fedcomp-index-scoring 2026.4.0

FedComp Index classification engine. Assigns contractors to four Posture Classes using two-axis classification (volume x frequency) from USASpending award data.

FedComp Index Scoring

Federal contractor posture classification. Assigns every federal contractor to one of four Posture Classes using two-axis thresholds on base contract data from USASpending.gov.

No composite score. No weighted sum. Classification is structural: base contract dollars ($5M threshold) crossed with base contract count (3 threshold).

• Class 1 - high volume + high frequency (systematic winners, $5M+ across 3+ base contracts)
• Class 2 - high volume + low frequency (concentrated risk, $5M+ but fewer than 3 base contracts)
• Class 3 - low volume + high frequency (growth pipeline, under $5M but 3+ base contracts)
• Class 4 - low volume + low frequency (entry level, under $5M and fewer than 3 base contracts)

Install

pip install fedcomp-index

Usage

from fedcomp_index_scoring import classify, classify_contractor, PostureClass

# Classify from two axes
cls = classify(base_dollars=12_000_000, base_contract_count=5)
print(cls)   # PostureClass.CLASS_1

# Get a full result object
result = classify_contractor(base_dollars=12_000_000, base_contract_count=5)
print(result.posture_class)         # PostureClass.CLASS_1
print(result.base_dollars)          # 12000000
print(result.base_contract_count)   # 5

# Concentrated risk: high dollars, few contracts
result = classify_contractor(base_dollars=8_000_000, base_contract_count=1)
print(result.posture_class)   # PostureClass.CLASS_2

Classification methodology (v1.1)

Two axes, no normalization, no composite score:

Axis	Threshold	What it measures
Base contract dollars (5yr)	$5M	Volume of competitively won work
Base contract count (5yr)	3	Frequency of distinct wins

"Base contracts" means definitive contracts, purchase orders, and BPA calls. Delivery orders and task orders are excluded from classification (they inflate frequency without representing separate competitions).

Posture Class	Volume	Frequency	Profile
Class 1	$5M+	3+ contracts	Systematic winner, repeatable pipeline
Class 2	$5M+	<3 contracts	Concentrated risk, few large awards
Class 3	<$5M	3+ contracts	Growth pipeline, building track record
Class 4	<$5M	<3 contracts	Entry level or dormant

Additional metrics

Beyond classification, this package provides:

Velocity - cadence-based per contractor. Compares early-half vs late-half award gaps to detect accelerating, on pace, slowing, declining, or inactive patterns. Requires 2+ base contracts.

from fedcomp_index_scoring import compute_velocity

v = compute_velocity(["2023-01-15", "2024-06-01", "2025-02-10"])
print(v["cadence"])   # "accelerating" / "on pace" / "slowing" / etc.

Proximity Map - inverse-frequency-squared weighting on shared NAICS/PSC codes, with scale filter (ratio^2 penalty for volume mismatches). Returns top 6 neighbors.

from fedcomp_index_scoring import build_proximity_map

neighbors = build_proximity_map(
    target_codes={"541511": 12, "R425": 3},
    target_volume=8_000_000,
    candidates=[
        {"id": "ABC", "codes": {"541511", "R425"}, "volume": 6_000_000},
        {"id": "DEF", "codes": {"541511"}, "volume": 50_000_000},
    ],
)
# ABC ranks higher: shared rare code + similar scale

Neighborhood Density - fraction of a contractor's neighbors that are also neighbors of each other. Higher density = tighter competitive cluster.

from fedcomp_index_scoring import neighborhood_density

d = neighborhood_density(
    neighbor_sets={"A": {"B", "C"}, "B": {"A", "C"}, "C": {"A"}},
    target_neighbors={"A", "B", "C"},
)

Full methodology: fedcompindex.org/methodology

Data sources

All data is sourced from public federal records:

• USASpending.gov - award history, dollar amounts, agencies, NAICS codes, PSC codes
• SAM.gov - entity registration, certifications
• SBA.gov - small business certification verification

Live rankings

Classified contractor rankings with dossier pages, proximity maps, and velocity tracking:

• Nevada - 348 contractors classified

Datasets

Pre-classified datasets available for download:

• Nevada Federal Contractors - FedComp Index (HuggingFace)
• Nevada Federal Contract Awards - Monthly (HuggingFace)
• Nevada Federal Contractors - FedComp Index (Kaggle)
• Nevada Federal Contract Awards - Monthly (Kaggle)

Also available on npm

npm install fedcomp-index-scoring

• fedcomp-index - meta-package
• fedcomp-index-scoring - classification engine
• fedcomp-index-data - pre-classified datasets

Links

• Website: https://fedcompindex.org/
• Nevada Rankings: https://fedcompindex.org/nv/
• Methodology: https://fedcompindex.org/methodology/
• Tabularium: https://fedcompindex.org/tabularium/
• FAQ: https://fedcompindex.org/faq/
• Source: https://github.com/fedcompindex/FedCompIndex
• PyPI: https://pypi.org/project/fedcomp-index/
• PyPI (Scoring): https://pypi.org/project/fedcomp-index-scoring/
• PyPI (Data): https://pypi.org/project/fedcomp-index-data/
• HuggingFace: https://huggingface.co/datasets/npetro6/nevada-federal-contractors
• Kaggle: https://www.kaggle.com/datasets/npetro6/nevada-federal-contractors-fedcomp-index

Citation

@software{fedcomp_index,
  title = {FedComp Index},
  author = {FedComp Index},
  url = {https://fedcompindex.org/},
  year = {2026}
}

License

MIT