Tampa Bay Lightning vs Montréal Canadiens prediction, odds, win probability, and matchup analysis for April 9, 2026
Get the playerWON NHL prediction for the Tampa Bay Lightning at Montréal Canadiens matchup on April 9, 2026. playerWON’s machine-learning model currently gives the Tampa Bay Lightning a 60.4% win probability.
🤖 Model favours Tampa Bay Lightning
(60.4%)
Compare market odds, fair odds, and value below.
April 9, 2026
Visitor Model vs Market
Tampa Bay Lightning
60.4%
Fair American: -153
Fair Decimal: 1.66
Best Market: -110 · BetRivers
Market Implied: 52.4%
Edge: +8.0%
EV: +15.3%
Calculate Value →
Home Model vs Market
Montréal Canadiens
39.6%
Fair American: +153
Fair Decimal: 2.53
Best Market: +102 · BetOnline.ag
Market Implied: 49.5%
Edge: -9.9%
EV: -20.0%
Calculate Value →
📊 Season Overview
|
|
Metric |
|
|---|---|---|
| 106 | Points | 106 |
| 0.646 | Points % | 0.646 |
| 290 | Goals For | 283 |
| 231 | Goals Against | 256 |
| 59 | Goal Differential | 27 |
| L1 | Current Streak | L1 |
🚍 Road vs 🏠 Home
|
|
Metric |
|
|---|---|---|
| 41 | Games | 41 |
| 24 | Wins | 24 |
| 53 | Points | 50 |
| 37 | Goal Differential | 10 |
🔥 Last 10 Games
|
|
Metric |
|
|---|---|---|
| 5-5-0 | Record | 7-3-0 |
| 10 | Points | 14 |
| 26 | Goals For | 28 |
| 30 | Goals Against | 24 |
| -4 | Goal Differential | 4 |
🥅 Last 10 Head-to-Head Games
|
|
H2H Summary |
|
|---|---|---|
| 7-3 | Record | 3-7 |
| 41 | Total Goals | 33 |
| +8 | Goal Differential | -8 |
| 4.1 | Goals / Game | 3.3 |
| 28.1 | Shots / Game | 29.6 |
| Date | Matchup | Score | Shots | Details |
|---|---|---|---|---|
| Mar 31, 2026 |
|
4 - 1 | 23 - 37 | View |
| Dec 28, 2025 |
|
4 - 5 | 36 - 22 | View |
| Dec 9, 2025 |
|
6 - 1 | 27 - 27 | View |
| Feb 9, 2025 |
|
5 - 3 | 21 - 35 | View |
| Jan 21, 2025 |
|
2 - 3 | 35 - 22 | View |
| Dec 29, 2024 |
|
5 - 2 | 37 - 23 | View |
| Apr 4, 2024 |
|
7 - 4 | 35 - 30 | View |
| Mar 2, 2024 |
|
3 - 4 | 31 - 29 | View |
| Dec 31, 2023 |
|
3 - 4 | 29 - 20 | View |
| Nov 7, 2023 |
|
5 - 3 | 32 - 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.