NHL Hockey

STL vs COL Prediction

April 5, 2026

5,000 Monte Carlo simulations

FINAL: COL 2 — STL 3. Our Monte Carlo simulation projected COL 3.5 - STL 1.59 (COL at 68.7% win probability). The spread is -1.5 and the total is 6.5.

COL
3.5
Projected Score
VS O/U 6.5
STL
1.59
Projected Score
Win Probability
68.7%
31.3%
COLSTL
-1.5
Spread (COL)
6.5
Total Line
5,000
Simulations
STLCOL L4
Calibrated accuracy at this confidence: 61.7% (1,083 games)

Projected Goals Range 10th – 90th percentile

STL
0.51.62.7
COL
2.43.54.6
FINALCOL 2 — STL 3
Projected
COL 3.5 — STL 1.59
Actual
COL 2 — STL 3

Pick Results

Under 6.5totalWIN+0.87u

Game Odds

COL ML
-218
STL ML
+180
Puck Line
-1.5
Total
6.5
Model Quality68/100 (STRONG)

Edge Detail

COL Edge
+0.1%
STL Edge
-4.4%
Projected Total
5.09
-1.41 vs line

Goalie Matchup

Joel Hofer
16-82.58 GAA90.9% SV
VS
Scott Wedgewood
1-22.16 GAA91.7% SV

Special Teams

Power Play
STL
17.9%
COL
17.8%
Penalty Kill
STL
75.6%
COL
83.6%
90% Confidence: 58.4% – 79.0% home win probability

AI Intelligence Analysis

NEUTRAL -1GREEN ZONE55.0% WR (n=86)
Model's 68.7% win probability and 0.83 goal edge for COL is a HIGH_EDGE_WARNING case where market (-218, 68.6% implied) is essentially pricing model accurately — but COL's struggling goalie (.878 SV%) and rest disadvantage (B2B vs STL rest) create model-vs-reality mismatch that justifies CAUTION.

Key Factors

  • Model extreme edge 68.7% vs market 68.6% — near-perfect agreement, but this is SUSPICIOUS per first principles (agreement on extreme edge suggests model might be overstating)
  • Wedgewood (.878 SV%) is 1.0% below league average (.888) — on B2B, could drift -0.01 to .868, costing 0.3 goals
  • STL rest advantage: 2 days rest vs COL B2B — 0.15 to 0.25 goal swing favoring STL
  • COL elite offense: 3.03 xGF/60 (balanced_elite archetype) is genuine, but Makar out (-0.2 goals) reduces true offensive margin

Risk Factors

  • Goalie variance: Wedgewood could have BAD_DAY (-1.8% SV% = -0.4 goals), erasing edge. OR he could be sharp (+1.5% = +0.3 goals). Model does not account for individual game variance.
  • B2B goalie fatigue: Historical data shows SV% drops 0.005-0.010 on B2B; Wedgewood at .878 baseline could drop to .868, creating swing
  • Market agreement trap: When model and market agree on extreme edge (68%+), historical data suggests actual WR is lower — our calibration warns of this.
Sharp MoneyWith ModelMarket aligned with model (both ~68-69% implied), suggesting consensus on COL dominance, but no sharp money to differentiate.
HIGH EDGE WARNINGGOALIE STRUGGLING COLB2B GOALIE COLREST DISADVANTAGE COLREST ADVANTAGE STLELITE OFFENSE COL

Edge Analysis

Moneyline
COL 68.7%
+0.1 pts
Spread
-1.5
+0.1 pts
Total
6.5
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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 5,000 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 →

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