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Baltimore vs Boston: Game 1

Sports Β· Kalshi Β· Called NO, verified WIN Β· Jul 22, 2026
View the original Kalshi market →
Verified Outcome
WIN
PillarLab called Baltimore will NOT win (NO @ 35%).
Result: Baltimore did not win. The call was correct.
Analyzed: Jul 22, 2026, 5:39 PM UTC
Market resolved: Jul 22, 2026, 8:02 PM UTC
Called 2h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxmlbgame/professional-baseball-game/kxmlbgame-26jul211910balbos

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: Baltimore vs Boston: Game 1 πŸ“… Date: 2026-07-21T23:10:00.000Z πŸ’° Volume: $3,404,645 Current Odds: β€’ Boston: 65% β€’ Baltimore: 35%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%)22.1s
PillarLab
🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ Baltimore vs Boston: Game 1

Team/PlayerCurrent Odds
Boston65%
Baltimore35%

2 options total

PillarLab

🎯 Best Trade

  • Action: Baltimore β€” NO @ 35%

  • Confidence: 🟑 MEDIUM Β· Uncertainty: MEDIUM

  • Why:

  • Missing starting pitcher Chris Bassitt (15-day IL).
  • Road team against a red-hot Boston squad on a 14-game win streak.
  • Lost the most recent head-to-head 6-5 on July 20 despite a close game.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
Boston65%🟒 FAVORED
Baltimore35%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 has Boston at 60% implied, market at 65% β€” both lean Boston.

πŸ“ˆ 2. PillarScore

Sample too small for confident read (only 3 games per side); Boston's wins over Baltimore in June and a close loss show quality, Baltimore's losses at home reflect home/road split but no clear trend.

πŸ₯ 3. Injury Report

Baltimore's Chris Bassitt on 15-day IL with low back discomfort, a key pitcher missing.

πŸ“± 4. Reddit Sentiment

Reddit leans Boston; game-thread hype is high on the home favorite, creating potential fade risk if overdone.

πŸ”’ 5. Team Stats & Form

Boston 14-game win streak, 10-0 in last 10; Baltimore 7-3 but lost 6-5 to Boston on July 20.

🌀️ 6. Situational Factors

Boston at home (Fenway), Game 1 of doubleheader, 1:35 PM ET, no weather issues; Baltimore on the road.

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

84% of handle on Boston (public heavy), 12% of bets on Baltimore β€” public money inflating Boston's price.

⏰ 8. Time Decay

MEDIUM - Bassitt's injury already priced, but any lineup change could shift; market looks stable.

⚑ 9. Contrarian Check

Baltimore's earlier 2-0 at Fenway (June 2 & 4) shows they can win here, but current form and injury gap favor Boston.

PillarLab

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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