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Overview

All benchmark data is computed from live PostgreSQL aggregation queries against the nil_deals table. No hardcoded values, no fake data.

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Three Comparison Scopes

ScopeDefinitionSQL Filter
Your SchoolOnly your university’s dealsuniversity_id = :current_user_university
ConferenceAll universities in your conferenceuniversity_id IN (SELECT id FROM universities WHERE conference_id = :your_conf)
Peer GroupUniversities in your custom peer conferencesuniversity_id IN (SELECT id FROM universities WHERE conference_id IN :peer_conf_ids)

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Core Aggregation Helper

The _agg() function runs a single SQL query with all filters applied: 
async def _agg(db, uni_filter, sport_id=None, position_id=None, 
               eligibility_year=None, comp_type=None):
    q = select(
        func.avg(NilDeal.total_value_usd),        # Avg total
        func.avg(NilDeal.guaranteed_value_usd),    # Avg guaranteed
        func.avg(NilDeal.performance_incentives_usd),  # Avg performance
        func.count(NilDeal.id),                    # Deal count
        func.sum(NilDeal.total_value_usd),         # Total value
        func.count(func.distinct(NilDeal.athlete_id)),  # Athletes with deals
    ).where(uni_filter, NilDeal.deal_status == "active")
    
    if sport_id:
        q = q.where(NilDeal.sport_id == uuid.UUID(sport_id))
    if position_id:
        q = q.where(NilDeal.position_id == uuid.UUID(position_id))
    if comp_type:
        q = q.where(NilDeal.compensation_type == comp_type)
    if eligibility_year:
        q = q.join(Athlete).where(Athlete.eligibility_year == int(eligibility_year))
This helper is called 3 times (school, conference, peer) for every benchmark request.

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Percentile Calculations

The grid endpoint uses PostgreSQL’s native percentile_cont() function: 
func.percentile_cont(0.25).within_group(sub.c.athlete_total)  # 25th percentile
func.percentile_cont(0.50).within_group(sub.c.athlete_total)  # Median
func.percentile_cont(0.75).within_group(sub.c.athlete_total)  # 75th percentile
These are computed on per-athlete totals (SUM of all deals for each athlete), not on individual deal values.

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Filter Support

Every endpoint accepts and respects these filters:
FilterParameterHow Applied
Sportsport_idWHERE NilDeal.sport_id = :id
Positionposition_idWHERE NilDeal.position_id = :id
Eligibility Yeareligibility_yearJOIN Athlete WHERE eligibility_year = :year
Comp Typecomp_typeWHERE NilDeal.compensation_type = :type
All filters apply to all three scopes (school, conference, peer) simultaneously.

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Data Anonymization

Per the product spec, benchmarks anonymize cross-institution data:
  • Conference and peer data show only aggregated statistics (AVG, SUM, percentiles)
  • Individual university names, athlete names, and deal details are never exposed in benchmark responses
  • Only the user’s own school data is identifiable