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Charoenphon vs Hirose

Sports Β· Kalshi Β· Called NO, verified WIN Β· Jul 20, 2026
View the original Kalshi market →
Verified Outcome
WIN
PillarLab called Yuya Hirose will NOT win (NO @ 16%).
Result: Yuya Hirose did not win. The call was correct.
Analyzed: Jul 20, 2026, 4:01 AM UTC
Market resolved: Jul 20, 2026, 5:07 AM UTC
Called 1h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxitfmatch/itf-mens-match/kxitfmatch-26jul19chahir

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: Charoenphon vs Hirose πŸ“… Date: 2026-07-20T07:00:00.000Z πŸ’° Volume: $4,032,634 Current Odds: β€’ Yuttana Charoenphon: 84% β€’ Yuya Hirose: 16%6.1s
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· Line Movement Β· Handle Split (Public vs Sharp) Β· Candidate Dossiers (case for + case against, every candidate β‰₯2%)11.3s
PillarLab
🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ Charoenphon vs Hirose

Team/PlayerCurrent Odds
Yuttana Charoenphon84%
Yuya Hirose16%

2 options total

PillarLab

🎯 Best Trade

  • Action: Yuya Hirose β€” NO @ 16%

  • Confidence: 🟑 MEDIUM Β· Uncertainty: LOW

  • Why:

  • Lower win rate (50.0%) and negative match record (13-13) in the past year.
  • No head-to-head or recent upset wins surfaced to suggest a price mismatch.
  • Playing in a qualifying round where the favorite's edge is typically respected.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
Yuttana Charoenphon84%🟒 FAVORED
Yuya Hirose16%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 Charoenphon as the clear favorite, though exact odds weren't surfaced; the market pricing aligns with this.

πŸ“ˆ 2. Line Movement

Line-movement data is not published for this event level; the current 84%-16% split shows no visible drift.

πŸ₯ 3. Injury Report

No injury reports were found for either player; fitness is not a factor here.

πŸ“± 4. Reddit Sentiment

Reddit has no discussion of this specific matchup; crowd sentiment is not influencing this price.

πŸ”’ 5. Team Stats & Form

Charoenphon holds a modest recent-form edge: 13-10 (56.5%) vs Hirose's 13-13 (50.0%) over the past year at ITF level, justifying his favorite status.

🌀️ 6. Situational Factors

The match is M15 Bali qualifying; Charoenphon as a Thai player may have regional familiarity, but no major travel or weather factors surfaced.

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

Handle-split data is not available for ITF qualifying; public vs sharp money cannot be assessed.

⏰ 8. Time Decay

LOW - No injury updates or lineup announcements are expected; the current pricing is stable.

⚑ 9. Contrarian Check

The contrarian take is Hirose's 50% win rate showing he is competitive at this level, but the market correctly prices the small form gap against him.

PillarLab

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

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9-Pillar Analysis ~30s

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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