Market intelligence infrastructure for digital assets
The research, methodology and data layer behind Moonboard. Every score the platform publishes is documented here: what goes in, how it is normalised, what the number means, and where it stops working.
Explains how the intelligence works
Formulas, input fields, normalisation steps, refresh intervals and the conditions under which each metric degrades. Written for analysts, researchers and developers who need to know what a number rests on before they use it.
Start with the methodologyLets you work with the intelligence
The live application: rankings across hundreds of assets, interactive charts, trending topics, ecosystem activity and news, all recomputed continuously as new data arrives.
Open the live dashboardFrom raw inputs to a comparable number
Moonboard collects heterogeneous data on hundreds of assets, brings it onto shared scales, and reduces it to a small set of ranked metrics. Each step below is documented in its own page.
Inputs
- Market data
- Social data
- News
- Liquidity
- Community
- Developer activity
Moonboard intelligence layer
- Normalisation
- Percentile ranking
- Z-scores
- Weighting
- Scoring
- Anomaly detection
Outputs
- Rankings
- Signals
- Dashboards
- Research
- Charts
Interactive analysis
- moonboard.ai
Seven metrics, documented individually
Moonboard publishes seven scores. They are not seven views of the same thing: three are percentile ranks, two are centred on 50, one is an absolute point total. Reading them correctly starts with knowing which is which.
Moonboard Score
absoluteMoonboard Score™
Absolute 0–100 composite that rewards social activity which is large relative to an asset’s market footprint.
OPS
percentileOutperformance Score
Blends the Moonboard Score with sentiment, liquidity, social scale and stability, then ranks the blend across the observed universe.
LMR
percentileLiquidity Momentum Ratio
Turnover — 24h volume relative to market capitalisation — log-damped and ranked across the universe.
SMI
percentileSocial Momentum Index
Weighted social footprint — posts, engagement and share of voice — ranked across the universe.
SDE
percentileSentiment Divergence Edge
Gap between how positive the conversation is and how calm the price has been, ranked across the universe.
COR
centredComposite Overperformance Ranking
Mean z-score across five standardised metrics, mapped from a ±3σ range onto 0–100.
WSD
centredWhale-Social Divergence
Difference between an asset’s turnover percentile and its retail-attention percentile, centred on 50.
Where the open questions are
Six areas where measurement is genuinely hard, and where Moonboard's own approach has known limits worth stating out loud.
Social intelligence
Separating conversation volume from conversation that means something.
AI agents & synthetic activity
Why distinguishing organic from generated engagement is getting harder, not easier.
Liquidity
What turnover can and cannot tell you about market depth.
Sentiment
Text classification at scale, and what happens at the extremes.
Ecosystem activity
Developer and community signals over horizons longer than a trade.
Narratives
Sector rotation, and the problem of classifying assets into themes.
What is measured, and how well
Five categories of input, each with its own refresh interval, coverage profile and failure mode. Social data is thin for new listings; market data is dense but noisy at low capitalisations. Both facts change how the scores should be read.
Run the arithmetic yourself
Calculators that implement the published formulas on values you type in. No account, no data collection, nothing leaves the browser — useful for checking that a documented formula behaves the way the text claims.
Analyse live assets on Moonboard
Everything documented here is computed continuously across the tracked universe in the Moonboard application.