Toronto Maple Leafs vs Los Angeles Kings prediction, odds, win probability, and matchup analysis for April 4, 2026
Get the playerWON NHL prediction for the Toronto Maple Leafs at Los Angeles Kings matchup on April 4, 2026. playerWONโs machine-learning model currently gives the Los Angeles Kings a 51.8% win probability.
๐ค Model favours Los Angeles Kings
(51.8%)
Compare market odds, fair odds, and value below.
April 4, 2026
Visitor Model vs Market
Toronto Maple Leafs
48.2%
Fair American: +107
Fair Decimal: 2.07
Best Market: +175 ยท BetRivers
Market Implied: 36.4%
Edge: +11.8%
EV: +32.6%
Calculate Value โ
Home Model vs Market
Los Angeles Kings
51.8%
Fair American: -107
Fair Decimal: 1.93
Best Market: -195 ยท BetOnline.ag
Market Implied: 66.1%
Edge: -14.3%
EV: -21.6%
Calculate Value โ
๐ Season Overview
|
|
Metric |
|
|---|---|---|
| 78 | Points | 90 |
| 0.476 | Points % | 0.549 |
| 253 | Goals For | 225 |
| 299 | Goals Against | 247 |
| -46 | Goal Differential | -22 |
| L5 | Current Streak | L1 |
๐ Road vs ๐ Home
|
|
Metric |
|
|---|---|---|
| 41 | Games | 41 |
| 14 | Wins | 15 |
| 34 | Points | 39 |
| -36 | Goal Differential | -20 |
๐ฅ Last 10 Games
|
|
Metric |
|
|---|---|---|
| 2-7-1 | Record | 6-2-2 |
| 5 | Points | 14 |
| 28 | Goals For | 32 |
| 47 | Goals Against | 31 |
| -19 | Goal Differential | 1 |
๐ฅ
Last 10 Head-to-Head Games
|
|
H2H Summary |
|
|---|---|---|
| 5-5 | Record | 5-5 |
| 30 | Total Goals | 23 |
| +7 | Goal Differential | -7 |
| 3.0 | Goals / Game | 2.3 |
| 30.3 | Shots / Game | 31.4 |
| Date | Matchup | Score | Shots | Details |
|---|---|---|---|---|
| Nov 13, 2025 |
|
4 - 3 | 37 - 15 | View |
| Mar 29, 2025 |
|
3 - 1 | 26 - 36 | View |
| Oct 16, 2024 |
|
2 - 6 | 34 - 26 | View |
| Jan 2, 2024 |
|
3 - 0 | 29 - 31 | View |
| Oct 31, 2023 |
|
4 - 1 | 27 - 30 | View |
| Dec 8, 2022 |
|
0 - 5 | 29 - 41 | View |
| Oct 29, 2022 |
|
2 - 4 | 28 - 29 | View |
| Nov 24, 2021 |
|
6 - 2 | 38 - 32 | View |
| Nov 8, 2021 |
|
5 - 1 | 29 - 34 | View |
| Mar 5, 2020 |
|
0 - 1 | 36 - 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.