PillarLab

Historical Relationship Duration Patterns

Historical Relationship Duration Patterns: a specialized PillarLab analytical framework for prediction market analysis. Built for Polymarket and Kalshi event contracts.

Historical Relationship Duration Patterns is one of over 1,700 specialized analytical frameworks PillarLab AI uses to evaluate prediction markets on Polymarket and Kalshi. Each pillar guides domain-specific analysis with transparent reasoning and source citations, helping traders identify mispriced event contracts.

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This pillar is part of PillarLab's structured analytical framework library. Sign up to access full analysis with source citations, factor weighting, and confidence scores.

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9-Pillar Analysis ~30s

#1 AI-Powered Prediction Market Analyzer Tool for Kalshi & Polymarket

Paste any market URL for a full 9-pillar analysis: Vegas lines, injury reports, weather forecasts, sentiment, edge vs market, and a Best Trade call.

How it works

Paste a Kalshi or Polymarket URL → AI fetches real-time API market data → 4–5 parallel intelligence sources → a deep 9-pillar analysis → an actionable prediction with an edge estimate. 5 market categories: Politics, Sports, Weather, Finance and General.

⚽ Sports (9 Pillars)

  1. 1.Vegas Lines → spread, moneyline, totals
  2. 2.Line Movement → sharp money signals
  3. 3.Injury Reports → player status tracking
  4. 4.Reddit Sentiment → r/sportsbook consensus
  5. 5.Team Statistics → form, H2H, splits
  6. 6.Situational Factors → rest, travel, schedule
  7. 7.Handle Splits → public vs sharp money
  8. 8.Time Decay → late line movement
  9. 9.Contrarian Check → fade-the-public value

🌦️ Weather (9 Pillars)

  1. 1.Forecast Consensus → GFS / ECMWF / NWS agreement
  2. 2.Ensemble Spread → GEFS uncertainty range
  3. 3.Forecast Skill Decay → accuracy vs time horizon
  4. 4.Historical Base Rate → NOAA climate frequency
  5. 5.Model Divergence → where models disagree vs market
  6. 6.Micro-Climate Bias → station-specific effects
  7. 7.Storm Track → NHC cone / hurricane tracking
  8. 8.Market Pricing → implied probability vs forecast
  9. 9.Contrarian Check → overshoot / mean-reversion

💰 Finance (9 Pillars)

  1. 1.Technical Analysis → RSI, MACD, support levels
  2. 2.Fundamental Metrics → P/E, revenue, earnings
  3. 3.Smart Money → institutional flows, insider trades
  4. 4.Reddit Sentiment → WSB, r/stocks analysis
  5. 5.Macro Indicators → Fed rates, inflation, GDP
  6. 6.News Catalysts → earnings, product launches
  7. 7.Social Velocity → Twitter momentum tracking
  8. 8.Market Signals → volume, bid-ask analysis
  9. 9.Contrarian Check → overcrowded trade detection

📰 General (9 Pillars)

  1. 1.Base Rate → historical frequency analysis
  2. 2.Official Benchmarks → expert forecasts
  3. 3.Reddit Sentiment → subreddit consensus
  4. 4.Social Velocity → viral momentum tracking
  5. 5.News Recency → 24–72h breaking news
  6. 6.Source Quality → credibility scoring
  7. 7.Time Decay → resolution timeline
  8. 8.Contrarian Check → hidden edge detection

🏛️ Politics (9 Pillars)

  1. 1.Polls Analysis → polling vs market odds divergence
  2. 2.Economic Impact → GDP, unemployment affecting votes
  3. 3.Approval Ratings → candidate favorability tracking
  4. 4.Cross-Platform Markets → Polymarket vs Kalshi arbitrage
  5. 5.Breaking News → events shifting political odds
  6. 6.Regional Patterns → swing state analysis
  7. 7.Social Sentiment → Reddit, X political trends
  8. 8.Data Uncertainty → confidence intervals
  9. 9.Contrarian Risk → devil's advocate view