Toronto Maple Leafs vs Ottawa Senators prediction, odds, win probability, and matchup analysis for April 15, 2026
Get the playerWON NHL prediction for the Toronto Maple Leafs at Ottawa Senators matchup on April 15, 2026. playerWONโs machine-learning model currently gives the Ottawa Senators a 70.5% win probability.
๐ค Model favours Ottawa Senators
(70.5%)
Compare market odds, fair odds, and value below.
April 15, 2026
Visitor Model vs Market
Toronto Maple Leafs
29.5%
Fair American: +239
Fair Decimal: 3.39
Best Market: +173 ยท BetOnline.ag
Market Implied: 36.6%
Edge: -7.1%
EV: -19.5%
Calculate Value โ
Home Model vs Market
Ottawa Senators
70.5%
Fair American: -239
Fair Decimal: 1.42
Best Market: -198 ยท BetOnline.ag
Market Implied: 66.4%
Edge: +4.1%
EV: +6.1%
Calculate Value โ
๐ Season Overview
|
|
Metric |
|
|---|---|---|
| 78 | Points | 99 |
| 0.476 | Points % | 0.604 |
| 253 | Goals For | 278 |
| 299 | Goals Against | 246 |
| -46 | Goal Differential | 32 |
| L5 | Current Streak | W1 |
๐ Road vs ๐ Home
|
|
Metric |
|
|---|---|---|
| 41 | Games | 41 |
| 14 | Wins | 23 |
| 34 | Points | 52 |
| -36 | Goal Differential | 31 |
๐ฅ Last 10 Games
|
|
Metric |
|
|---|---|---|
| 2-7-1 | Record | 6-3-1 |
| 5 | Points | 13 |
| 28 | Goals For | 36 |
| 47 | Goals Against | 26 |
| -19 | Goal Differential | 10 |
๐ฅ
Last 10 Head-to-Head Games
|
|
H2H Summary |
|
|---|---|---|
| 2-8 | Record | 8-2 |
| 26 | Total Goals | 42 |
| -16 | Goal Differential | +16 |
| 2.6 | Goals / Game | 4.2 |
| 27.2 | Shots / Game | 33.7 |
| Date | Matchup | Score | Shots | Details |
|---|---|---|---|---|
| Mar 21, 2026 |
|
2 - 5 | 14 - 43 | View |
| Feb 28, 2026 |
|
5 - 2 | 40 - 23 | View |
| Dec 27, 2025 |
|
5 - 7 | 31 - 33 | View |
| Mar 15, 2025 |
|
4 - 2 | 25 - 23 | View |
| Jan 25, 2025 |
|
1 - 2 | 29 - 23 | View |
| Nov 12, 2024 |
|
3 - 0 | 41 - 27 | View |
| Feb 10, 2024 |
|
3 - 5 | 35 - 32 | View |
| Dec 27, 2023 |
|
4 - 2 | 30 - 36 | View |
| Dec 7, 2023 |
|
4 - 3 | 22 - 41 | View |
| Nov 8, 2023 |
|
6 - 3 | 31 - 30 | 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.