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Pittsburgh vs New York Y

Sports Β· Kalshi Β· Called NO, verified LOSS Β· Jul 22, 2026
View the original Kalshi market →
Verified Outcome
LOSS
PillarLab called Pittsburgh will NOT win (NO @ 41%).
Result: Pittsburgh won. The call was wrong.
Analyzed: Jul 22, 2026, 4:58 AM UTC
Market resolved: Jul 22, 2026, 8:27 PM UTC
Called 15h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxmlbgame/professional-baseball-game/KXMLBGAME-26JUL221335PITNYY?utm_source=kalshiapp_eventpage&referral=27384e31-6de9-4bb2-a4e6-87819f15dd5b

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: Pittsburgh vs New York Y πŸ“… Date: 2026-07-22T17:35:00.000Z πŸ’° Volume: $57,748 Current Odds: β€’ New York Y: 60% β€’ Pittsburgh: 41%4.4s
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· Line Movement Β· Handle Split (Public vs Sharp) Β· Candidate Dossiers (case for + case against, every candidate β‰₯2%)10.4s
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🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ Pittsburgh vs New York Y

Team/PlayerCurrent Odds
New York Y60%
Pittsburgh41%

2 options total

PillarLab

🎯 Best Trade

  • Action: Pittsburgh β€” NO @ 41%

  • Confidence: 🟑 MEDIUM Β· Uncertainty: MEDIUM

  • Why:

  • Key player Endy RodrΓ­guez out with lower-body injury
  • Road game at Yankee Stadium
  • Despite strong road scoring, recent loss to Yankees 8-5
  • Injury and market drift suggest lower probability of win
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
New York Y60%🟒 FAVORED
Pittsburgh41%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 prices New York Y at 60% and Pittsburgh at 41%, consistent with sportsbook odds around -117/-148 vs -103/-106.

πŸ“ˆ 2. Line Movement

Line movement showed New York moneyline drifting from around -117 to -148, indicating the market's lean toward the Yankees.

πŸ₯ 3. Injury Report

Pittsburgh's Endy RodrΓ­guez (lower-body) is listed as out/doubtful with a return date of July 23-24; New York's Carlos RodΓ³n is on the 15-day IL with elbow inflammation.

πŸ“± 4. Reddit Sentiment

Reddit game-thread chatter noted Yankees' offensive frustration and a postponed game, with no strong consensus favoring either sideβ€”overall sentiment neutral.

πŸ”’ 5. Team Stats & Form

Pittsburgh enters at 52-49, New York at 56-44. Recent form: the prior game on July 20 saw Pirates 5, Yankees 8. Pittsburgh has scored 5.33 runs per road game recently, but the Yankees have a slight edge in record and home performance.

🌀️ 6. Situational Factors

Game at Yankee Stadium favors New York; the July 21 postponement affected restβ€”Yankees had extra rest, Pirates may have had disrupted routine.

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

No handle split data is publicly available for this MLB moneyline market, so the public-sharp split cannot be assessed.

⏰ 8. Time Decay

MEDIUM - Injury news on RodrΓ­guez is known and priced in; further lineup announcements could shift sentiment slightly, but the market already reflects current info.

⚑ 9. Contrarian Check

Pittsburgh's recent offensive surge and near even-money pricing in some early lines suggest they are a live underdog, but the market's drift and home-field advantage for New York justify the 60% price.

PillarLab

⚠️ Disclaimer: AI can make mistakes. This is not financial advice. Do your own research before placing any trades.

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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 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