Florida Panthers vs Pittsburgh Penguins prediction, odds, win probability, and matchup analysis for April 4, 2026
Get the playerWON NHL prediction for the Florida Panthers at Pittsburgh Penguins matchup on April 4, 2026. playerWONโs machine-learning model currently gives the Pittsburgh Penguins a 52.1% win probability.
๐ค Model favours Pittsburgh Penguins
(52.1%)
Compare market odds, fair odds, and value below.
April 4, 2026
Visitor Model vs Market
Florida Panthers
47.9%
Fair American: +109
Fair Decimal: 2.09
Best Market: +188 ยท BetRivers
Market Implied: 34.7%
Edge: +13.2%
EV: +38.0%
Calculate Value โ
Home Model vs Market
Pittsburgh Penguins
52.1%
Fair American: -109
Fair Decimal: 1.92
Best Market: -208 ยท BetOnline.ag
Market Implied: 67.5%
Edge: -15.4%
EV: -22.9%
Calculate Value โ
๐ Season Overview
|
|
Metric |
|
|---|---|---|
| 84 | Points | 98 |
| 0.512 | Points % | 0.598 |
| 251 | Goals For | 293 |
| 276 | Goals Against | 268 |
| -25 | Goal Differential | 25 |
| W3 | Current Streak | L3 |
๐ Road vs ๐ Home
|
|
Metric |
|
|---|---|---|
| 41 | Games | 41 |
| 17 | Wins | 20 |
| 35 | Points | 48 |
| -35 | Goal Differential | 7 |
๐ฅ Last 10 Games
|
|
Metric |
|
|---|---|---|
| 5-4-1 | Record | 5-5-0 |
| 11 | Points | 10 |
| 36 | Goals For | 46 |
| 35 | Goals Against | 40 |
| 1 | Goal Differential | 6 |
๐ฅ
Last 10 Head-to-Head Games
|
|
H2H Summary |
|
|---|---|---|
| 6-4 | Record | 4-6 |
| 37 | Total Goals | 32 |
| +5 | Goal Differential | -5 |
| 3.7 | Goals / Game | 3.2 |
| 34.4 | Shots / Game | 30.1 |
| Date | Matchup | Score | Shots | Details |
|---|---|---|---|---|
| Oct 23, 2025 |
|
5 - 3 | 17 - 37 | View |
| Mar 23, 2025 |
|
3 - 4 | 27 - 31 | View |
| Jan 3, 2025 |
|
2 - 3 | 31 - 33 | View |
| Dec 3, 2024 |
|
4 - 5 | 41 - 16 | View |
| Feb 14, 2024 |
|
5 - 2 | 23 - 27 | View |
| Jan 26, 2024 |
|
3 - 2 | 31 - 37 | View |
| Dec 8, 2023 |
|
1 - 3 | 26 - 33 | View |
| Mar 4, 2023 |
|
1 - 4 | 32 - 42 | View |
| Jan 24, 2023 |
|
6 - 7 | 39 - 49 | View |
| Dec 15, 2022 |
|
4 - 2 | 39 - 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.