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New York M vs Texas

Sports Β· Kalshi Β· Called NO, verified WIN Β· Sep 24, 2026
View the original Kalshi market →
Verified Outcome
WIN
PillarLab called New York M will NOT win (NO @ 28%).
Result: New York M did not win. The call was correct. Final result: Texas won.
Analyzed: Sep 24, 2026, 7:10 PM UTC
Market resolved: Sep 24, 2026, 9:13 PM UTC
Called 2h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxmlbgame/professional-baseball-game/kxmlbgame-26sep241435nymtex

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: MULTI_CANDIDATE πŸ“Š Market: New York M vs Texas πŸ“… Date: 2026-09-24T18:35:00.000Z πŸ’° Volume: $648,291 Current Odds: β€’ Texas: 73% β€’ New York M: 28%0.9s
PillarLab
⏳ Step 2: Gathering intelligence (parallel)...
PillarLab
βœ… Intelligence gathered: Vegas Lines Β· Injury Report Β· Team Stats Β· Reddit Sentiment Β· Candidate Dossiers (case for + case against, every candidate β‰₯2%)4.5s
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🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ New York M vs Texas

Team/PlayerCurrent Odds
Texas73%
New York M28%

2 options total

PillarLab

🎯 Best Trade

  • Action: New York M β€” NO @ 28%

  • Confidence: πŸ”΄ LOW Β· Uncertainty: LOW

  • Why:

  • New York M is 71-85 on the season, a .455 winning percentage that is well below Texas's .500 mark and consistent with a sub-30% road price.
  • The Mets' September 22 win came via a late rally after Texas had built a 4-2 lead, so the 7-2 final score flatters the Mets' actual performance in that game.
  • No injury or lineup information is available to suggest the Mets have a hidden edge that the market has missed.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
Texas73%βšͺ FAIR
New York M28%βšͺ FAIR

Verdicts compare my own read against the market price β€” 🟒 UNDERPRICED (market has it too low) Β· πŸ”΄ OVERPRICED (market has it too high). The analysis below is the why.

PillarLab

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

πŸ“Š 1. Vegas Line

My Vegas Line reads Texas as the clear favorite in this matchup, with the market pricing Texas around 73% and New York M around 28%, and no current moneyline prices for this exact September 24 game appear in the results.

πŸ“ˆ 2. PillarScore

The last-15-games log is not provided for either side, but the available recent results show Texas at 78-78 and New York M at 71-85, with the Mets winning the September 22 meeting 7-2 after a late rally, so the recent form read is mixed and does not clearly separate the sides.

πŸ₯ 3. Injury Report

No injury, suspension, or lineup-status information for either the Mets or the Rangers appears in the results, so there is no hard disqualifier from missing players on either side.

πŸ“± 4. Reddit Sentiment

Reddit sentiment is essentially absent for this specific game, with the two surfaced threads containing only subreddit rules and a general pregame comment, so there is no crowd over-hype to fade on either side.

πŸ”’ 5. Team Stats & Form

The Mets' 71-85 record and the Rangers' 78-78 record are the only team-level form data available, and the September 22 game showed a back-and-forth contest that the Mets won late, so neither side's recent results clearly contradict the market price.

🌀️ 6. Situational Factors

Situational factors are not detailed in the intelligence, but the Mets are on the road and carry the worse season record, which is consistent with their lower price.

πŸ”€ 7. Line Movement

No opening-vs-current line movement data is present for this matchup, so there is no reverse or steam move to read.

⏰ 8. Time Decay

LOW - no pending injury news, lineup announcements, or time-sensitive information is present in the intelligence, so there is little for the market to price in soon.

⚑ 9. Contrarian Check

The main contrarian risk is that the Mets' September 22 win and their late-season surge could be underrated by the market, but with no injury or lineup data to confirm a sustained edge, this is not enough to fade the favorite.

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

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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