← Back to Track Record

Shanmugam vs Da Costa

Sports Β· Kalshi Β· Called NO, verified WIN Β· Jul 20, 2026
View the original Kalshi market →
Verified Outcome
WIN
PillarLab called Abhinav Sanjeev Shanmugam will NOT win (NO @ 31%).
Result: Abhinav Sanjeev Shanmugam did not win. The call was correct.
Analyzed: Jul 20, 2026, 5:54 AM UTC
Market resolved: Jul 20, 2026, 6:48 AM UTC
Called 1h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxitfmatch/itf-mens-match/kxitfmatch-26jul19shadac

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: BINARY πŸ“Š Market: Shanmugam vs Da Costa πŸ“… Date: 2026-07-20T12:30:00.000Z πŸ’° Volume: $1,505,316 Current Odds: β€’ Rafalentino Ali Da Costa: 71% β€’ Abhinav Sanjeev Shanmugam: 31%5.9s
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%)15.8s
PillarLab
🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ Shanmugam vs Da Costa

Team/PlayerCurrent Odds
Rafalentino Ali Da Costa71%
Abhinav Sanjeev Shanmugam31%

2 options total

PillarLab

🎯 Best Trade

  • Action: Abhinav Sanjeev Shanmugam β€” NO @ 31%

  • Confidence: 🟑 MEDIUM Β· Uncertainty: MEDIUM

  • Why:

  • Won only 4 of last 10 matches, inconsistent recent form.
  • Da Costa's superior 19-6 ITF record dwarfs Shanmugam's results.
  • First meeting; no historical edge to draw upon for an upset.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
Rafalentino Ali Da Costa71%🟒 FAVORED
Abhinav Sanjeev Shanmugam31%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

The wider betting market does not price this exact match cleanly; this prediction market's own pricing (Da Costa 71%, Shanmugam 31%) is the only verifiable line available, with no conflicting sportsbook signal detected.

πŸ“ˆ 2. Line Movement

Line-movement history for this specific match isn't published at this ITF level; the current prices show no visible drift, suggesting a stable market.

πŸ₯ 3. Injury Report

No publicly reported injuries or withdrawals for either player at this M15 level; the market prices assume full availability.

πŸ“± 4. Reddit Sentiment

Reddit discussion for an ITF M15 qualifying match is negligible; sentiment is not a factor in this pricing.

πŸ”’ 5. Team Stats & Form

Shanmugam has won 4 of his last 10 matches; Da Costa's ITF profile shows a 19-6 record, but head-to-head is 0-0, limiting direct form comparison. Da Costa's higher overall win rate justifies his favorite status.

🌀️ 6. Situational Factors

Both players compete on hard outdoor courts in Bali; no travel or rest disadvantage reported for either. The neutral venue favors Da Costa's higher ranking/record.

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

Betting-handle splits for ITF qualifying matches are not publicly reported; the current prices reflect market consensus without a known public/sharp divergence.

⏰ 8. Time Decay

MEDIUM - The match is scheduled for 20 Jul 2026, with no recent news to decay; however, the thin market could react quickly to any late withdrawal or weather update, but no such news is current.

⚑ 9. Contrarian Check

The contrarian risk is that Shanmugam's recent match volume (4/10 wins) could translate into upset potential on a hard court where he is more practiced, but the market already prices him as a clear underdog.

PillarLab

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

Stop guessing. See the edge.

Paste any Kalshi or Polymarket market. PillarLab runs a full 9-pillar analysis and hands you a Best Trade call in about 30 seconds.

Free to start Β· 10 credits Β· no card

PillarLab AI is an AI prediction-market analyzer that runs a 9-pillar analysis on Kalshi and Polymarket markets and publishes a public, settlement-verified track record of its decisive calls.

PillarLab AI
PillarLab
9-Pillar Analysis ~30s

#1 AI-Powered Prediction Market Analyzer Tool for Kalshi & Polymarket

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