Plain-English definitions for the scoring pillars behind the +35%, 2x, and 5x lists. These are not black-box vibes — they map back to the actual Upside Ranks scoring engine.
Value Gap measures whether a stock looks attractively priced relative to its growth, margins, and analyst upside. The score blends forward P/E, PEG, EV/revenue versus revenue growth, analyst target upside, and margin quality. High-growth companies can still score well at higher multiples if the growth-adjusted valuation looks reasonable. Missing data is not automatically punished; the model redistributes weight across available inputs.
Earnings Momentum measures whether the business is showing financial acceleration. The score starts neutral, then rewards positive earnings growth, quarterly revenue growth running ahead of annual revenue growth, strong profit margins, and strong gross margins. It penalizes declining earnings or revenue deceleration. For the +35% list, this is also a quality gate: stocks need strong earnings momentum to qualify.
Catalyst Edge measures how directly a company is tied to the BRRR AI infrastructure thesis. The engine primarily uses prebuilt Catalyst Edge scores; if a ticker is missing, it falls back to rules based on AI-related business description keywords, sector, and industry. This is the most thesis-driven pillar: it rewards names positioned around AI demand, datacenters, semiconductors, GPUs, cloud, automation, and related infrastructure bottlenecks.
Technical Setup measures whether the chart looks coiled, healthy, and not already overextended or broken. The model rewards range compression, healthy moving-average positioning, declining volume during consolidation, proximity to highs when the trend is strong, and moderate recent momentum. It penalizes stocks far below key moving averages, down heavily from 52-week or all-time highs, or showing multi-year death-chart behavior.
Social Momentum measures whether retail and attention velocity are building around the ticker. When available, it uses the BRRR Buzz Score engine, which looks at Google search velocity, Reddit mention acceleration, Wikipedia page-view surges, options activity skew, and short-interest momentum. If live attention data is unavailable, the model falls back to market-based proxies such as unusual volume and short interest rather than forcing missing data into the score.
Goldilox Upside measures the plausible-but-aggressive bull case: near-perfect execution plus near-perfect demand catalysts, without relying on fantasy outcomes. It converts a manually estimated bull-case market cap into an upside multiple, then maps that multiple into a 0–100 score. More importantly, it acts as a guardrail: the 2x list requires at least a 2.2x Goldilox multiple, and the 5x list requires at least a 6.0x Goldilox multiple. This prevents low-upside names from leaking into high-upside lists just because they score well elsewhere.
+35% list: quality-focused, with Value Gap and Earnings Momentum carrying more weight and an Earnings Momentum qualification gate.
2x list: catalyst-led, with Catalyst Edge plus Goldilox Upside guardrails doing the heavy lifting.
5x list: asymmetric moonshot-focused, requiring a much higher Goldilox multiple before a stock can qualify.
The probability column is not a historical backtest probability or a Monte Carlo forecast; it is a model-implied confidence estimate derived from the same pillar inputs used to rank each list. Each tier has its own weighting formula: the +35% list leans most on Value Gap and Earnings Momentum, the 2x list leans on Catalyst Edge plus Goldilox Upside, and the 5x list leans most on Goldilox Upside, Catalyst Edge, Social Momentum, and Technical Setup. After the tier-specific composite score is calculated, the code maps that score into a bounded probability range so easier targets show higher probabilities and true moonshots stay appropriately low. The result is best understood as a calibrated ranking output for that outcome tier, not a guaranteed actuarial probability.