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Chicago C vs Pittsburgh

Sports Β· Kalshi Β· Called NO, verified WIN Β· Jul 26, 2026
View the original Kalshi market →
Verified Outcome
WIN
PillarLab called Chicago C will NOT win (NO @ 45%).
Result: Chicago C did not win. The call was correct.
Analyzed: Jul 26, 2026, 5:36 PM UTC
Market resolved: Jul 26, 2026, 8:12 PM UTC
Called 3h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxmlbgame/professional-baseball-game/KXMLBGAME-26JUL261335CHCPIT?utm_source=kalshiapp_eventpage&referral=1d67bb94-a005-4219-acfd-7d3b12e4e6a0

PillarLab
πŸ“‘ Fetching Kalshi data...
PillarLab
βœ… Got data from Kalshi API!0.5s
PillarLab
πŸ” Step 1: Detecting market type and category...
PillarLab
βœ… Category: ⚽ SPORTS βœ… Type: BINARY πŸ“Š Market: Chicago C vs Pittsburgh πŸ“… Date: 2026-07-26T17:35:00.000Z πŸ’° Volume: $518,904 Current Odds: β€’ Pittsburgh: 57% β€’ Chicago C: 45%4.3s
PillarLab
⏳ Step 2: Gathering intelligence (parallel)...
PillarLab
βœ… Intelligence gathered: Vegas Lines Β· Injury Report Β· Team Stats Β· Reddit Sentiment Β· Situational Factors Β· PillarScore Β· Handle Split (Public vs Sharp) Β· Candidate Dossiers (case for + case against, every candidate β‰₯2%)27.9s
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🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ Chicago C vs Pittsburgh

Team/PlayerCurrent Odds
Pittsburgh57%
Chicago C45%

2 options total

PillarLab

🎯 Best Trade

  • Action: Chicago C β€” NO @ 45%

  • Confidence: 🟑 MEDIUM Β· Uncertainty: LOW

  • Why:

  • Despite recent series wins, Chicago is still on the road and not the market favorite, with 65% of money on Pittsburgh.
  • Injury list includes key pitchers (Steele, Harvey, Miller, Hodge), weakening the rotation.
  • Season record (59-45) is strong, but recent road form is less consistent over a longer sample.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
Pittsburgh57%🟒 FAVORED
Chicago C45%πŸ”΄ TRAILING

I price with the market β€” 🟒 favored Β· πŸ”΄ trailing. The analysis below is the why.

PillarLab

πŸ”¬ 9-Pillar Analysis (Sharp Trading Method)

πŸ“Š 1. Vegas Line

My Vegas line aligns with market odds, pricing Pittsburgh around 57% and Chicago around 45%, consistent with sportsbook consensus.

πŸ“ˆ 2. PillarScore

PillarScore shows Pittsburgh at 71.4 (5-2 in last 7) but sample is too small to be reliable; Chicago's 61.7 (9-6 in last 15) also lacks full opponent-quality context from truncated data β€” insufficient to override market.

πŸ₯ 3. Injury Report

Chicago missing multiple pitchers (Steele, Harvey, Miller, Hodge) and Pittsburgh missing key hitters (Cruz, Horwitz, RodrΓ­guez) β€” both sides depleted, no clear edge.

πŸ“± 4. Reddit Sentiment

Reddit sentiment leans toward Cubs confidence after back-to-back wins in the series, but no strong contrarian signal; market remains Pittsburgh-favored.

πŸ”’ 5. Team Stats & Form

Chicago's recent form is stronger with series wins including an 11-0 rout; Pirates split recent series with Milwaukee, Cleveland, and Yankees, and have lost first two at home to Chicago.

🌀️ 6. Situational Factors

Home advantage for Pittsburgh, but Chicago has already taken the first two games of the series at PNC Park, including a blowout; no weather or travel edge noted.

πŸ’° 7. Handle Split (Public vs Sharp)

Betting consensus shows 65% of money on Pittsburgh and 35% on Chicago, suggesting public leaning on favorite, but sharp money split not confirmed.

⏰ 8. Time Decay

LOW - No imminent news or lineup changes likely to shift price before game; the market has already absorbed the series results.

⚑ 9. Contrarian Check

The 'obvious loser' is Chicago? But they've won the first two games on the road, so they are not undervalued; the market's 45% may be too low given recent head-to-head dominance, but that is already reflected in the narrow gap.

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9-Pillar Analysis ~30s

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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 β†’ AI fetches real-time API market data β†’ 4–5 parallel intelligence sources β†’ a deep 9-pillar analysis β†’ an actionable prediction with an edge estimate. 5 market categories: Politics, Sports, Weather, Finance and General.

⚽ Sports (9 Pillars)

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

🌦️ Weather (9 Pillars)

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

πŸ’° Finance (9 Pillars)

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

πŸ“° General (9 Pillars)

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

πŸ›οΈ Politics (9 Pillars)

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