Week 1 NFL player projection
Jonathan Taylor
PROJECTION
Jonathan Taylor is a IND RB facing BAL in Week 1. He averaged 93 rushing yards across 17 recorded 2025 games, in a range from 26 to 244. Below: the WinForge median projection, then the record it has to be judged against.
MEDIAN PROJECTION
WinForge projection (median)
53
Rushing Yards
WinForge projection (median)
2.4
Receptions
IND vs BAL · Sun, Sep 13 · 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
Jonathan Taylor has been consistent rather than trending. 93.2 rushing yards per game in 2025, compared with 78.3 in 2022 — a change of under 20% either way across the seasons WinForge stores.
Measured against this week's 52.5 projection, he cleared it in 14 of 17 games last season.
Split his 17 games down the middle and the two halves do not match: 106.2 rushing yards per game early, then 81.7 late.
HOW HE ACTUALLY FINISHED IN 2025
93.2
Rushing Yards / game
14/17
games clearing this projection
55
targets
82%
avg snap share
White line = his actual median (76). Gold line = this week's WinForge projection (52.5). 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 244 rushing yards in Week 10 against ATL; the quietest was 26 in Week 18 against HOU. He finished above his own 93-per-game average in 6 of 17 games.
He accounted for at least one touchdown in 10 of 17 games, and more than one in 5 of them. 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 83 yards. That single play is worth about 89% 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 snap share held roughly steady through the season: about 81% across his first 5 recorded games and 80% across his last 5. A stable role is the part of a projection least likely to be wrong.
Across those games he handled 55 targets and 323 carries.
2025 GAME LOG
| Wk | Matchup | Rec/Tgt | Rushing Yards | TD | Snap | Long |
|---|---|---|---|---|---|---|
| 1 | IND vs MIA | 3/3 | 71 | 0 | 71% | 16 |
| 2 | IND vs DEN | 2/2 | 165 | 1 | 93% | 68 |
| 3 | IND vs TEN | 3/4 | 102 | 3 | 79% | 46 |
| 4 | IND vs LA | 5/5 | 76 | 0 | 87% | 12 |
| 5 | IND vs LV | 3/4 | 66 | 3 | 74% | 16 |
| 6 | IND vs ARI | 4/4 | 123 | 1 | 92% | 30 |
| 7 | IND vs LAC | 3/3 | 94 | 3 | 87% | 23 |
| 8 | IND vs TEN | 2/2 | 153 | 3 | 75% | 80 |
| 9 | IND vs PIT | 2/2 | 45 | 0 | 79% | 9 |
| 10 | IND vs ATL | 3/3 | 244 | 3 | 91% | 83 |
| 12 | IND vs KC | 2/3 | 58 | 0 | 90% | 27 |
| 13 | IND vs HOU | 3/5 | 85 | 0 | 98% | 33 |
| 14 | IND vs JAX | 0/0 | 74 | 1 | 74% | 14 |
| 15 | IND vs SEA | 3/4 | 87 | 0 | -- | -- |
| 16 | IND vs SF | 3/3 | 46 | 1 | 88% | 19 |
| 17 | IND vs JAX | 3/6 | 70 | 1 | 89% | 10 |
| 18 | IND vs HOU | 2/2 | 26 | 0 | 52% | 11 |
SEASON BY SEASON
| Season | G | Tgt | Rushing Yards | Per game | TD | PPR/G |
|---|---|---|---|---|---|---|
| 2022 | 11 | 40 | 861 | 78.3 | 4 | 14.9 |
| 2023 | 10 | 23 | 741 | 74.1 | 8 | 16.6 |
| 2024 | 14 | 31 | 1,431 | 102.2 | 12 | 18.3 |
| 2025 | 17 | 55 | 1,585 | 93.2 | 20 | 22.5 |
CAREER VS BAL
No career game against BAL on record in the 2022-2025 seasons WinForge stores. There is no head-to-head sample to lean on here, which is itself worth knowing.
WHY THIS PROJECTION MOVES
Jonathan Taylor is tagged as RB1 for IND. The page uses team-leading RB projection volume in the current weekly board, then layers in a middle-tier prior matchup with BAL ranked #17 vs RB and allowing 20.0 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 rushing-yards model explained 42% of the variation in actual RB rushing 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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