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San Luis vs Queretaro

Sports Β· Kalshi Β· Called NO, verified LOSS Β· Oct 21, 2026
View the original Kalshi market →
Verified Outcome
LOSS
PillarLab called San Luis will NOT win (NO @ 41%).
Result: San Luis won. The call was wrong. Final result: San Luis won.
Analyzed: Oct 21, 2026, 1:07 AM UTC
Market resolved: Oct 21, 2026, 2:59 AM UTC
Called 2h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxligamxgame/san-luis-vs-queretaro/KXLIGAMXGAME-26OCT20ASLQUE?utm_source=kalshiapp_eventdetails

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: MULTI_CANDIDATE πŸ“Š Market: San Luis vs Queretaro πŸ“… Date: 2026-10-21T04:00:00.000Z πŸ’° Volume: $322,029 Current Odds: β€’ San Luis: 41% β€’ Queretaro: 32% β€’ Tie: 28%2.8s
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%)30.3s
PillarLab
🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
PillarLab

πŸ“Š Market Summary

⚽ San Luis vs Queretaro

Team/PlayerCurrent Odds
San Luis41%
Queretaro32%
Tie28%

3 options total

PillarLab

🎯 My Call

Queretaro to win

  • Confidence: 🟑 MEDIUM

⚠️ High risk: bold call. I'm 23 points away from the market on Queretaro (me 55%, them 32%). They're usually right, so if you play this, keep it small.

Queretaro is the side in form, and San Luis is getting paid on a February result.

πŸ‘‰ Buy San Luis NO at 59Β’

My fair price is 77Β’, so that's 18Β’ of edge.

Why NO? A draw kills a Queretaro YES bet. San Luis NO pays if Queretaro wins AND if it's a draw. Two ways to cash instead of one.

  • How I'd run it: Normal size. I'd add if the lineup confirms Homenchenko starts and get out if he's ruled out close to kickoff.
OptionMy priceMarket
San Luis23Β’41Β’
Queretaro55Β’32Β’
Tie22Β’28Β’

I set my prices from the research alone, before I saw the market. These are pre-game prices. If the game is already live, the score beats my numbers.

Why

  • Queretaro have won 8 of their last 15 and are unbeaten in five away league games, W4 D1 L0.
  • San Luis have won only 4 of their last 15 and are conceding 2.00 a game.
  • The crowd is pricing the 3-0 win San Luis got in February, and the form underneath it has moved the other way.
  • Homenchenko has 5 league goals and has played every minute of the last three, so Queretaro's main attacker is fit and in rhythm.
  • What kills it: San Luis's home record against Queretaro is the one thing that beats me, and it's been good enough to win the matchup at their place.

  • What flips me: Confirmation that Homenchenko is out would make me switch to San Luis or the tie.

PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketRead
San Luis41%πŸ”΄ OVERPRICED
Queretaro32%🟒 UNDERPRICED
Tie28%βšͺ FAIR

Verdicts compare my own read against the market price, 🟒 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 has no published price for this exact fixture; adjacent matches carry odds but do not apply here, so the 41/32/28 market split stands as the only price reference and shows no visible drift.

πŸ“ˆ 2. PillarScore

Queretaro's recent form reads as the stronger side on opponent quality: they sit 2nd with 6W-2D-2L and +7 GD, conceded only twice in their last five, while San Luis sit 16th with -7 GD and lost to Santos Laguna in Jornada 11, so the market's 41% lean toward San Luis is not supported by current form.

πŸ₯ 3. Injury Report

No injury or suspension report for either side is available for this match; the only availability note is that Santiago Homenchenko (Queretaro) has played every minute of the last three matches, from an adjacent fixture.

πŸ“± 4. Reddit Sentiment

No Reddit sentiment data was gathered for this fixture, so no crowd over-hype can be identified.

🧠 Off the Record

No framework. Just what I actually think.

Nobody has a clean read on this one. There's no injury report, no lines for this game, and the tables disagree on where both teams sit. What I do trust is the form. Queretaro have won 8 of their last 15, and they've been hard to beat away from home, unbeaten in five with four wins. San Luis have won 4 of their last 15 and are shipping two goals a game. The crowd is leaning on the 3-0 San Luis won in February, and that's a real result, but it's one game and the form since has gone the other way.

The head-to-head is the thing people will use to argue for San Luis. The records floating around don't agree on how good San Luis have been at home against Queretaro, so I'm not building anything on that number. Six meetings split 3-3 is the number I'll respect.

The tie is live because this matchup has been tight, but I'd need a reason to think neither side scores, and nothing in the research gives me that. I'm buying Queretaro at normal size and I'll add if the lineup confirms Homenchenko.

πŸ”’ 5. Team Stats & Form

San Luis's recent results are the weak point: from the adjacent Toluca preview, they won only 4 of their last 15, averaged 1.60 scored and 2.00 conceded, and are described as sitting near the foot of the table; Queretaro are the league's stronger defence per the same sources.

🌀️ 6. Situational Factors

Venue favours San Luis historically (5 wins in 7 home meetings with Queretaro per the tie dossier), but this is an H2H stat from older meetings and does not outweigh current table position and form.

πŸ”€ 7. Line Movement

No opening-versus-current line movement is present for this fixture, so no sharp-money signal can be read.

⏰ 8. Time Decay

LOW - no injury news or lineup announcements are visible, and no pending news is identifiable that would move the price.

⚑ 9. Contrarian Check

The main contrarian risk is that San Luis's 3-0 February win and strong home H2H record (3 wins from last 6 meetings) could reassert itself at home, and the draw price at 28% could be understated if the match stays tight.

PillarLab

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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
  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
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  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