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San Diego vs Arizona

Sports Β· Kalshi Β· Called YES, verified LOSS Β· Aug 5, 2026
View the original Kalshi market →
Verified Outcome
LOSS
PillarLab called Arizona to win (YES @ 6%).
Result: Arizona did not win. The call was wrong.
Analyzed: Aug 5, 2026, 4:06 AM UTC
Market resolved: Aug 5, 2026, 4:37 AM UTC
Called 1h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxmlbgame/professional-baseball-game/KXMLBGAME-26AUG042140SDAZ?utm_source=kalshiapp_eventpage&referral=43d930ea-fdc3-4a27-b54e-154feacc3d32

PillarLab
πŸ“‘ Fetching Kalshi data...
PillarLab
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: San Diego vs Arizona πŸ“… Date: 2026-08-05T01:40:00.000Z πŸ’° Volume: $4,097,353 Current Odds: β€’ San Diego: 95% β€’ Arizona: 6%10.7s
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· Line Movement Β· Candidate Dossiers (case for + case against, every candidate β‰₯2%)11.6s
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🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ San Diego vs Arizona

Team/PlayerCurrent Odds
San Diego95%
Arizona6%

2 options total

PillarLab

🎯 Best Trade

  • Action: Arizona β€” YES @ 6%

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

  • Why β€” the case against San Diego:

  • San Diego won the Aug 3 series opener vs Arizona, which seems to anchor the price.
  • San Diego is near .500 and within striking distance in the NL West, not a dominant club.
  • No named injury or form edge justifies a 95% quote for a team that split the series and is on the road.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
San Diego95%πŸ”΄ OVERPRICED
Arizona6%🟒 UNDERPRICED

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 this roughly as a San Diego 56% / Arizona 44% game based on recent matchup pricing, a far narrower spread than the 95/6 prediction-market quote.

πŸ“ˆ 2. PillarScore

PillarScore sample is too small to be reliable (San Diego 57.1 over 6 games, Arizona 56.4 over 10), but the raw logs show a split series with Arizona 6-4 and San Diego 4-2 against similar mid-tier competition.

πŸ₯ 3. Injury Report

No confirmed injury/disqualifier on either side was found, only a partial Padres IL note and Merrill Kelly listed for Arizona, so health does not explain a 95% price.

πŸ“± 4. Reddit Sentiment

Reddit game threads center on bullpen usage and late-inning swings with no durable consensus, suggesting the 95% San Diego price is not backed by a measurable public edge.

πŸ”’ 5. Team Stats & Form

Arizona holds the better recent record (~55-51 vs ~50-53) and run differential in late-July/early-August snapshots and won the most recent head-to-head on Aug 4, 2026.

🌀️ 6. Situational Factors

Arizona is at home at Chase Field for this series with home-field advantage, while San Diego is on the road in a tight NL West race.

πŸ”€ 7. Line Movement

Partial July line movement showed Arizona from +127 to +118, indicating some money toward the underdog, though no confirmed current steam line was found.

⏰ 8. Time Decay

MEDIUM - the 95% quote looks stale against the same-series split and Arizona's home win; probable-pitcher and lineup news for the Aug 5 game could compress it sharply.

⚑ 9. Contrarian Check

The market is too harsh on Arizona: the obvious loser has the better record, home field, and just beat San Diego on Aug 4.

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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.

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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