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Sturm Graz vs Marseille

Sports · Kalshi · Called NO, verified LOSS · Oct 22, 2026
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Verified Outcome
LOSS
PillarLab called Sturm Graz will NOT win (NO @ 28%).
Result: Sturm Graz won. The call was wrong. Final result: Sturm Graz won.
Analyzed: Oct 22, 2026, 4:10 PM UTC
Market resolved: Oct 22, 2026, 6:39 PM UTC
Called 2h before resolution. The call was made before the outcome was known.

https://kalshi.com/markets/kxuelgame/sturm-graz-vs-marseille/KXUELGAME-26OCT22STUOLM?utm_source=kalshiapp_eventdetails

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📡 Fetching Kalshi data...
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✅ Got data from Kalshi API!0.8s
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🔍 Step 1: Detecting market type and category...
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✅ Category: ⚽ SPORTS ✅ Type: MULTI_CANDIDATE 📊 Market: Sturm Graz vs Marseille 📅 Date: 2026-10-22T19:45:00.000Z 💰 Volume: $230,889 Current Odds: • Marseille: 44% • Sturm Graz: 28% • Tie: 27%2.8s
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⏳ Step 2: Gathering intelligence (parallel)...
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✅ Intelligence gathered: Vegas Lines · Injury Report · Team Stats · Reddit Sentiment · Candidate Dossiers (case for + case against, every candidate ≥2%)27.2s
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🧠 Step 3: Deep Analysing across all 9 pillars — hang tight, ~20s...
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📊 Market Summary

⚽ Sturm Graz vs Marseille

Team/PlayerCurrent Odds
Marseille44%
Sturm Graz28%
Tie27%

3 options total

PillarLab

🎯 My Call

Marseille to win

  • Confidence: 🟡 MEDIUM

Marseille are the side that actually shoots, and Sturm Graz still haven't scored in the League Phase.

⏳ Wait: Sturm Graz NO is the play, but not at 72¢

My fair price is 70¢. It's above that now, so buy it under 70¢.

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

  • How I'd run it: Normal size. I add if Marseille's confirmed line-up has both Meïté and Paixão starting, and I get out if Sturm score first and hold their shape.
OptionMy priceMarket
Marseille46¢44¢
Sturm Graz30¢28¢
Tie24¢27¢

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

  • Sturm Graz have one point and no goals after two matchdays, blanked 3-0 by Bournemouth, so their attack is the biggest problem on the card.
  • Marseille average 1.9 shots a game against Sturm's 1.0, which is nearly double the threat.
  • Sturm are missing seven players including Weiper, Rózga and Jatta, and that depth hole matters more than their home record.
  • The ratings are close at 6.5 to 6.4, so Marseille only need a small edge, and they have it on shots and attacking quality.
  • What kills it: Sturm's five-game home unbeaten run and the 3-2 win over Marseille last time, if they find the first goal they've been missing and sit deep behind it.

  • What flips me: Confirmed news that Meïté or Paixão are out for Marseille, which removes the attacking edge I'm paying for.

PillarLab

📈 Where the Market Stands

OptionMarketRead
Marseille44%⚪ FAIR
Sturm Graz28%🔴 OVERPRICED
Tie27%⚪ FAIR

Verdicts compare my own read against the market price, 🟢 UNDERPRICED (market has it too low) · 🔴 OVERPRICED (market has it too high). The analysis below is the why.

PillarLab

🔬 9-Pillar Analysis (Sharp Trading Method)

📊 1. Vegas Line

My Vegas Line has Sturm Graz at about 28.6% before margin removal, which lines up with the 28% market price; the rest of that block is truncated, so no Marseille or draw price is confirmed.

📈 2. PillarScore

Sturm Graz have no League Phase goals after two matchdays, including a 3-0 loss at Bournemouth, but have won five straight home matches in all competitions, including 6-1 over Grazer AK; Marseille have lost both League Phase games (0-1 to Olympiacos) and sit on zero points, with a Ligue 1 away win at Angers as their most recent domestic result.

🏥 3. Injury Report

Sturm Graz are missing Daniil Khudyakov, Gizo Mamageishvili, Nelson Weiper, Otar Kiteishvili, Filip Rózga, Seedy Jatta and Emran Soglo, while Marseille are missing Pierre-Emile Højbjerg (rating 5.98).

📱 4. Reddit Sentiment

No Reddit sentiment data came back for this fixture, which is typical for a mid-tier European league-phase match that draws little fan discussion, so there is no crowd-hype read to weigh.

🧠 Off the Record

No framework. Just what I actually think.

Everybody's looking at Sturm's home record and treating it like it's the whole story. Unbeaten in five at home is nice, but they haven't scored a League Phase goal yet, they're on one point, and they're missing seven players including Weiper, Rózga and Jatta. That's a hole in the attack, not just a bad week. Marseille are zero points and in just as much trouble, so this isn't a side playing with a free hand. Højbjerg is out, which hurts, but Marseille still average 1.9 shots a game to Sturm's 1.0, and the ratings are almost identical at 6.5 to 6.4. When a home side with no goals faces a team that shoots nearly twice as much, the home crowd gets priced like it's worth more than it is. The 3-2 win over Marseille is undated in my notes, so I'm not leaning on it. I'm buying Marseille at normal size. The draw has a real shot here because both sides need points and neither wants to lose, but the shot numbers make me take the side with the cleaner attack.

🔢 5. Team Stats & Form

Sturm Graz have scored zero goals in the League Phase and average 1.0 shots per game against Marseille's 1.9, with WhoScored average ratings of 6.4 versus 6.5; Marseille's own European form is also poor, with two defeats and no points.

🌤️ 6. Situational Factors

Sturm Graz host with a five-match home unbeaten run and have collected four points at home in Europe, while Marseille travel to Graz with a squad already missing Højbjerg; rest and travel details were not provided.

🔀 7. Line Movement

No line-movement history was provided for this fixture, and the current prices show no visible drift from what was gathered.

⏰ 8. Time Decay

MEDIUM - the injury lists for both sides could still change before kickoff, and any late confirmation of Sturm Graz's absentees would matter most.

⚡ 9. Contrarian Check

The main risk to a Sturm Graz NO is their strong home form, including a 6-1 domestic win and a previous 3-2 win over Marseille, which could carry them past a side that has also been scoreless in Europe.

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⚽ Sports (9 Pillars)

  1. 1.Vegas Lines → spread, moneyline, totals
  2. 2.Line Movement → sharp money signals
  3. 3.Injury Reports → player status tracking
  4. 4.Reddit Sentiment → r/sportsbook consensus
  5. 5.Team Statistics → form, H2H, splits
  6. 6.Situational Factors → rest, travel, schedule
  7. 7.Handle Splits → public vs sharp money
  8. 8.Time Decay → late line movement
  9. 9.Contrarian Check → fade-the-public value

🌦️ Weather (9 Pillars)

  1. 1.Forecast Consensus → GFS / ECMWF / NWS agreement
  2. 2.Ensemble Spread → GEFS uncertainty range
  3. 3.Forecast Skill Decay → accuracy vs time horizon
  4. 4.Historical Base Rate → NOAA climate frequency
  5. 5.Model Divergence → where models disagree vs market
  6. 6.Micro-Climate Bias → station-specific effects
  7. 7.Storm Track → NHC cone / hurricane tracking
  8. 8.Market Pricing → implied probability vs forecast
  9. 9.Contrarian Check → overshoot / mean-reversion

💰 Finance (9 Pillars)

  1. 1.Technical Analysis → RSI, MACD, support levels
  2. 2.Fundamental Metrics → P/E, revenue, earnings
  3. 3.Smart Money → institutional flows, insider trades
  4. 4.Reddit Sentiment → WSB, r/stocks analysis
  5. 5.Macro Indicators → Fed rates, inflation, GDP
  6. 6.News Catalysts → earnings, product launches
  7. 7.Social Velocity → Twitter momentum tracking
  8. 8.Market Signals → volume, bid-ask analysis
  9. 9.Contrarian Check → overcrowded trade detection

📰 General (9 Pillars)

  1. 1.Base Rate → historical frequency analysis
  2. 2.Official Benchmarks → expert forecasts
  3. 3.Reddit Sentiment → subreddit consensus
  4. 4.Social Velocity → viral momentum tracking
  5. 5.News Recency → 24–72h breaking news
  6. 6.Source Quality → credibility scoring
  7. 7.Time Decay → resolution timeline
  8. 8.Contrarian Check → hidden edge detection

🏛️ Politics (9 Pillars)

  1. 1.Polls Analysis → polling vs market odds divergence
  2. 2.Economic Impact → GDP, unemployment affecting votes
  3. 3.Approval Ratings → candidate favorability tracking
  4. 4.Cross-Platform Markets → Polymarket vs Kalshi arbitrage
  5. 5.Breaking News → events shifting political odds
  6. 6.Regional Patterns → swing state analysis
  7. 7.Social Sentiment → Reddit, X political trends
  8. 8.Data Uncertainty → confidence intervals
  9. 9.Contrarian Risk → devil's advocate view