Montréal Canadiens vs New York Islanders prediction, odds, win probability, and matchup analysis for April 12, 2026
Get the playerWON NHL prediction for the Montréal Canadiens at New York Islanders matchup on April 12, 2026. playerWON’s machine-learning model currently gives the New York Islanders a 58.9% win probability.
🤖 Model favours New York Islanders
(58.9%)
Compare market odds, fair odds, and value below.
April 12, 2026
Visitor Model vs Market
Montréal Canadiens
41.1%
Fair American: +143
Fair Decimal: 2.43
Best Market: -102 · BetOnline.ag
Market Implied: 50.5%
Edge: -9.4%
EV: -18.6%
Calculate Value →
Home Model vs Market
New York Islanders
58.9%
Fair American: -143
Fair Decimal: 1.7
Best Market: -108 · BetRivers
Market Implied: 51.9%
Edge: +7.0%
EV: +13.4%
Calculate Value →
📊 Season Overview
|
|
Metric |
|
|---|---|---|
| 106 | Points | 91 |
| 0.646 | Points % | 0.555 |
| 283 | Goals For | 233 |
| 256 | Goals Against | 241 |
| 27 | Goal Differential | -8 |
| L1 | Current Streak | L3 |
🚍 Road vs 🏠 Home
|
|
Metric |
|
|---|---|---|
| 41 | Games | 41 |
| 24 | Wins | 22 |
| 56 | Points | 46 |
| 17 | Goal Differential | -2 |
🔥 Last 10 Games
|
|
Metric |
|
|---|---|---|
| 7-3-0 | Record | 3-7-0 |
| 14 | Points | 6 |
| 28 | Goals For | 24 |
| 24 | Goals Against | 35 |
| 4 | Goal Differential | -11 |
🥅 Last 10 Head-to-Head Games
|
|
H2H Summary |
|
|---|---|---|
| 5-5 | Record | 5-5 |
| 35 | Total Goals | 32 |
| +3 | Goal Differential | -3 |
| 3.5 | Goals / Game | 3.2 |
| 28.3 | Shots / Game | 31.5 |
| Date | Matchup | Score | Shots | Details |
|---|---|---|---|---|
| Mar 21, 2026 |
|
3 - 7 | 22 - 36 | View |
| Feb 26, 2026 |
|
4 - 3 | 26 - 24 | View |
| Mar 20, 2025 |
|
3 - 4 | 41 - 25 | View |
| Dec 3, 2024 |
|
1 - 2 | 31 - 27 | View |
| Oct 19, 2024 |
|
3 - 4 | 24 - 36 | View |
| Apr 11, 2024 |
|
2 - 3 | 14 - 31 | View |
| Jan 25, 2024 |
|
3 - 4 | 46 - 26 | View |
| Dec 16, 2023 |
|
3 - 5 | 33 - 41 | View |
| Apr 12, 2023 |
|
2 - 4 | 19 - 35 | View |
| Feb 11, 2023 |
|
3 - 4 | 30 - 31 | 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.