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Gribaldo vs Scomparin

Sports Β· Kalshi Β· Called NO, verified WIN Β· Jul 20, 2026
View the original Kalshi market →
Verified Outcome
WIN
PillarLab called Matteo Gribaldo will NOT win (NO @ 31%).
Result: Matteo Gribaldo did not win. The call was correct.
Analyzed: Jul 20, 2026, 12:59 PM UTC
Market resolved: Jul 20, 2026, 5:02 PM UTC
Called 4h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxitfmatch/itf-mens-match/kxitfmatch-26jul20grisco

PillarLab
πŸ“‘ Fetching Kalshi data...
PillarLab
βœ… Got data from Kalshi API!0.7s
PillarLab
πŸ” Step 1: Detecting market type and category...
PillarLab
βœ… Category: ⚽ SPORTS βœ… Type: BINARY πŸ“Š Market: Gribaldo vs Scomparin πŸ“… Date: 2026-07-20T19:00:00.000Z πŸ’° Volume: $184 Current Odds: β€’ Pietro Romeo Scomparin: 73% β€’ Matteo Gribaldo: 31%7.5s
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%)15.6s
PillarLab
🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ Gribaldo vs Scomparin

Team/PlayerCurrent Odds
Pietro Romeo Scomparin73%
Matteo Gribaldo31%

2 options total

PillarLab

🎯 Best Trade

  • Action: Matteo Gribaldo β€” NO @ 31%

  • Confidence: 🟑 MEDIUM Β· Uncertainty: LOW

  • Why:

  • Gribaldo's second-serve points won (32.93%) and return points won are significantly below Scomparin's over the same period.
  • His overall clay record is less established, and the market prices him as a clear underdog despite a prior H2H win.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
Pietro Romeo Scomparin73%🟒 FAVORED
Matteo Gribaldo31%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 pegs Scomparin at 58.85% implied win probability from a bookmaker model, trailing the prediction market's 73%.

πŸ“ˆ 2. Line Movement

No line movement data is publicly tracked for this match; the market's static pricing suggests no significant late shift.

πŸ₯ 3. Injury Report

No injury or fitness concerns are publicly reported for either player; both are expected to compete at full strength.

πŸ“± 4. Reddit Sentiment

Reddit sentiment is virtually nonexistent for this matchup, as M15 tournaments rarely generate discussion; the market price is uninfluenced by crowd hype.

πŸ”’ 5. Team Stats & Form

Matteo Gribaldo defeated Pietro Romeo Scomparin 6-1, 7-6(3) in their only prior meeting (M15 Rovinj 2026), yet Scomparin's model-ranked serve/return numbers (43.98% second-serve points won vs Gribaldo's 32.93%) and stronger clay record (80–52) explain his higher market price.

🌀️ 6. Situational Factors

The match is on clay in Segrate, which favors Scomparin given his stronger clay record (80–52); Gribaldo has no clear situational advantage.

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

Handle split is not published for this match; without sharp money data, the market price stands as the sole aggregated opinion.

⏰ 8. Time Decay

LOW - No key news is pending (no new injury updates, no lineup announcements); the price is unlikely to shift before the match starts.

⚑ 9. Contrarian Check

Gribaldo's straight-sets win over Scomparin in their sole 2026 meeting is a concrete result that the market is discounting; if conditions repeat, Gribaldo could be live.

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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