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San Jose vs Orlando

Sports Β· Kalshi Β· Called YES, verified LOSS Β· Jul 23, 2026
View the original Kalshi market →
Verified Outcome
LOSS
PillarLab called San Jose to win (YES @ 60%).
Result: San Jose did not win. The call was wrong.
Analyzed: Jul 23, 2026, 2:32 AM UTC
Market resolved: Jul 23, 2026, 4:43 AM UTC
Called 2h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxmlsgame/major-league-soccer-game/KXMLSGAME-26JUL22SJORL?utm_source=kalshiapp_eventpage&referral=1d67bb94-a005-4219-acfd-7d3b12e4e6a0

PillarLab
πŸ“‘ Fetching Kalshi data...
PillarLab
βœ… Got data from Kalshi API!0.6s
PillarLab
πŸ” Step 1: Detecting market type and category...
PillarLab
βœ… Category: ⚽ SPORTS βœ… Type: MULTI_CANDIDATE πŸ“Š Market: San Jose vs Orlando πŸ“… Date: 2026-07-23T05:30:00.000Z πŸ’° Volume: $200,750 Current Odds: β€’ San Jose: 60% β€’ Orlando: 23% β€’ Tie: 20%5.0s
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%)20.4s
PillarLab
🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ San Jose vs Orlando

Team/PlayerCurrent Odds
San Jose60%
Orlando23%
Tie20%

3 options total

PillarLab

🎯 Best Trade

  • Action: San Jose β€” YES @ 60%

  • Confidence: 🟑 MEDIUM Β· Uncertainty: LOW

  • Why: this is a 3-way match-odds market β€” a draw is a real, common outcome in football (roughly 25-27% of matches historically), so the bar for calling the favorite outright is higher than in a straight two-way bet. San Jose clears it here at 60%, well clear of Orlando (23%), the next-most-likely outcome.

  • Home field at PayPal Park with a recent 3-1 win over Portland Timbers (2026-07-22).
  • Orlando missing striker Duncan McGuire (shoulder), reducing their attacking depth.
  • San Jose's defensive absences (four out) are already factored into the price, and the team has shown resilience at home.
  • Note: this is a group-stage/league fixture β€” there is no extra time here. A draw at the end of 90 minutes IS the final result for this match, not a step toward a decider.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
San Jose60%🟒 FAVORED
Orlando23%πŸ”΄ TRAILING
Tie20%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 is not publicly available for this specific matchup, but the prediction market's own pricing at 60/23/20 is the dominant market read.

πŸ“ˆ 2. PillarScore

PillarScore sample is insufficient (5 games each) for a confident read; San Jose (1W-3L-1D) has a recent 3-1 win over Portland, while Orlando (2W-1D-2L) arrives off a 2-6 loss to Cincinnati, but the sample is too small to draw a strong form edge.

πŸ₯ 3. Injury Report

San Jose out Noel Buck (lower body), DeJuan Jones (lower body), Benji Kikanovic (upper body), Daniel Munie (lower body); Orlando out Joran Gerbet (thigh) and Duncan McGuire (shoulder) – both sides have notable absences, especially San Jose's defensive depth and Orlando's striker.

πŸ“± 4. Reddit Sentiment

Reddit discussion was light pre-match chatter without a sharp consensus, consistent with the market pricing; no strong over-hype detected.

πŸ”’ 5. Team Stats & Form

San Jose's recent 3-1 win over Portland breaks a prior losing streak, but Orlando owns a 5-0 blowout win over San Jose in a prior meeting; San Jose's home form and Orlando's heavy 2-6 loss to Cincinnati temper the favorite's case.

🌀️ 6. Situational Factors

Match at PayPal Park gives San Jose home-field advantage; both teams face travel (Orlando to San Jose), but no extreme travel or hostile venue noted.

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

Handle split data not publicly available for this matchup; market pricing shows no visible drift from the initial 60/23/20 split.

⏰ 8. Time Decay

LOW – all known injuries and form are already priced in; no pending lineup news to cause a repricing.

⚑ 9. Contrarian Check

Tie at 20% may be undervalued given the recent 1-1 draw between these sides and both teams' attacking absences (McGuire for Orlando, defensive depth for San Jose), but Orlando's 5-0 win shows blowout risk.

PillarLab

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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