Columbus Blue Jackets vs Detroit Red Wings prediction, odds, win probability, and matchup analysis for April 7, 2026
Get the playerWON NHL prediction for the Columbus Blue Jackets at Detroit Red Wings matchup on April 7, 2026. playerWONโs machine-learning model currently gives the Detroit Red Wings a 56.1% win probability.
๐ค Model favours Detroit Red Wings
(56.1%)
Compare market odds, fair odds, and value below.
April 7, 2026
Visitor Model vs Market
Columbus Blue Jackets
43.9%
Fair American: +128
Fair Decimal: 2.28
Best Market: +100 ยท BetOnline.ag
Market Implied: 50.0%
Edge: -6.1%
EV: -12.2%
Calculate Value โ
Home Model vs Market
Detroit Red Wings
56.1%
Fair American: -128
Fair Decimal: 1.78
Best Market: -112 ยท Bovada
Market Implied: 52.8%
Edge: +3.3%
EV: +6.2%
Calculate Value โ
๐ Season Overview
|
|
Metric |
|
|---|---|---|
| 92 | Points | 92 |
| 0.561 | Points % | 0.561 |
| 253 | Goals For | 241 |
| 253 | Goals Against | 258 |
| 0 | Goal Differential | -17 |
| L2 | Current Streak | L1 |
๐ Road vs ๐ Home
|
|
Metric |
|
|---|---|---|
| 41 | Games | 41 |
| 20 | Wins | 21 |
| 44 | Points | 46 |
| -9 | Goal Differential | -10 |
๐ฅ Last 10 Games
|
|
Metric |
|
|---|---|---|
| 2-7-1 | Record | 2-6-2 |
| 5 | Points | 6 |
| 21 | Goals For | 29 |
| 34 | Goals Against | 45 |
| -13 | Goal Differential | -16 |
๐ฅ
Last 10 Head-to-Head Games
|
|
H2H Summary |
|
|---|---|---|
| 4-6 | Record | 6-4 |
| 36 | Total Goals | 39 |
| -3 | Goal Differential | +3 |
| 3.6 | Goals / Game | 3.9 |
| 28.2 | Shots / Game | 31.4 |
| Date | Matchup | Score | Shots | Details |
|---|---|---|---|---|
| Dec 4, 2025 |
|
5 - 6 | 33 - 32 | View |
| Nov 22, 2025 |
|
3 - 4 | 35 - 33 | View |
| Mar 1, 2025 |
|
3 - 5 | 46 - 21 | View |
| Feb 27, 2025 |
|
5 - 2 | 30 - 31 | View |
| Jan 2, 2025 |
|
5 - 4 | 26 - 25 | View |
| Mar 19, 2024 |
|
3 - 4 | 35 - 26 | View |
| Nov 11, 2023 |
|
4 - 5 | 22 - 28 | View |
| Oct 16, 2023 |
|
4 - 0 | 28 - 23 | View |
| Jan 14, 2023 |
|
4 - 3 | 25 - 28 | View |
| Dec 4, 2022 |
|
4 - 2 | 35 - 34 | 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.