Dynamic Tracking

Purpose

Maintain live rankings that update incrementally as new comparisons arrive. Suitable for ongoing evaluation, A/B testing, and systems where candidates enter/exit over time. Tracks rating uncertainty and temporal dynamics.

When to use

• Continuous stream of comparisons (not batch)
• Candidates added/removed over time
• A/B testing or tournament-style evaluation
• Need to track rating trajectories and momentum

Budget

State Ledger

candidates: {}          # id → {mu, sigma, last_active, history: []}
comparison_log: []      # [{pair, winner, timestamp, context}]
method: ""              # elo | glicko2 | trueskill2 | whr
parameters: {}          # K-factor, tau, beta, etc.
iteration: 0
window_size: 30
convergence: {stable: false, score: 0.0}

Available Tactics

• adaptive-pair-selection — select next matchup for maximum information
• consistency-audit-loop — periodic transitivity audit

Available SOPs

• pair-selector
• comparison-executor
• rating-update
• convergence-check
• cycle-detection
• ranking-synthesis

Execution Guidance

1. Initialize new candidates with default prior
2. For each incoming comparison: a. Run comparison-executor (or accept external result) b. Run rating-update with online method c. Every window_size comparisons, run convergence-check
3. Periodically run cycle-detection on recent window
4. Increase sigma for candidates inactive > threshold
5. On demand: run ranking-synthesis for current snapshot

Output Format

current_ranking:
  - {rank: 1, candidate: "...", mu: 1842, sigma: 45, games: 23}
  - {rank: 2, candidate: "...", mu: 1798, sigma: 52, games: 18}
method: glicko2
total_comparisons: 342
last_updated: "2026-05-19T14:30:00Z"
rating_period: 7
top_movers:
  - {candidate: "...", delta: +120, last_5: "WWWLW"}
stability_score: 0.88