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San Diego FC vs Seattle Prediction (Oct 25)

Sports Β· Kalshi Β· Called NO, not settled yet PENDING Β· Oct 25, 2026
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PillarLab called San Diego FC will NOT win (NO @ 41%).
The market has not settled yet. This page updates with the real result automatically, win or loss.
Analyzed: Oct 25, 2026, 2:50 AM UTC

https://kalshi.com/markets/kxmlsgame/san-diego-fc-vs-seattle/KXMLSGAME-26OCT24SDSEA?utm_source=kalshiapp_eventdetails

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πŸ“‘ Fetching Kalshi data...
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βœ… Got data from Kalshi API!0.8s
PillarLab
πŸ” Step 1: Detecting market type and category...
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βœ… Category: ⚽ SPORTS βœ… Type: MULTI_CANDIDATE πŸ“Š Market: San Diego FC vs Seattle πŸ“… Date: 2026-10-25T05:30:00.000Z πŸ’° Volume: $85,561 Current Odds: β€’ San Diego FC: 41% β€’ Seattle: 36% β€’ Tie: 24%3.2s
PillarLab
⏳ Step 2: Gathering intelligence (parallel)...
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βœ… Intelligence gathered: Sportsbook Consensus (50+ books, vig removed) Β· Injury Report Β· Team Stats Β· Candidate Dossiers (case for + case against, every candidate β‰₯2%)27.0s
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🧠 Step 3: Deep Analysing across all 9 pillars β€” hang tight, ~20s...
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πŸ“Š Market Summary

⚽ San Diego FC vs Seattle

Team/PlayerCurrent Odds
San Diego FC41%
Seattle36%
Tie24%

3 options total

PillarLab

🎯 My Call

Seattle to win

  • Confidence: 🟑 MEDIUM

⚠️ Higher risk. I'm 13 points away from the sportsbooks on Seattle (me 46%, them 33%). That's a real disagreement, so size it carefully.

San Diego are getting paid on their head-to-head edge, but they haven't won in eight and keep conceding four or more, so I'm on Seattle.

πŸ‘‰ Buy San Diego FC NO at 59Β’

My fair price is 69Β’, so that's 10Β’ of edge.

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

  • How I'd run it: Normal size on Seattle. Add if Seattle's XI is confirmed intact; get out if Morris or GΓ³mez is ruled out.
OptionMy priceMarket
San Diego FC31Β’41Β’
Seattle46Β’36Β’
Tie23Β’24Β’

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

  • San Diego have gone eight MLS matches without a win and sit 12th in the West on 34 points.
  • Seattle are seventh on 42 points and unbeaten in 10 of their last 11, which makes them the form side.
  • San Diego have conceded four or more in seven straight, so their back line is the leak I'm trading.
  • The 2-0-1 head-to-head is real history, but it doesn't outweigh where both teams are right now.
  • What kills it: Dreyer and Bakambu get San Diego a home win, since Dreyer has a goal or assist in five straight and San Diego already beat Seattle twice in this series.

  • What flips me: Confirmed news that Seattle's Morris or GΓ³mez is out of the lineup.

PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketBook ConsensusGapVerdict
San Diego FC41%43%-2 ptsβšͺ FAIR PRICE
Seattle36%32.9%+3.1 ptsβšͺ FAIR PRICE
Tie24%24.5%-0.5 ptsβšͺ FAIR PRICE

Gap is market minus book consensus. Negative means the market is cheaper than the books, that is the discount we look for. 5 or more points cheaper is where the edge lives. 5 or more points over is overpaying.

PillarLab

πŸ”¬ 9-Pillar Analysis (Sharp Trading Method)

🏦 1. Book Consensus

Consensus of 9 sportsbooks found by live search, vig removed: San Diego FC 43%, Seattle 32.9%, Tie 24.5%. Spread across the top books on San Diego FC: 0.8 pts (books agree).

πŸ“ˆ 2. Form & Head-to-Head

San Diego FC are winless in their last eight MLS matches with a DDLDD recent run, while Seattle are unbeaten in 10 of their last 11 but with 9 draws in 30 games, and San Diego lead the head-to-head 2-0-1 since 04/05/2025 with Seattle yet to win a meeting.

πŸ₯ 3. Injury Report

No injury or suspension information is published for San Diego FC, and Seattle's Paul Rothrock is listed as questionable with a hamstring issue.

🧾 4. Starters & Lineups

Seattle's listed XI (Morris, RusnΓ‘k, Roldan, GΓ³mez) and San Diego's projected XI (Dreyer, Bakambu, Achouri, Godoy, Pirani) are referenced, but the sources do not say when either was announced relative to the price.

🧠 Off the Record

No framework. Just what I actually think.

Here's what the crowd is doing. They see San Diego ahead in the head-to-head and they see the Snapdragon crowd, and they're paying for the name. But look at the table. San Diego are 12th in the West on 34 points, eight games without a win, and they've given up four or more in seven straight. Seattle are seventh on 42 points and unbeaten in ten of eleven. That's the form side, and nobody wants to say it out loud.

The tie is the part I respect. San Diego's form guide is DDLDD, so draws are how they've been earning points, and I give the draw real weight. It's not enough to fight the form though.

The head-to-head is the number I don't trust. The sources disagree on the series, one says 2-0-1 and another says 1-1-1, so I'm not building the case on it. I'm building it on who's winning right now.

What I'm doing: buying Seattle at normal size, with a small piece of the tie as a hedge. The caveat is that the only injury note I have is Paul Rothrock listed as questionable (hamstring) for Seattle, and nobody has confirmed anything beyond that for either side, so I'll be checking lineups before I add.

πŸ“° 5. Recent News (48h)

Nothing material in the last 48 hours beyond the Rothrock hamstring flag is confirmed in the intelligence.

🌀️ 6. Situational Factors

San Diego appear to be the home side at Snapdragon Stadium, while rest, travel, and weather details are not provided.

πŸ”€ 7. Line Movement

Line movement isn't available for this game: the prices came from live web search, not our snapshot feed.

πŸ•’ 8. What the Books May Not Have Priced

LOW - no dated late scratch, starter change, or suspension appears in the intelligence that the books would not already have absorbed.

⚑ 9. Why the Other Side Is Priced Where It Is

Seattle's 36% reflects a head-to-head record with zero wins in three meetings against San Diego, and an unbeaten run built largely on draws, with only 11 wins from 30 games.

PillarLab

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

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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