Montréal Canadiens vs Philadelphia Flyers prediction, odds, win probability, and matchup analysis for April 14, 2026
Get the playerWON NHL prediction for the Montréal Canadiens at Philadelphia Flyers matchup on April 14, 2026. playerWON’s machine-learning model currently gives the Montréal Canadiens a 55.7% win probability.
🤖 Model favours Montréal Canadiens
(55.7%)
Compare market odds, fair odds, and value below.
April 14, 2026
Visitor Model vs Market
Montréal Canadiens
55.7%
Fair American: -126
Fair Decimal: 1.8
Best Market: -190 · BetOnline.ag
Market Implied: 65.5%
Edge: -9.8%
EV: -15.0%
Calculate Value →
Home Model vs Market
Philadelphia Flyers
44.3%
Fair American: +126
Fair Decimal: 2.26
Best Market: +166 · BetOnline.ag
Market Implied: 37.6%
Edge: +6.7%
EV: +17.8%
Calculate Value →
📊 Season Overview
|
|
Metric |
|
|---|---|---|
| 106 | Points | 98 |
| 0.646 | Points % | 0.598 |
| 283 | Goals For | 250 |
| 256 | Goals Against | 243 |
| 27 | Goal Differential | 7 |
| L1 | Current Streak | W3 |
🚍 Road vs 🏠 Home
|
|
Metric |
|
|---|---|---|
| 41 | Games | 41 |
| 24 | Wins | 20 |
| 56 | Points | 48 |
| 17 | Goal Differential | -2 |
🔥 Last 10 Games
|
|
Metric |
|
|---|---|---|
| 7-3-0 | Record | 7-3-0 |
| 14 | Points | 14 |
| 28 | Goals For | 36 |
| 24 | Goals Against | 25 |
| 4 | Goal Differential | 11 |
🥅 Last 10 Head-to-Head Games
|
|
H2H Summary |
|
|---|---|---|
| 5-5 | Record | 5-5 |
| 38 | Total Goals | 32 |
| +6 | Goal Differential | -6 |
| 3.8 | Goals / Game | 3.2 |
| 24.8 | Shots / Game | 30.1 |
| Date | Matchup | Score | Shots | Details |
|---|---|---|---|---|
| Dec 16, 2025 |
|
4 - 1 | 21 - 22 | View |
| Nov 4, 2025 |
|
5 - 4 | 42 - 20 | View |
| Apr 5, 2025 |
|
2 - 3 | 24 - 26 | View |
| Mar 27, 2025 |
|
4 - 6 | 30 - 30 | View |
| Oct 27, 2024 |
|
4 - 3 | 24 - 26 | View |
| Apr 9, 2024 |
|
3 - 9 | 36 - 30 | View |
| Mar 28, 2024 |
|
1 - 4 | 30 - 17 | View |
| Jan 10, 2024 |
|
2 - 3 | 19 - 39 | View |
| Mar 28, 2023 |
|
2 - 3 | 29 - 27 | View |
| Feb 24, 2023 |
|
5 - 2 | 31 - 26 | View |
This matchup prediction is generated using playerWON’s machine-learning model, incorporating team performance, recent form, and home/away effects. View full model performance to compare results across games.