Vegas Golden Knights vs Seattle Kraken prediction, odds, win probability, and matchup analysis for April 9, 2026
Get the playerWON NHL prediction for the Vegas Golden Knights at Seattle Kraken matchup on April 9, 2026. playerWONโs machine-learning model currently gives the Vegas Golden Knights a 67.3% win probability.
๐ค Model favours Vegas Golden Knights
(67.3%)
Compare market odds, fair odds, and value below.
April 9, 2026
Visitor Model vs Market
Vegas Golden Knights
67.3%
Fair American: -206
Fair Decimal: 1.49
Best Market: -180 ยท BetUS
Market Implied: 64.3%
Edge: +3.0%
EV: +4.7%
Calculate Value โ
Home Model vs Market
Seattle Kraken
32.7%
Fair American: +206
Fair Decimal: 3.06
Best Market: +160 ยท BetOnline.ag
Market Implied: 38.5%
Edge: -5.8%
EV: -15.0%
Calculate Value โ
๐ Season Overview
|
|
Metric |
|
|---|---|---|
| 95 | Points | 79 |
| 0.579 | Points % | 0.482 |
| 265 | Goals For | 226 |
| 250 | Goals Against | 263 |
| 15 | Goal Differential | -37 |
| W3 | Current Streak | L3 |
๐ Road vs ๐ Home
|
|
Metric |
|
|---|---|---|
| 41 | Games | 41 |
| 19 | Wins | 19 |
| 46 | Points | 43 |
| -4 | Goal Differential | -4 |
๐ฅ Last 10 Games
|
|
Metric |
|
|---|---|---|
| 7-0-3 | Record | 2-8-0 |
| 17 | Points | 4 |
| 40 | Goals For | 20 |
| 25 | Goals Against | 39 |
| 15 | Goal Differential | -19 |
๐ฅ
Last 10 Head-to-Head Games
|
|
H2H Summary |
|
|---|---|---|
| 6-4 | Record | 4-6 |
| 29 | Total Goals | 22 |
| +7 | Goal Differential | -7 |
| 2.9 | Goals / Game | 2.2 |
| 29.2 | Shots / Game | 26.8 |
| Date | Matchup | Score | Shots | Details |
|---|---|---|---|---|
| Jan 31, 2026 |
|
3 - 2 | 23 - 29 | View |
| Oct 11, 2025 |
|
1 - 2 | 27 - 22 | View |
| Apr 10, 2025 |
|
1 - 2 | 25 - 25 | View |
| Dec 21, 2024 |
|
2 - 6 | 23 - 34 | View |
| Nov 8, 2024 |
|
3 - 4 | 24 - 32 | View |
| Mar 21, 2024 |
|
1 - 3 | 22 - 37 | View |
| Mar 12, 2024 |
|
5 - 4 | 34 - 30 | View |
| Jan 1, 2024 |
|
0 - 3 | 35 - 27 | View |
| Oct 10, 2023 |
|
1 - 4 | 33 - 28 | View |
| Apr 13, 2023 |
|
3 - 1 | 19 - 31 | View |
This matchup prediction is generated using playerWONโs machine-learning model, incorporating team performance, recent form, and home/away effects. View full model performance to compare results across games.