Points by season
Career NHL points, ending with the 84-game projection (violet).
Pool value
Projected full-season fantasy worth.
why 18?
Each row is one fantasy category turned into points over a full 84-game season. Add the rows up and that is his total fantasy worth.
- proj goals5 × 3+15
- proj assists8 × 1+8
- PP points0 × 0.5+0
- game-winning goals0 × 0.5+0
- OT goals0 × 0.25+0
- shots64 × 0.1+6.4
- PIM80 × 0.05+4
- ice time (min)931 × 0.01+9.3
- total43 pool points · an elite forward season scores 24243 = 18/100
Weights follow what the average fantasy league actually counts: goals at 3× assists (the universal core), power-play points and game-winners at half weight (common extra categories), OT goals, shots and PIM small (many leagues skip them), and ice time as a quiet availability bonus. The scale: add up a whole season of those categories and compare against the best forwards in the NHL — an elite season scores about 242 points, and that is what 100 means. So 43 points is 18 out of a perfect fantasy season at his position. Defensemen and forwards are graded on separate scales because the D slot can only be filled by a defenseman. Goals and assists come from the backtested season model; the rest ride your own season-to-date pace over 84 games (21 games so far). Hits/blocks are excluded: the free NHL feeds don't publish full-season totals, and we don't invent them.
The Situation
- Minutes are shrinking: 9:22/game over the last 6, down 19% from 11:32 earlier in the season.
- Every night: in the lineup for all of the team's last 10 games.
- Availability: every game this season — 17 of 17 for SJS.
Every line is measured from our own boxscore ledger; absence reasons from the NHL Status Report and our transaction wire only — no insider guessing.
Finishing check underperforming O/U ▼ −1.3
- TRADE VALUE: BELOW PACE — mildly. Cheap production relative to the underlying play.
- 0.0% shooting on 16 shots against HIS OWN career baseline of 9.9% (720 shots before this season) — that's 1.6 goals below what his shot volume normally buys for him (1.6 expected goals). under pace. His shrunk talent rate is 7.8%, worth PD −1.3 goals per 84 over/under his own standard.
Model: goals vs shot volume vs live league position baselines; talent SH% is an empirical-Bayes shrinkage of the season…
His shooting talent is estimated by shrinking his season shooting percentage toward his league position mean in proportion to sample size (kappa pseudo-shots prior), then projecting the gap over 84 games at his current shot volume. Small samples stay near zero — the number only moves when the evidence does.
The method follows published hockey-analytics research: a shooter is judged against his own career shooting percentage, because a player's own career rate predicts his next goals better than any league average (“Expected by Whom? A Skill-Adjusted Expected Goals Model for NHL Shooters and Goaltenders,” arXiv:2511.07703). Thin careers (rookies) shrink toward the league mean for the position, and the season number is shrunk toward the player's own anchor with empirical-Bayes weights — Nandakumar & Jensen (2019), Annual Review of Statistics and Its Application, doi:10.1146/annurev-statistics-030718-105202. This section is about the gap between luck and his pace, not talent.
Season stats
NHL regular-season totals, season by season. The 84-G proj line extends the current season with our public model — methodology in the caption below the table.
| Season | Team | GP | G | A | P | +/- | PIM | PPG | PPP | SHG | TOI/GP | GWG | OTG | S | S% | FOW% |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Sharks | 60 | 4 | 8 | 12 | -1 | 35 | 0 | 0 | 0 | 11:04 | 2 | 0 | 68 | 5.9% | 52.2% |
| 2015-16 | Sharks | 14 | 0 | 3 | 3 | 1 | 16 | 0 | 0 | 0 | 10:16 | 0 | 0 | 7 | 0.0% | 66.7% |
| 2016-17 | Sharks | 3 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 11:18 | 0 | 0 | 3 | 0.0% | 0.0% |
| 2017-18 | Sharks | 47 | 7 | 7 | 14 | 1 | 28 | 0 | 0 | 1 | 10:53 | 1 | 0 | 57 | 12.3% | 58.8% |
| 2018-19 | Sharks | 82 | 7 | 10 | 17 | 4 | 76 | 0 | 0 | 1 | 11:01 | 3 | 0 | 84 | 8.3% | 53.7% |
| 2019-20 | Sharks | 62 | 8 | 16 | 24 | -8 | 80 | 0 | 2 | 0 | 16:23 | 2 | 0 | 94 | 8.5% | 49.2% |
| 2019-20 | Lightning | 8 | 0 | 2 | 2 | 0 | 17 | 0 | 0 | 0 | 12:25 | 0 | 0 | 8 | 0.0% | 48.8% |
| 2020-21 | Lightning | 55 | 6 | 14 | 20 | 16 | 52 | 0 | 0 | 0 | 15:07 | 1 | 0 | 72 | 8.3% | 50.0% |
| 2021-22 | Rangers | 79 | 13 | 20 | 33 | 13 | 69 | 0 | 0 | 1 | 16:43 | 2 | 0 | 116 | 11.2% | 49.4% |
| 2022-23 | Rangers | 82 | 11 | 20 | 31 | 3 | 58 | 1 | 2 | 0 | 14:10 | 2 | 0 | 89 | 12.4% | 45.9% |
| 2023-24 | Rangers | 80 | 4 | 8 | 12 | -13 | 78 | 0 | 0 | 1 | 12:34 | 1 | 0 | 61 | 6.6% | 54.0% |
| 2024-25 | Sharks | 77 | 5 | 3 | 8 | -37 | 75 | 0 | 0 | 1 | 13:47 | 0 | 0 | 73 | 6.8% | 42.9% |
| 2025-26 | Sharks | 82 | 5 | 7 | 12 | -18 | 73 | 0 | 0 | 0 | 11:21 | 0 | 0 | 66 | 7.6% | 40.0% |
| 2026-27 | Sharks | 21 | 0 | 1 | 1 | -2 | 20 | 0 | 0 | 0 | 11:05 | 0 | 0 | 16 | 0.0% | 16.7% |
| 84-G proj | Sharks | 84 | 5 | 8 | 13 | — | 65 | — | 0 | — | 12:34 | — | — | 97 | — | — |
84-G proj — current pace blended with the player's own history (recent seasons weighted heaviest), mixed with a similarity projection from historical players with comparable profiles, then adjusted for the aging curve (league-average peak at 27; decline after) — at age 33 that is -4%. Dashes mark stats the model doesn't project.
Comparable player-seasons: Barclay Goodrow (2025-25) · Garnet Hathaway (2025-25) · Brandon Duhaime (2025-25) · Jordan Greenway (2025-25) · Zemgus Girgensons (2025-25).
Line mates
Teammates sharing the most ice time with Barclay over the last 10 games — reconstructed shift-by-shift from the NHL's official timing feed (goalies excluded).
| Player | Shared min | GP | |
|---|---|---|---|
| Zack Ostapchuk SJS | 56 | 8 | |
| Darnell Nurse SJS | 49 | 10 | |
| Jacob Trouba SJS | 47 | 10 | |
| Ty Dellandrea SJS | 43 | 8 | |
| Sam Dickinson SJS | 38 | 10 |
Data via the public NHL API · 2026 Pro Hockey Stats