Draw Duel
JJ Peterka faceoff stats, 2025-26
BOS · R · Left shot · full player page
Model rating
36.8%
90%: 30.8%–43.2% vs an average regular, on 51 draws since 2023-24
2025-26 FO%
33.3%
8 won of 24 (regular season)
Strong / weak side
33.3% / 100.0%
21 strong-side, 1 weak-side draws this season
Next game
No projection scheduled.
Where Peterka 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 | 16 | 3 | 18.8% |
| OZ right | 2 | 0 | 0.0% |
| NZ left (att.) | 4 | 2 | 50.0% |
| NZ right (att.) | 1 | 0 | 0.0% |
| Centre | 4 | 0 | 0.0% |
| NZ left (def.) | 2 | 1 | 50.0% |
| NZ right (def.) | 1 | 1 | 100.0% |
| DZ left | 6 | 3 | 50.0% |
| DZ right | 0 | 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 · R29.0%
- Aatu Räty VAN · L29.7%
- Jean-Gabriel Pageau NYI · R29.9%
- Vincent Trocheck UTA · R30.4%
- J.T. Miller NYR · L30.5%
- Auston Matthews TOR · L30.7%
Easiest
- Jack Hughes NJD · L49.6%
- Noah Ostlund BUF · L49.3%
- Trevor Zegras PHI · L48.1%
- Danila Yurov MIN · L46.4%
- Pavel Buchnevich STL · L46.1%
- Calum Ritchie NYI · R45.9%
Splits by season
| Season | All | EV | PP | PK | OZ | NZ | DZ | Strong | Weak |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | 33.3% 24 | 34.8% 23 | 0.0% 1 | — 0 | 25.0% 12 | 42.9% 7 | 40.0% 5 | 33.3% 21 | 100.0% 1 |
| 2024-25 | 16.7% 12 | 16.7% 12 | — 0 | — 0 | 0.0% 6 | 20.0% 5 | 100.0% 1 | 28.6% 7 | 0.0% 3 |
| 2023-24 | 20.0% 15 | 20.0% 15 | — 0 | — 0 | 14.3% 7 | 0.0% 1 | 28.6% 7 | 12.5% 8 | 28.6% 7 |
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? |
|---|---|---|---|---|---|
| Pavel Zacha | 1–2 | 33.3% | 31.1% | 31.1% | normal luck (z 0.1) |
| Vincent Trocheck | 1–2 | 33.3% | 30.3% | 30.3% | normal luck (z 0.1) |
| Nathan MacKinnon | 0–2 | 0.0% | 35.8% | 35.6% | a bit unusual (z -1.1) |
| Brayden Schenn | 1–1 | 50.0% | 36.1% | 36.2% | normal luck (z 0.4) |
| Adam Henrique | 1–1 | 50.0% | 33.0% | 33.0% | normal luck (z 0.5) |
| Evan Rodrigues | 1–1 | 50.0% | 38.9% | 38.9% | normal luck (z 0.3) |
| Matty Beniers | 0–1 | 0.0% | 34.2% | 34.1% | normal luck (z -0.7) |
| Wyatt Johnston | 0–1 | 0.0% | 37.8% | 37.8% | normal luck (z -0.8) |
| Oliver Kapanen | 1–0 | 100.0% | 39.8% | 39.9% | a bit unusual (z 1.2) |
| Gavin Brindley | 0–1 | 0.0% | 48.7% | 48.6% | normal luck (z -1.0) |
| Macklin Celebrini | 0–1 | 0.0% | 33.4% | 33.3% | normal luck (z -0.7) |
| Jesse Ylonen | 0–1 | 0.0% | 45.5% | 45.4% | normal luck (z -0.9) |
Last 10 games
| Date | Opp | FOW | Draws | FO% |
|---|---|---|---|---|
| 2026-03-24 | vs EDM | 1 | 1 | 100.0% |
| 2026-02-25 | vs COL | 0 | 1 | 0.0% |
| 2026-01-31 | vs DAL | 0 | 1 | 0.0% |
| 2026-01-29 | @ CAR | 1 | 1 | 100.0% |
| 2026-01-27 | @ FLA | 1 | 2 | 50.0% |
| 2026-01-21 | vs PHI | 0 | 1 | 0.0% |
| 2026-01-17 | vs SEA | 0 | 1 | 0.0% |
| 2026-01-15 | vs DAL | 0 | 1 | 0.0% |
| 2026-01-13 | vs TOR | 0 | 1 | 0.0% |
| 2026-01-07 | vs OTT | 0 | 1 | 0.0% |
Projected faceoffs won
No upcoming games projected.
FAQ
What is JJ Peterka's faceoff percentage?
The season line above shows JJ Peterka'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 JJ Peterka win tonight?
When JJ Peterka 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 JJ Peterka 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%.