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Prozorova vs Eala

Sports Β· Kalshi Β· Called NO, verified LOSS Β· Sep 24, 2026
View the original Kalshi market →
Verified Outcome
LOSS
PillarLab called Tatiana Prozorova will NOT win (NO @ 35%).
Result: Tatiana Prozorova won. The call was wrong. Final result: Tatiana Prozorova won.
Analyzed: Sep 24, 2026, 1:30 PM UTC
Market resolved: Sep 24, 2026, 1:52 PM UTC
Called 0h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxwtamatch/wta-tennis-match/kxwtamatch-26sep22proeal

PillarLab
πŸ“‘ Fetching Kalshi data...
PillarLab
βœ… Got data from Kalshi API!0.8s
PillarLab
πŸ” Step 1: Detecting market type and category...
PillarLab
βœ… Category: ⚽ SPORTS βœ… Type: BINARY πŸ“Š Market: Prozorova vs Eala πŸ“… Date: 2026-09-23T06:00:00.000Z πŸ’° Volume: $5,983,609 Current Odds: β€’ Alexandra Eala: 66% β€’ Tatiana Prozorova: 35%1.0s
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%)7.5s
PillarLab
🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ Prozorova vs Eala

Team/PlayerCurrent Odds
Alexandra Eala66%
Tatiana Prozorova35%

2 options total

PillarLab

🎯 Best Trade

  • Action: Tatiana Prozorova β€” NO @ 35%

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

  • Why:

  • Prozorova's hard-court record is 1-7 (12.5%), the surface this match is played on.
  • She lost 6-1 6-4 to Zakharova in the W100 Landisville first round on 4 August 2026, a recent straight-sets defeat.
  • No source names Prozorova beside any odds figure, and the 35% price itself appears nowhere in the scraped results β€” the market is thin and unconfirmed.
PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
Alexandra Eala66%βšͺ FAIR
Tatiana Prozorova35%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 Eala as the clear favorite at roughly 66%, and no independent book price for this specific Singapore Open R16 match exists to contradict that read.

πŸ“ˆ 2. PillarScore

Prozorova's recent form is genuinely live β€” 5 wins in her last 10 and a 27-14 2026 record with a W75 Maribor title β€” but the only surface-specific split available shows her at 1-7 on hard courts, which is the number that actually travels into this matchup.

πŸ₯ 3. Injury Report

No injury, withdrawal, or fatigue report exists for either player, so there is no hard disqualifier on either side and no clean bill of health stated for either.

πŸ“± 4. Reddit Sentiment

No Reddit or forum sentiment exists for this fixture β€” it is a lower-tier WTA 250 R16 match that the crowd simply is not discussing, so there is no hype signal to fade.

πŸ”’ 5. Team Stats & Form

Prozorova's 1-7 hard-court record and her 6-1 6-4 first-round loss to Zakharova at W100 Landisville on 4 August 2026 are the form cracks; Eala's only hard stat is a 41.13% vs 39.69% second-serve-points-won edge over the past six months.

🌀️ 6. Situational Factors

No bracket, rest, travel, or venue data is published for this Singapore Open R16 fixture, so situational factors cannot be scored either way.

πŸ”€ 7. Line Movement

No line-movement history is published for this match β€” it is a thin lower-tier WTA fixture, and the only prices visible are the current 66/35 split, which shows no drift.

⏰ 8. Time Decay

LOW - no pending injury news, lineup announcement, or fresh dated item exists for either player, so there is no near-term catalyst the market has yet to price.

⚑ 9. Contrarian Check

The main contrarian risk is that Prozorova's live 5-of-10 recent form and 27-14 2026 record are real and the 1-7 hard-court split is a stale or small-sample artifact, which would make the 35% price too short rather than too long.

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⚽ 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
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  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
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  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
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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