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Pre-scored federal contractor datasets with FedComp Index scores and posture class assignments. Updated monthly.

Project description

FedComp Index Data

Pre-scored federal contractor datasets bundled for Python. Each contractor has a FedComp Index score (0-100) and posture class assignment based on five years of USASpending award data.

Updated monthly with fresh data from the FedComp Index pipeline.

Install

pip install fedcomp-index-data

Usage

from fedcomp_index_data import load_state, lookup

# Load all scored contractors for a state
contractors = load_state("NV")
print(f"{len(contractors)} contractors scored")

# Filter by posture class
class_1 = [c for c in contractors if c["posture_class"] == "Class 1"]
print(f"{len(class_1)} Class 1 contractors")

# Look up a specific contractor by UEI
c = lookup("CGAKREGGN9J3")
print(c["legal_name"])      # FLEET VEHICLE SOURCE INC
print(c["fedcomp_index"])   # 81
print(c["posture_class"])   # Class 1

Available states

State Contractors Updated
Nevada (NV) 348 March 2026

More states are added as the FedComp Index expands coverage.

Data fields

Field Description
rank FedComp Index rank within the state
legal_name Registered legal name from SAM.gov
uei Unique Entity Identifier
cage CAGE code
fedcomp_index FedComp Index score (0-100)
posture_class Posture class: Class 1 (60+), Class 2 (40-59), Class 3 (<40)
awards_5yr_total_usd Total award dollars over trailing 5-year window
award_count Number of distinct awards
active_contracts Currently active contracts
last_award_date Date of most recent award (YYYY-MM)
primary_naics Primary NAICS code
top_agency Most frequent awarding agency
city Registered city
certifications SBA certifications (pipe-separated)
state State of registration
scored_date Year-month this score was computed

FedComp Index scoring

Two drivers, no normalization:

Driver Weight How it works
Award volume 90% log10 of total dollars won, mapped to 0-100
Award recency 10% Last award date, bucketed by age

Posture classes are fixed thresholds:

  • Class 1 - score 60+ (~$100M+ in awards)
  • Class 2 - score 40-59 (~$5M-$100M)
  • Class 3 - below 40

Full methodology: fedcompindex.org/wiki/methodology

Related packages

Data sources

Links

License

MIT

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