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St. Louis vs New Jersey Prediction (Nov 5)

Sports Β· Kalshi Β· Called NO, not settled yet PENDING Β· Nov 5, 2026
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PillarLab called New Jersey will NOT win (NO @ 68%).
The market has not settled yet. This page updates with the real result automatically, win or loss.
Analyzed: Nov 5, 2026, 10:22 PM UTC

https://kalshi.com/markets/kxnhlgame/stl-blues-vs-nj-devils/KXNHLGAME-26NOV05STLNJ?utm_source=kalshiapp_eventdetails

PillarLab
πŸ“‘ Fetching Kalshi data...
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βœ… Got data from Kalshi API!0.8s
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πŸ” Step 1: Detecting market type and category...
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βœ… Category: ⚽ SPORTS βœ… Type: BINARY πŸ“Š Market: St. Louis vs New Jersey πŸ“… Date: 2026-11-06T03:00:00.000Z πŸ’° Volume: $60,150 Current Odds: β€’ New Jersey: 68% β€’ St. Louis: 32%1.8s
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

⚽ St. Louis vs New Jersey

Team/PlayerCurrent Odds
New Jersey68%
St. Louis32%

2 options total

PillarLab

🎯 My Call

New Jersey to win

  • Confidence: 🟑 MEDIUM

New Jersey is the right side at home, but the Devils' goalie and special teams make this number shorter than it should be.

πŸ‘‰ Buy New Jersey NO at 32Β’

My fair price is 38Β’, so that's 6Β’ of edge.

Why NO if I think New Jersey wins? The market has New Jersey at 68Β’, higher than my 62%. NO costs 32Β’ and pays $1 if New Jersey loses.

  • How I'd run it: Small to normal size. I add if Hellebuyck is confirmed in net, and I get out if Allen is announced and the Devils take early penalties.
OptionMy priceMarket
New Jersey62Β’68Β’
St. Louis38Β’32Β’

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

  • New Jersey is 10-5 against a Blues team sitting around 7-7-1, and the gap in shots is 32.6 to 26.4 per game.
  • The Devils score 3.43 a game against 3.14 for St. Louis, and that volume edge travels.
  • St. Louis is thin on the back end with Mailloux, Krug and Dube on IR and Parayko day-to-day.
  • Hellebuyck is 2-0-0 with a 1.50 GAA for New Jersey, which covers up a weak 5-3 Allen line if he starts.
  • What kills it: Hofer starts for St. Louis, the Blues power play at 23.7% punishes a 70.0% Devils penalty kill, and the game gets decided on special teams.

  • What flips me: News that Allen starts for New Jersey while Hofer starts for St. Louis, plus Rodrigues and Mantha both out.

PillarLab

πŸ“ˆ Where the Market Stands

OptionMarketBook ConsensusGapVerdict
New Jersey68%62.8%+5.2 ptsπŸ”΄ OVERPAYING
St. Louis32%37.2%-5.2 pts🟒 DISCOUNT

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 7 sportsbooks found by live search, vig removed: New Jersey 62.8%, St. Louis 37.2%. Spread across the top books on New Jersey: 1.7 pts (books agree).

πŸ“ˆ 2. Form & Head-to-Head

New Jersey has won six of its last seven, most recently a 4-3 shootout win over Edmonton on Nov. 4 after blowing a 3-1 lead, and St. Louis has won two straight (5-4 at Detroit Oct. 31, 4-1 vs. Boston Oct. 29), though one source says its most recent game was a 0-for-6 power-play night against the Rangers, so the sequence is unconfirmed. Head-to-head records conflict between sources (one shows New Jersey 6-4-0, another says St. Louis took 6 of the last 10), so no reliable head-to-head read exists.

πŸ₯ 3. Injury Report

The primary injury feed returned no information, so coverage is absent rather than a clean bill of health; one candidate dossier lists New Jersey's Evan Rodrigues and Anthony Mantha as day-to-day with Seamus Casey on IR, and St. Louis's Logan Mailloux and Torey Krug on IR with Dillon Dube on IR (est. Dec. 4) and Colton Parayko day-to-day, but none of this is confirmed by an official report.

🧾 4. Starters & Lineups

No starting lineup or goalie announcement for this game was found in the intelligence, so the listed goalies (Connor Hellebuyck for New Jersey, Joel Hofer or Jordan Binnington for St. Louis) cannot be confirmed as the starters, and the announcement timing relative to the line is unknown.

🧠 Off the Record

No framework. Just what I actually think.

The crowd sees 10-5 against a team around .500 and pays up for the home name. I get the lean, but the Devils are not clean. Their penalty kill is 70.0%, and St. Louis has a 23.7% power play. That is the real danger in this game.

The goalie matchup is where this gets decided. Hofer at 2.42 and .917 is the better line on paper than Allen at 3.11 and .887. If Hellebuyck is the one in net for New Jersey, that flips and the Devils are a clear buy. Binnington at .846 is a gift to anybody shooting 32.6 times a game.

St. Louis also has a pile of guys on IR, which hurts in a road game. I do not love the price, but I'd rather be on the side with the shot volume and the home ice. My move is New Jersey, small, and I size up once the starters are confirmed.

πŸ“° 5. Recent News (48h)

Nothing material in the last 48 hours was found beyond the Nov. 4 Edmonton result.

🌀️ 6. Situational Factors

New Jersey is at home at Prudential Center, but no home-record split or rest and travel data is given for either side, so situational edges can't be quantified.

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 confirmed starters, no injury report, and no line-movement snapshot exist for this game, so there is no identifiable post-move news the books may have missed.

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

The main risk to the trailing St. Louis side is that its own goaltending and depth problems are real and not fully resolved, while the one favorable data point (Hofer's numbers) may not be the starter.

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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