San Jose Sharks vs Chicago Blackhawks prediction, odds, win probability, and matchup analysis for April 15, 2026
Get the playerWON NHL prediction for the San Jose Sharks at Chicago Blackhawks matchup on April 15, 2026. playerWONโs machine-learning model currently gives the Chicago Blackhawks a 55.2% win probability.
๐ค Model favours Chicago Blackhawks
(55.2%)
Compare market odds, fair odds, and value below.
April 15, 2026
Visitor Model vs Market
San Jose Sharks
44.8%
Fair American: +123
Fair Decimal: 2.23
Best Market: -114 ยท BetOnline.ag
Market Implied: 53.3%
Edge: -8.5%
EV: -15.9%
Calculate Value โ
Home Model vs Market
Chicago Blackhawks
55.2%
Fair American: -123
Fair Decimal: 1.81
Best Market: +105 ยท DraftKings
Market Implied: 48.8%
Edge: +6.4%
EV: +13.2%
Calculate Value โ
๐ Season Overview
|
|
Metric |
|
|---|---|---|
| 86 | Points | 72 |
| 0.524 | Points % | 0.439 |
| 251 | Goals For | 213 |
| 292 | Goals Against | 275 |
| -41 | Goal Differential | -62 |
| W1 | Current Streak | W1 |
๐ Road vs ๐ Home
|
|
Metric |
|
|---|---|---|
| 41 | Games | 41 |
| 18 | Wins | 14 |
| 38 | Points | 36 |
| -25 | Goal Differential | -28 |
๐ฅ Last 10 Games
|
|
Metric |
|
|---|---|---|
| 5-4-1 | Record | 2-7-1 |
| 11 | Points | 5 |
| 31 | Goals For | 25 |
| 35 | Goals Against | 42 |
| -4 | Goal Differential | -17 |
๐ฅ
Last 10 Head-to-Head Games
|
|
H2H Summary |
|
|---|---|---|
| 4-6 | Record | 6-4 |
| 30 | Total Goals | 34 |
| -4 | Goal Differential | +4 |
| 3.0 | Goals / Game | 3.4 |
| 29.7 | Shots / Game | 27.2 |
| Date | Matchup | Score | Shots | Details |
|---|---|---|---|---|
| Apr 6, 2026 |
|
2 - 3 | 29 - 23 | View |
| Feb 2, 2026 |
|
3 - 6 | 27 - 17 | View |
| Mar 13, 2025 |
|
2 - 4 | 26 - 25 | View |
| Oct 31, 2024 |
|
2 - 3 | 29 - 29 | View |
| Oct 17, 2024 |
|
2 - 4 | 22 - 27 | View |
| Mar 23, 2024 |
|
5 - 4 | 31 - 30 | View |
| Mar 17, 2024 |
|
2 - 5 | 27 - 31 | View |
| Jan 16, 2024 |
|
1 - 2 | 38 - 25 | View |
| Feb 25, 2023 |
|
4 - 3 | 35 - 48 | View |
| Jan 1, 2023 |
|
5 - 2 | 28 - 22 | 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.