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Kos vs Sroka

Sports Β· Kalshi Β· Called NO, verified WIN Β· Jul 27, 2026
View the original Kalshi market →
Verified Outcome
WIN
PillarLab called Dorian Kos will NOT win (NO @ 25%).
Result: Dorian Kos did not win. The call was correct.
Analyzed: Jul 27, 2026, 5:00 AM UTC
Market resolved: Jul 27, 2026, 11:47 AM UTC
Called 7h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxitfmatch/itf-mens-match/KXITFMATCH-26JUL27KOSSRO?utm_source=kalshiapp_eventpage&referral=1d67bb94-a005-4219-acfd-7d3b12e4e6a0

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: Kos vs Sroka πŸ“… Date: 2026-07-27T16:00:00.000Z πŸ’° Volume: $10 Current Odds: β€’ Milosz Sroka: 72% β€’ Dorian Kos: 25%9.0s
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%)26.0s
PillarLab
🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ Kos vs Sroka

Team/PlayerCurrent Odds
Milosz Sroka72%
Dorian Kos25%

2 options total

PillarLab

🎯 Best Trade

  • Action: Dorian Kos β€” NO @ 25%

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

  • Why:

  • Poor recent form: lost 9 of last 15 matches, including heavy losses (6-0,6-1 style).
  • 2026 ITF record 0-2, showing lack of success at this level.
  • Inconsistent results; no winning streak in logged matches.
  • Despite sportsbook odds suggesting near-even, his match log reveals lack of competitiveness.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
Milosz Sroka72%🟒 FAVORED
Dorian Kos25%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 Sroka at 50.8% and Kos at 49.2%, reflecting a near-even contest.

πŸ“ˆ 2. PillarScore

PillarScore read is inconclusive due to small sample (7 games for Sroka, 15 for Kos); both have mixed results and no clear form edge.

πŸ₯ 3. Injury Report

No known injuries for either player; both are fit to play.

πŸ“± 4. Reddit Sentiment

No Reddit discussion available for this niche event; no hype signal.

πŸ”’ 5. Team Stats & Form

Kos has lost 9 of his last 15 matches, including blowout losses (6-3,6-0 to Iakubenko; 6-1,6-2 to Fondriest); Sroka, despite a 3-3 record, has straight-set wins over weak opponents.

🌀️ 6. Situational Factors

No situational edge; both players competing at the same ITF Futures clay level.

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

No public/sharp money flow data available for this match.

⏰ 8. Time Decay

MEDIUM - low volume (10) means the price may not reflect the most recent form, but no stale news identified.

⚑ 9. Contrarian Check

The biggest risk is that the betting market sees this as near-even, suggesting Sroka at 72% may be overvalued if the prediction market lags.

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.

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