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San Francisco vs Pittsburgh

Sports Β· Kalshi Β· Called YES, verified WIN Β· Sep 3, 2026
View the original Kalshi market →
Verified Outcome
WIN
PillarLab called Pittsburgh to win (YES @ 61%).
Result: Pittsburgh won. The call was correct. Final result: Pittsburgh won.
Analyzed: Sep 3, 2026, 4:21 PM UTC
Market resolved: Sep 3, 2026, 7:27 PM UTC
Called 3h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxmlbgame/san-francisco-vs-pittsburgh/KXMLBGAME-26SEP031235SFPIT?utm_source=kalshiapp_eventdetails

PillarLab
πŸ“‘ Fetching Kalshi data...
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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 Francisco vs Pittsburgh πŸ“… Date: 2026-09-03T16:35:00.000Z πŸ’° Volume: $388,567 Current Odds: β€’ Pittsburgh: 61% β€’ San Francisco: 40%1.1s
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⏳ 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%)11.9s
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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 Francisco vs Pittsburgh

Team/PlayerCurrent Odds
Pittsburgh61%
San Francisco40%

2 options total

PillarLab

🎯 Best Trade

  • Action: Pittsburgh β€” YES @ 61%

  • Confidence: 🟑 MEDIUM Β· Uncertainty: LOW

  • Why: 32 sharp books price Pittsburgh between 59 and 61 (Pinnacle 60, DraftKings 60, FanDuel 60, BetMGM 60, Caesars (WH) 61, Betfair 60, BetRivers 61, Bovada 60, BetOnline 61, LowVig 61, Marathonbet 59, Unibet FR 60, Unibet NL 61, Unibet SE 61, William Hill 59, 888sport 60, Fanatics 60, BetAnySports 61, Betclic 60, Betsson 60, BetUS 60, Coolbet 60, GTbets 61, LeoVegas 61, Matchbook 60, MyBookie 60, NordicBet 60, 1xBet 59, Tipico 61, Winamax 60, Winamax FR 60, everygame 61). Consensus, vig removed: 60.3%. The market has Pittsburgh at 61%. That is the fair price. No discount, but the side is clear.

Line move since our first snapshot: Pittsburgh 59.1% to 60.3%. Money moved toward Pittsburgh.

  • Why San Francisco is priced where it is (40%):
  • Pitching is a clear weakness: allowed 13 runs in the last meeting, with Logan Webb struggling and showing fatigue.
  • Bullpen and starters look vulnerable against a hot Pirates lineup that has won three straight.
  • Despite scoring 12 runs on Sept. 1, they still lost, exposing an inability to close out games.

πŸ“ What would change this call: if the market drops Pittsburgh 5+ points under the books it becomes a HIGH-confidence discount. If the top books split by more than 5 points, or a starter is scratched, it goes to HOLD.

PillarLab

🏦 What the Books Say

Percent is the number the call runs on. American odds in parentheses are the same price, shown the way a sportsbook shows it.

BookPittsburghSan Francisco
Pinnacle60.3% (-160)39.7% (+147)
DraftKings60.3% (-171)39.7% (+141)
FanDuel60% (-166)40% (+140)
BetMGM60.2% (-170)39.8% (+140)
Caesars (WH)61.2% (-164)38.8% (+154)
Betfair60.3% (-154)39.7% (+150)
BetRivers60.9% (-175)39.1% (+145)
Bovada60.3% (-170)39.7% (+141)
BetOnline60.5% (-163)39.5% (+147)
LowVig60.5% (-163)39.5% (+147)
Marathonbet59% (-164)41% (+132)
Unibet FR60.1% (-169)39.9% (+140)
Unibet NL60.9% (-175)39.1% (+145)
Unibet SE60.9% (-175)39.1% (+145)
William Hill58.7% (-162)41.3% (+130)
888sport60.4% (-175)39.6% (+140)
Fanatics60.2% (-170)39.8% (+140)
BetAnySports60.6% (-166)39.4% (+146)
Betclic59.9% (-175)40.1% (+135)
Betsson60% (-172)40% (+137)
BetUS60% (-161)40% (+143)
Coolbet60.3% (-172)39.7% (+140)
GTbets60.6% (-163)39.4% (+148)
LeoVegas60.8% (-177)39.2% (+143)
Matchbook60.4% (-154)39.6% (+152)
MyBookie59.9% (-165)40.1% (+140)
NordicBet60% (-172)40% (+137)
1xBet59% (-152)41% (+139)
Tipico60.8% (-182)39.2% (+140)
Winamax59.8% (-172)40.2% (+135)
Winamax FR60.1% (-179)39.9% (+135)
everygame60.6% (-163)39.4% (+148)
Consensus, vig removed60.3%39.7%

32 sportsbooks priced this game. Spread across the top 32 on Pittsburgh: 1.9 pts (books agree). Best price: Pittsburgh -152 at 1xBet Β· San Francisco +154 at Caesars (WH).

PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketBook ConsensusGapVerdict
Pittsburgh61%60.3%+0.7 ptsβšͺ FAIR PRICE
San Francisco40%39.7%+0.3 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 32 sportsbooks, vig removed: Pittsburgh 60.3%, San Francisco 39.7%. Pinnacle: Pittsburgh 60.3%, San Francisco 39.7%. Spread across the top books on Pittsburgh: 1.9 pts (books agree).

πŸ“ˆ 2. Form & Head-to-Head

Pittsburgh enters on a three-game win streak (five of seven), including a 13-12 walk-off win over San Francisco on Sept. 1; San Francisco also won its prior game but lost that slugfest despite scoring 12.

πŸ₯ 3. Injury Report

No injury report is available for either team; probable starters are Blade Tidwell for San Francisco and Har Linski for Pittsburgh, with Landen Roupp and Jared Jones mentioned for a later rematch.

🧾 4. Starters & Lineups

No confirmed lineups for this game were announced in the intelligence; probable starters are Blade Tidwell (San Francisco) and Har Linski (Pittsburgh), noted before the last line move.

πŸ“° 5. Recent News (48h)

Nothing material in the last 48 hours beyond the teams' recent results and the Sept. 1 slugfest.

🌀️ 6. Situational Factors

San Francisco is on the road; Pittsburgh is at home, having won three straight and five of seven, with no back-to-back or weather concerns noted.

πŸ”€ 7. Line Movement

Since our first snapshot (2026-09-02 20:05 UTC): Pittsburgh 59.1% to 60.3%, San Francisco 41% to 39.7%. Money moved toward Pittsburgh.

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

LOW - No late scratches or lineup changes dated after the last line move appear in the intelligence.

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

San Francisco's price reflects a pitching staff that allowed 13 runs in the last meeting, with Logan Webb showing fatigue and the bullpen vulnerable, despite an explosive offense that scored 12.

PillarLab

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

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9-Pillar Analysis ~30s

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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.

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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