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Cozad vs Chavez

Sports Β· Kalshi Β· Called NO, verified WIN Β· Sep 23, 2026
View the original Kalshi market →
Verified Outcome
WIN
PillarLab called Ryan Cozad will NOT win (NO @ 32%).
Result: Ryan Cozad did not win. The call was correct. Final result: Tito Chavez won.
Analyzed: Sep 23, 2026, 9:37 AM UTC
Market resolved: Sep 23, 2026, 7:18 PM UTC
Called 10h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxitfmatch/cozad-vs-chavez/KXITFMATCH-26SEP22COZCHA?utm_source=kalshiapp_eventdetails

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: BINARY πŸ“Š Market: Cozad vs Chavez πŸ“… Date: 2026-09-23T00:00:00.000Z πŸ’° Volume: $3,283 Current Odds: β€’ Tito Chavez: 68% β€’ Ryan Cozad: 32%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%)2.7s
PillarLab
🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ Cozad vs Chavez

Team/PlayerCurrent Odds
Tito Chavez68%
Ryan Cozad32%

2 options total

PillarLab

🎯 Best Trade

  • Action: Ryan Cozad β€” NO @ 32%

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

  • Why:

  • Cozad has lost 10 of his last 11 matches, a documented form collapse that justifies his low price.
  • Scores24's set-by-set listing shows Cozad dropping the second set 5-6, evidence he is losing the tight moments.
  • Career head-to-head is level, so Cozad has no historical edge to offset his current slump.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
Tito Chavez68%βšͺ FAIR
Ryan Cozad32%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 Tito Chavez as the clear favorite at roughly 66-68%, with no independent book line available to sharpen that number.

πŸ“ˆ 2. PillarScore

The only form data in the file is Cozad's collapse β€” he has lost 10 of his last 11 matches β€” while Chavez's recent log is absent, so the one documented streak points hard against the trailing side.

πŸ₯ 3. Injury Report

No injury, suspension, or lineup information for either player appears in the intelligence, so this pillar is neutral and cannot be used to move the price.

πŸ“± 4. Reddit Sentiment

No Reddit sentiment data exists for this low-profile M15 Columbia SC fixture, so there is no crowd-hype signal to fade in either direction.

πŸ”’ 5. Team Stats & Form

Cozad's recent results are worse than his 32% implies β€” 10 losses in his last 11 matches β€” and Chavez holds the return-efficiency edge at 28.73% to 27.87%.

🌀️ 6. Situational Factors

No bracket, rest, travel, or venue-condition data is published for this match, so situational factors are neutral and give neither side an edge.

πŸ”€ 7. Line Movement

No line-movement history is published for this thin lower-tier tennis market; the only visible prices (Chavez 66-68%, Cozad 32-34%) show no drift.

⏰ 8. Time Decay

LOW - no pending injury, lineup, or news item is dated after the last price, so nothing is queued to reprice this market before the match.

⚑ 9. Contrarian Check

The main contrarian risk is that career head-to-head is level, so if Cozad's 10-of-11 slump is a small-sample blip rather than a real decline, the 32% could be slightly cheap.

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.

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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