Draw Duel
Marc McLaughlin faceoff stats, 2025-26
NJD · C · Right shot · full player page
Model rating
39.3%
90%: 33.4%–45.5% vs an average regular, on 69 draws since 2023-24
2025-26 FO%
36.4%
12 won of 33 (regular season)
Strong / weak side
45.5% / 25.0%
11 strong-side, 16 weak-side draws this season
Next game
No projection scheduled.
Where McLaughlin wins draws
Win % at every dot, 2025-26 and 2024-25 regular season, in his own attacking direction.
Show as a table
| Dot | Draws | Won | FO% |
|---|---|---|---|
| OZ left | 4 | 1 | 25.0% |
| OZ right | 8 | 3 | 37.5% |
| NZ left (att.) | 6 | 3 | 50.0% |
| NZ right (att.) | 3 | 2 | 66.7% |
| Centre | 8 | 4 | 50.0% |
| NZ left (def.) | 1 | 0 | 0.0% |
| NZ right (def.) | 1 | 1 | 100.0% |
| DZ left | 9 | 2 | 22.2% |
| DZ right | 25 | 10 | 40.0% |
Toughest and easiest opponents
Model win % against today's regular centres, averaged over home and road and every dot. This is a prediction, not his record.
Toughest
- Claude Giroux OTT · R31.7%
- Aatu Räty VAN · L31.7%
- Jean-Gabriel Pageau NYI · R32.4%
- J.T. Miller NYR · L32.6%
- Auston Matthews TOR · L32.8%
- Jack Drury NSH · L32.9%
Easiest
- Jack Hughes NJD · L52.2%
- Noah Ostlund BUF · L51.9%
- Trevor Zegras PHI · L50.7%
- Danila Yurov MIN · L49.0%
- Pavel Buchnevich STL · L48.7%
- Calum Ritchie NYI · R48.5%
Splits by season
| Season | All | EV | PP | PK | OZ | NZ | DZ | Strong | Weak |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | 36.4% 33 | 34.4% 32 | 100.0% 1 | — 0 | 33.3% 6 | 46.2% 13 | 28.6% 14 | 45.5% 11 | 25.0% 16 |
| 2024-25 | 43.8% 32 | 43.8% 32 | — 0 | — 0 | 33.3% 6 | 66.7% 6 | 40.0% 20 | 42.3% 26 | 50.0% 4 |
| 2023-24 | 25.0% 4 | 25.0% 4 | — 0 | — 0 | 0.0% 1 | 50.0% 2 | 0.0% 1 | 33.3% 3 | — 0 |
Small numbers are draws taken. Strong = his strong-side dots (a left shot's left dots, in his own attacking direction); centre-ice draws count in neither.
Head-to-head, last three seasons
Raw records against his most frequent opponents next to what the model expected at the dots they actually met at. The blended number moves the model toward the record by n / (n + 500), because pair-specific edges are tiny (about ±2 points) and most of a lopsided record is luck.
| Opponent | Record | Raw | Model | Blended | Unusual? |
|---|---|---|---|---|---|
| Andrew Copp | 3–3 | 50.0% | 37.6% | 37.7% | normal luck (z 0.6) |
| Marco Kasper | 0–4 | 0.0% | 40.6% | 40.3% | a bit unusual (z -1.7) |
| Fraser Minten | 0–4 | 0.0% | 40.9% | 40.6% | a bit unusual (z -1.7) |
| Bo Horvat | 0–3 | 0.0% | 34.3% | 34.1% | a bit unusual (z -1.3) |
| Noel Acciari | 2–1 | 66.7% | 39.7% | 39.8% | normal luck (z 1.0) |
| Phillip Danault | 1–2 | 33.3% | 35.5% | 35.5% | normal luck (z -0.1) |
| Ryan Poehling | 1–2 | 33.3% | 39.6% | 39.6% | normal luck (z -0.2) |
| Sean Couturier | 1–2 | 33.3% | 35.2% | 35.2% | normal luck (z -0.1) |
| Pavel Zacha | 2–1 | 66.7% | 38.5% | 38.7% | a bit unusual (z 1.0) |
| Mark Kastelic | 0–2 | 0.0% | 27.2% | 27.1% | normal luck (z -0.9) |
| Morgan Frost | 1–1 | 50.0% | 37.9% | 38.0% | normal luck (z 0.4) |
| Vladislav Namestnikov | 0–2 | 0.0% | 46.6% | 46.4% | a bit unusual (z -1.3) |
Last 10 games
| Date | Opp | FOW | Draws | FO% |
|---|---|---|---|---|
| 2026-04-14 | @ BOS | 1 | 6 | 16.7% |
| 2026-04-12 | vs OTT | 2 | 6 | 33.3% |
| 2026-04-11 | @ DET | 1 | 6 | 16.7% |
| 2026-04-09 | vs PIT | 5 | 6 | 83.3% |
| 2026-04-07 | vs PHI | 1 | 4 | 25.0% |
| 2026-04-05 | @ MTL | 1 | 2 | 50.0% |
| 2026-04-04 | vs MTL | 1 | 3 | 33.3% |
| 2025-04-15 | @ BOS | 2 | 4 | 50.0% |
| 2025-01-11 | @ FLA | 2 | 3 | 66.7% |
| 2024-12-17 | @ CGY | 0 | 1 | 0.0% |
Projected faceoffs won
No upcoming games projected.
FAQ
What is Marc McLaughlin's faceoff percentage?
The season line above shows Marc McLaughlin's raw faceoff wins and percentage this season. The model rating beside it is his expected win rate against an average regular centre at a neutral dot, adjusted for opponents, home ice and dot side, with a 90% interval.
How many faceoffs will Marc McLaughlin win tonight?
When Marc McLaughlin has a game scheduled, the projection above gives expected draws, expected win rate against tonight's likely opposing centres, and a 10th–90th percentile range of faceoffs won. Projections update nightly.
Is Marc McLaughlin better on his strong side?
Almost every centre is. The strong/weak split above compares his win rate on his strong-side dots (a left shot's left dots) with his weak-side dots. League-wide, strong-side takers win about 52.6% and weak-side takers about 44.9%.