Seattle Kraken vs Vegas Golden Knights prediction, odds, win probability, and matchup analysis for April 15, 2026
Get the playerWON NHL prediction for the Seattle Kraken at Vegas Golden Knights matchup on April 15, 2026. playerWONโs machine-learning model currently gives the Vegas Golden Knights a 67.8% win probability.
๐ค Model favours Vegas Golden Knights
(67.8%)
Compare market odds, fair odds, and value below.
April 15, 2026
Visitor Model vs Market
Seattle Kraken
32.2%
Fair American: +211
Fair Decimal: 3.11
Best Market: +250 ยท BetUS
Market Implied: 28.6%
Edge: +3.6%
EV: +12.7%
Calculate Value โ
Home Model vs Market
Vegas Golden Knights
67.8%
Fair American: -211
Fair Decimal: 1.47
Best Market: -286 ยท BetRivers
Market Implied: 74.1%
Edge: -6.3%
EV: -8.5%
Calculate Value โ
๐ Season Overview
|
|
Metric |
|
|---|---|---|
| 79 | Points | 95 |
| 0.482 | Points % | 0.579 |
| 226 | Goals For | 265 |
| 263 | Goals Against | 250 |
| -37 | Goal Differential | 15 |
| L3 | Current Streak | W3 |
๐ Road vs ๐ Home
|
|
Metric |
|
|---|---|---|
| 41 | Games | 41 |
| 15 | Wins | 20 |
| 36 | Points | 49 |
| -33 | Goal Differential | 19 |
๐ฅ Last 10 Games
|
|
Metric |
|
|---|---|---|
| 2-8-0 | Record | 7-0-3 |
| 4 | Points | 17 |
| 20 | Goals For | 40 |
| 39 | Goals Against | 25 |
| -19 | Goal Differential | 15 |
๐ฅ
Last 10 Head-to-Head Games
|
|
H2H Summary |
|
|---|---|---|
| 5-5 | Record | 5-5 |
| 25 | Total Goals | 29 |
| -4 | Goal Differential | +4 |
| 2.5 | Goals / Game | 2.9 |
| 27.0 | Shots / Game | 30.7 |
| Date | Matchup | Score | Shots | Details |
|---|---|---|---|---|
| Apr 9, 2026 |
|
3 - 4 | 34 - 33 | View |
| 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 |
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.