Bittensor Subnet Intelligence
Bittensor analytics sections
Momentum scoring across 129 subnets — powered by TurboVision pipeline
Bittensor subnet scoring: ICM Analytics tracks 129+ Bittensor subnets with a proprietary Master Score combining market dynamics (35%), GitHub development signals (40%), and AI-driven fundamental analysis (25%). Updated daily via automated pipeline.
How scores work: Each subnet receives a Market Score (price, volume, liquidity, staking), GitHub Score (commits, PRs, releases, code quality), and AI Score (usefulness, adoption, code quality, ship speed). These combine into a 0-100 Master Score with letter ratings (S ≥ 80, A ≥ 65, B ≥ 50). Higher scores indicate stronger multi-dimensional momentum.
Data by ICM Analytics — Bittensor subnet intelligence powered by TaoStats ingestion, GitHub scanning, and LLM analysis. Updated daily.
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Why Momentum Matters in dTAO
The structural feedback loop that drives subnet performance
The dTAO Momentum Flywheel
Unlike traditional markets, dTAO has a built-in mechanism that amplifies momentum. Capital allocation directly influences a subnet's economic capacity, creating a self-reinforcing cycle that makes momentum both predictable and persistent.
Stakers allocate TAO
More TAO/block rewards
Fund development & mining
Community & awareness
Cycle repeats
Scoring Methodology
Three pillars combine into a single Master Score per subnet
Market Score
Volatility-adjusted price dynamics, trading volume, liquidity depth, alpha staking flows, and emission share. Captures how capital is moving into or out of a subnet.
0 – 100 • Weight: 35%GitHub Score
Commit cadence & momentum trend (30d vs 90d), merged PRs, release velocity, PR review depth (avg reviews + self-merge ratio), commit message quality (conventional commits %), dependency management (Dependabot/Renovate), CI/tests/lint/docs depth, and bus factor (contributor concentration risk).
0 – 100 • Weight: 40%AI Score
LLM-driven analysis scoring all four dimensions independently: Usefulness (real-world utility & durability), Code Quality (engineering rigor), Adoption (community traction & bus factor), Ship Speed (velocity & momentum). Blended 60/40 with heuristic signals for each dimension.
0 – 100 • Weight: 25%Master Score
Weighted composite: Market (35%) + GitHub (40%) + AI (25%). Produces a letter rating: S (exceptional), A (strong), B (good), C (below average). GitHub development quality carries the most weight.
0 – 100 • S ≥ 80 • A ≥ 65 • B ≥ 50 • C < 50Top Subnets by Master Score
Ranked by combined market, GitHub, and AI momentum signals
Subnet Spotlight
Deep dive into top-performing subnets
How To Use This Data
Interpreting subnet scores for staking and delegation decisions
Reading the Scores
The Master Score is not a buy/sell signal — it's a momentum indicator that helps you understand which subnets have the strongest multi-dimensional signals at any given time. Here's how to interpret each component:
- High Market + Low GitHub: Price momentum without development backing. Could be speculative — proceed with caution.
- High GitHub + Low Market: Strong development activity that the market hasn't priced in yet. Potential opportunity if fundamentals hold.
- High AI Score + Low Market: The LLM analysis sees strong usefulness and adoption potential, but capital hasn't followed. May indicate early-stage or undiscovered subnet.
- All three high (S-rated): Strong consensus across market, development, and AI analysis. These subnets have the most robust momentum profiles.
- Declining Master Score: Momentum is fading — could signal capital rotation, development slowdown, or narrative exhaustion.
From Momentum to Action
Traditional momentum investing suggests buying winners and avoiding losers over short-to-medium timeframes. In dTAO, this translates to:
- Staking allocation: Weight toward higher Master Score subnets for emission capture
- Rebalancing: Review allocations when scores shift by >10 points in either direction
- Risk management: Diversify across 5-8 subnets rather than concentrating in the top 1-2
- Time horizon: Momentum signals are most informative over 3-10 day windows, per academic research on dTAO markets