=odds
  &filter[odds]=markets:1

2. Find defensive leagues (Under 2.0 goals market)

GET /fixtures/search?window=24
  &where[OVERALL_GOALS_SCORED]=0:1000
  &status=NOT_STARTED

3. Find corner-heavy leagues

Useful for corners betting markets. 9+ total corners per match → OVERALL_CORNERS_SCORED > 4500.

GET /fixtures/search?window=24
  &where[OVERALL_CORNERS_SCORED]=4500:9999

4. Compare home vs. away scoring strength in a league

A large gap between HOME_GOALS_SCORED and AWAY_GOALS_SCORED indicates strong home advantage in that league. Fetch fixtures with metrics included and compare the two values client-side.

GET /fixtures?league_id=8&include=metrics
  &filter[metrics]=types:303,304

5. Find card-heavy leagues for disciplinary markets

GET /fixtures/search?window=48
  &where[OVERALL_YELLOWCARDS_SCORED]=1750:9999

This targets leagues averaging 3.5+ yellow cards per match (1750 × 2 = 3500 = 3.5 cards).

6. Build a league stats dashboard

Use all OVERALL_* types to build a sortable league comparison table. Fetch fixtures for a date range with include=metrics and group by league.id, reading the metric values for each fixture.

GET /fixtures?start_date=2026-06-01T00:00:00Z&end_date=2026-06-07T23:59:59Z
  &include=metrics
  &filter[metrics]=developer_types:league_average

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UI Implementation Notes

• Display values: Always divide the stored integer by 1000 before displaying (2200 → 2.2).
• OVERALL filter inputs: When accepting a "total match average" from the user (e.g. "2.5 goals"), divide by 2 before sending to the API (2.5 → 1.25 → 1250 in API units).
• HOME/AWAY filter inputs: Divide by 1000 only — no halving required.
• Range sliders: Scale the slider range by 1000. For goals a reasonable UI range is 0–4.0 per side (0–4000 in API units).