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New York Y vs Philadelphia

Sports Β· Kalshi Β· Called NO, verified WIN Β· Jul 26, 2026
View the original Kalshi market →
Verified Outcome
WIN
PillarLab called New York Y will NOT win (NO @ 36%).
Result: New York Y did not win. The call was correct.
Analyzed: Jul 26, 2026, 10:54 PM UTC
Market resolved: Jul 27, 2026, 2:32 AM UTC
Called 4h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxmlbgame/professional-baseball-game/kxmlbgame-26jul261920nyyphi

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πŸ“‘ Fetching Kalshi data...
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βœ… Got data from Kalshi API!0.4s
PillarLab
πŸ” Step 1: Detecting market type and category...
PillarLab
βœ… Category: ⚽ SPORTS βœ… Type: BINARY πŸ“Š Market: New York Y vs Philadelphia πŸ“… Date: 2026-07-26T23:20:00.000Z πŸ’° Volume: $3,028,196 Current Odds: β€’ Philadelphia: 66% β€’ New York Y: 36%3.2s
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%)24.5s
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🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ New York Y vs Philadelphia

Team/PlayerCurrent Odds
Philadelphia66%
New York Y36%

2 options total

PillarLab

🎯 Best Trade

  • Action: New York Y β€” NO @ 36%

  • Confidence: 🟑 MEDIUM Β· Uncertainty: MEDIUM

  • Why:

  • Cody Bellinger placed on 10-day IL with a left hamstring strain on game day, removing a key bat.
  • Starter Will Warren has poor recent form: 0-3 with a 5.45 ERA over his last 8 appearances (7 starts).
  • Road game against a strong home team despite recent series wins; the market may view the streak as unsustainable against the Phillies' overall talent.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
Philadelphia66%🟒 FAVORED
New York Y36%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 reads Philadelphia at 51.4% implied, lower than the prediction market's 66%, suggesting the market is weighting recent series results more heavily than the static moneyline.

πŸ“ˆ 2. PillarScore

Insufficient sample (only 3 games each) but Philadelphia lost its last 4 overall while New York won 3 straight and took the first two of the series, indicating a form advantage for the trailing side that the market price does not fully reflect.

πŸ₯ 3. Injury Report

New York lost Cody Bellinger (10-day IL, left hamstring strain, 2026-07-26); Philadelphia has multiple bullpen injuries (Keller, Banks, Trivino, Stubbs) and MarchΓ‘n day-to-day.

πŸ“± 4. Reddit Sentiment

Reddit sentiment leaned Yankees after two wins, but no quantified split; the crowd may be hyping the underdog, but it's not a strong signal.

πŸ”’ 5. Team Stats & Form

New York's recent form (3 straight wins, series lead) is stronger than their 36% price implies, while Philadelphia's 4-game losing streak is worse than their 66% price suggests; however the Bellinger injury and home-field edge for Philadelphia may justify some of the gap.

🌀️ 6. Situational Factors

Philadelphia has home field at Citizens Bank Park, but faces momentum pressure after losing first two games of the series.

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

No public handle split data available; the market appears driven by public sentiment favoring Philadelphia based on season standing rather than recent form.

⏰ 8. Time Decay

MEDIUM - the market may not have fully adjusted to New York's series wins and Philadelphia's losing streak, but the Bellinger injury news is fresh and could push the price toward Philadelphia if bettors react, or toward New York if they view the injury as manageable.

⚑ 9. Contrarian Check

New York Y is the lower-priced side at 36% despite a 3-game win streak and series lead over Philadelphia; the market may be overreacting to Bellinger's injury and Philadelphia's home reputation, creating potential value if New York continues momentum.

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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