FINAL: SJS 1 — NYI 2. Our Monte Carlo simulation projected SJS 2.97 - NYI 3.62 (NYI at 59.5% win probability). The spread is 1.5 and the total is 6.0.
SJS
2.97
Projected Score
VS
O/U 6.0
NYI
3.62
Projected Score
Win Probability
SJSNYI
+1.5
Spread (SJS)
6.0
Total Line
1,000
Simulations
Calibrated accuracy at this confidence: 61.7% (1,083 games)
Projected Goals Range 10th – 90th percentile
NYI
2.53.64.7
SJS
1.93.04.1
Projected
SJS 2.97 — NYI 3.62
Actual
SJS 1 — NYI 2
Pick Results
Over 6.5totalLOSS-1.00u
Game Odds
SJS ML
+110
NYI ML
-132
Puck Line
+1.5
Total
6.0
Edge Detail
SJS Edge
-7.1%
NYI Edge
+2.6%
Projected Total
6.59
+0.59 vs line
Goalie Matchup
Ilya Sorokin
30-242.54 GAA91.3% SV
Yaroslav Askarov
4-63.57 GAA88.6% SV
Special Teams
Power Play
Penalty Kill
90% Confidence: 34.4% – 46.5% home win probability
AI Intelligence Analysis
NEUTRAL -1YELLOW ZONE48.2% WR (n=56)
NYI (-130) is the road favorite against a B2B SJS team but the away favorite zone is structurally RED (46.4% WR), and NYI's 3-in-4 schedule (fatigue_factor 0.98) plus cross-country travel from NYC to SJS partially offsets SJS's B2B disadvantage.
Key Factors
- SJS on B2B: fatigue_factor 0.85 after losing 3-2 OT to STL last night
- NYI 3-in-4 schedule (fatigue_factor 0.98), traveled 2,572 miles NYC→SJS — significant travel fatigue
- SJS Askarov .896 SV% (backup tier) on B2B — performance likely to drop 0.5-1.0% SV%
- NYI Sorokin .907 SV% (average tier) — modest goalie advantage for NYI
- Away ML favorite zone: RED (46.4% WR, 948 bets) — structural headwind for NYI play
Risk Factors
- NYI cross-country travel 2,572 miles — one of the longest travel distances on today's slate
- SJS at home with crowd support despite B2B fatigue — home ice partially cancels NYI advantage
- NYI L2 losing streak, on 3-in-4 schedule — fatigue on both sides neutralizes B2B edge
B2B FATIGUERED ZONEAWAY DOG POISONGOALIE UNCONFIRMED
Edge Analysis
Moneyline
NYI 59.5%
-7.1 pts
Spread
+1.5
-7.1 pts
Total
6.0
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How this prediction was generated: This page shows output from the Olympus Bets NHL Hockey Monte Carlo engine. Each game is simulated 1,000 times using real-time team data, injury reports, and current odds. Probabilities are calibrated using Bayesian methods and sized via the Kelly Criterion. Full methodology →