About
What is the National Premier League?
The National Premier League (NPL) ranks and compares all 36 Indian states and union territories across 27 categories and 1000+ metrics of human development and governance. Think of it as a Premier League table for states — making lagging states visible, celebrating leading ones, and driving public awareness through data.
Methodology
Scores are computed in three stages: metric normalization, category aggregation, and overall ranking. The goal is to produce a single comparable score (0–100) for every state across every dimension.
Step 1: Metric Normalization
Each metric is normalized to a 0–100 scale using min-max normalization. Before normalizing, values are capped at the 2nd and 98th percentile to prevent extreme outliers (e.g., a tiny UT with an anomalous value) from compressing the scale for all other states.
capped_value = clamp(value, P2, P98)
score = (capped_value − P2) / (P98 − P2) × 100
If polarity is negative (lower is better), the score is inverted: score = 100 − score
A score of 100 means the state is at or above the 98th percentile (best performer). A score of 0 means it is at or below the 2nd percentile. Metrics need at least 2 states with data to be scored.
Step 2: Category Scores
Each category score is a weighted average of all metric scores within that category. Weights range from 0.3x to 1.5x and reflect the relative importance and reliability of each metric.
category_score = Σ(metric_score × weight) / Σ(weight)
For example, in the Health category, IMR and MMR carry 1.5x weight because they are the most critical health outcomes, while non-veg consumption carries 0.3x as a dietary indicator with indirect health relevance. A state is only scored in a category if it has data for at least one metric in that category.
Step 3: Overall Score
The overall score is a weighted average across all categories where the state has data. Categories are weighted by their importance to human development:
overall_score = Σ(category_score × category_weight) / Σ(category_weight)
1.5x — Economy, Employment, Education, Health
1.2x — Women & Gender, Children & Youth, Infrastructure, Crime & Justice
1.0x — Prices, Agriculture, Energy & Environment, Governance, Financial Inclusion, Demographics, Digital & Tech
0.8x — Elderly, Industry, Transport, Urban Quality, Mental Health
0.7x — Disability, Telecom
0.5x — Sports, Culture & Tourism, Media, Migration, Civil Society
States are then ranked by overall score and assigned a tier based on their score.
Weight Rationale
Metric weights are assigned based on three factors: (1) how directly the metric measures the category's core outcome, (2) reliability and coverage of the data source, and (3) policy significance. Core outcome indicators (e.g., GSDP per capita for economy, IMR for health, unemployment rate for employment) receive 1.2x–1.5x weight. Supplementary or proxy indicators (e.g., TV ownership for infrastructure, heritage sites for culture) receive 0.3x–0.8x weight. Standard indicators receive 1.0x.
Tiers
Top-performing states with strong outcomes across most metrics
Above-average states steadily improving
States making progress but with significant gaps
States needing focused attention and investment
Data Sources
• Ministry of Statistics (MoSPI) — National Accounts, PLFS, CPI, IIP, ASI
• National Family Health Survey (NFHS) — Health, Nutrition, Women, Children
• Census of India / SRS — Demographics, Literacy, Mortality
• National Crime Records Bureau (NCRB) — Crime, Police, Prisons
• UDISE+ / AISHE — School and Higher Education
• Reserve Bank of India (RBI) — Banking, Finance, State Budgets
• TRAI — Telecom and Internet
• Ministry of Road Transport — Roads, Vehicles
• Central Electricity Authority — Power, Renewables
• NITI Aayog — SDG Index, Multidimensional Poverty Index
• Data For India (dataforindia.com) — Curated government datasets, CC-BY 4.0
Limitations
• Data availability varies across states and metrics. Small UTs may have fewer data points.
• Data is typically 1-2 years old due to publication lag by government agencies.
• Rankings simplify complex realities. Use them as a starting point for deeper analysis, not as definitive judgments.