Data Sufficiency Score

Overview

The Data Sufficiency Score assesses whether a market has enough high-quality historical data to support a reliable prediction. It acts as a foundational check, helping you avoid markets where analysis is impossible and outcomes are pure chance.

What It Does

This pillar calculates a score by analyzing three key factors: the sheer volume of historical data points (sample size), the completeness of that data (missing values), and the availability of similar past events for comparison. It synthesizes these metrics into a single score indicating the statistical robustness of the available information.

Why It Matters

It prevents you from wasting capital and effort on markets that lack a statistical foundation for analysis. A high score suggests other analytical pillars can be applied with more confidence, while a low score is a strong warning sign of a high-risk speculate.

How It Works

First, the pillar scans all available historical data related to the market's key drivers. It then counts the number of relevant data points and calculates the percentage of missing information. Finally, it searches for historically analogous events and combines these findings into a normalized 0-100 score.

Methodology

The score is a weighted average: Score = (0.5 * log(N)) + (0.3 * Completeness Ratio) + (0.2 * Comparability Score). N is the sample size. The Completeness Ratio is calculated as (1 - [missing data points / total data points]). The Comparability Score is a 0-10 scale based on the number of similar historical events found in the last 5 years.

Edge & Advantage

This provides a critical first-pass filter, saving you from analyzing markets where any perceived edge is just statistical noise.

Key Indicators

• Sample Size (N) Count

  high

  Total number of relevant historical data points available for analysis.

• Data Completeness Ratio

  high

  The percentage of data that is present versus missing or null within the dataset.

• Historical Comparable Availability

  medium

  Measures the existence of similar past events or markets to draw analogies from.

Data Sources

• Internal Market Data Lake

  Aggregated historical data from all other pillars and market feeds.

• Event History Databases

  Archives of past market events and outcomes for comparability analysis.

Example Questions This Pillar Answers

• → Is there enough polling data to reliably predict the outcome of this special election?
• → Does this new crypto token have sufficient trading history to perform technical analysis?
• → Are there enough historical examples of a movie like this to forecast its opening weekend box office?

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