Week 1 NFL player projection
Rashid Shaheed
PROJECTION
Rashid Shaheed is a SEA WR facing NE in Week 1. He averaged 38 receiving yards across 18 recorded 2025 games, in a range from 0 to 114. Below: the WinForge median projection, then the record it has to be judged against.
MEDIAN PROJECTION
WinForge projection (median)
61
Receiving Yards
WinForge projection (median)
3.9
Receptions
SEA vs NE · Wed, Sep 9 · updated Jul 18, 2026. These weekly projections are prepared for Week 1. This is the median: half of the outcomes WinForge simulates land above it and half below.
WHAT THE RECORD SHOWS
Rashid Shaheed has been consistent rather than trending. 38.2 receiving yards per game in 2025, compared with 44.4 in 2022 — a change of under 20% either way across the seasons WinForge stores.
Measured against this week's 61 projection, he cleared it in 5 of 18 games last season.
Split his 18 games down the middle and the two halves do not match: 55.4 receiving yards per game early, then 20.9 late.
HOW HE ACTUALLY FINISHED IN 2025
38.2
Receiving Yards / game
5/18
games clearing this projection
92
targets
61%
avg snap share
White line = his actual median (36.5). Gold line = this week's WinForge projection (61). This bar is built from recorded results, not a modelled range.
THE SHAPE OF HIS 2025 SEASON
His 2025 weeks were not evenly spread. The best was 114 receiving yards in Week 5 against NYG; the quietest was 0 in Week 16 against LA. He finished above his own 38-per-game average in 9 of 18 games.
He accounted for at least one touchdown in 2 of 18 games, never more than one in a game. Scoring is the most volatile part of any projection, so a record built on a handful of multi-score weeks reads differently from one spread evenly.
His longest single play on record in 2025 went for 87 yards. That single play is worth about 228% of an average game for him, which is the difference between a total built on steady volume and one that depends on a single play landing.
His role moved during the season: roughly 73% of snaps across his first 5 recorded games, then 44% across his last 5 — a shrinking share. Role changes travel into the next season more reliably than yardage does.
Across those games he handled 92 targets and 9 carries.
2025 GAME LOG
| Wk | Matchup | Rec/Tgt | Receiving Yards | TD | Snap | Long |
|---|---|---|---|---|---|---|
| 1 | NO vs ARI | 6/9 | 33 | 0 | 85% | 17 |
| 2 | NO vs SF | 4/5 | 52 | 1 | 70% | 39 |
| 3 | NO vs SEA | 4/6 | 42 | 0 | 74% | 15 |
| 4 | NO vs BUF | 4/5 | 47 | 0 | 70% | 15 |
| 5 | NO vs NYG | 4/5 | 114 | 1 | 67% | 87 |
| 6 | NO vs NE | 4/6 | 28 | 0 | 61% | 17 |
| 7 | NO vs CHI | 4/9 | 40 | 0 | 77% | 13 |
| 8 | NO vs TB | 9/12 | 75 | 0 | 78% | 10 |
| 9 | NO vs LA | 5/9 | 68 | 0 | 83% | 26 |
| 10 | SEA vs ARI | 1/1 | 3 | 0 | 34% | 10 |
| 11 | SEA vs LA | 2/5 | 27 | 0 | 54% | 21 |
| 12 | SEA vs TEN | 0/2 | 0 | 0 | 56% | 5 |
| 13 | SEA vs MIN | 1/3 | 7 | 0 | 48% | 9 |
| 14 | SEA vs ATL | 4/5 | 67 | 0 | 51% | 33 |
| 15 | SEA vs IND | 5/7 | 74 | 0 | -- | -- |
| 16 | SEA vs LA | 0/1 | 0 | 0 | 54% | 31 |
| 17 | SEA vs CAR | 1/1 | 8 | 0 | 18% | 8 |
| 18 | SEA vs SF | 1/1 | 2 | 0 | 48% | 2 |
SEASON BY SEASON
| Season | G | Tgt | Receiving Yards | Per game | TD | PPR/G |
|---|---|---|---|---|---|---|
| 2022 | 11 | 34 | 488 | 44.4 | 3 | 10.4 |
| 2023 | 15 | 75 | 719 | 47.9 | 5 | 11.6 |
| 2024 | 6 | 41 | 349 | 58.2 | 3 | 14.3 |
| 2025 | 18 | 92 | 687 | 38.2 | 2 | 9.6 |
CAREER VS NE
| Season | Wk | Team | Rec/Tgt | Receiving Yards | TD |
|---|---|---|---|---|---|
| 2025 | Wk 6 | NO | 4/6 | 28 | 0 |
| 2023 | Wk 5 | NO | 2/2 | 28 | 0 |
WHY THIS PROJECTION MOVES
Rashid Shaheed is tagged as WR4 rotation for SEA. The page uses top-4 WR projection volume for SEA, then layers in a middle-tier prior matchup with NE ranked #13 vs WR and allowing 22.8 fantasy points per game so the public read is tied to this player's actual weekly situation.
ACCURACY FRAMING AND EVERY OTHER WEEK 1 PROJECTION
In the independent 2025 test using games kept out of training, the WinForge receiving-yards model explained 59% of the variation in actual WR receiving yards. The model-fit result shows how closely projections tracked real differences in results; it is not a hit rate. Read the accuracy ledger and methodology for context.
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