WR Model

Week 1, 2026 board

How the WR Model Works

What you're looking at

Each week our model projects a receiving-yards and receptions line for every team's WR1 and WR2 (plus other receivers who play at least 40% of snaps). "Our Line" is the model's median projection rounded to the nearest half point; "Range" is its 25th–75th percentile band.

The model

A LightGBM quantile ensemble trained on 2021-present player-game data: target and air-yards share, snap rates, depth-chart role, injuries, team pass-rate-over-expected and pace, opponent pass-defense EPA and pressure, Vegas totals/spreads, and Next Gen Stats separation metrics. The five quantile predictions form a full probability curve, so any sportsbook line can be priced as a calibrated P(over).

Edges and flags

"Book" is the sportsbook line we evaluated (Hard Rock when it posts one). "P(Over)" is the model's calibrated probability the player clears that line. "Edge" compares that to the de-vigged sharp-market consensus (Pinnacle preferred). The ⚑ flag marks plays where the edge is at least 4 points AND the sharp market leans the same way against Hard Rock's price.

Grading

After games finish, every prediction is graded against the evaluated line, and flagged edges get closing-line-value (CLV) tracking — how much the market moved toward or away from our number by kickoff, in probability terms. The performance strip shows the last four graded weeks.

Receiving Yards Receptions WR1/WR2 only
Player Team Opp Our Line Range (q25–q75) Book Book Line P(Over) Edge ⚑
Jaxon Smith-Njigba WR1 SEA NE 59.5 38.6–78.8 draftkings 85.5 47.6% -2.6 pts
Puka Nacua WR1 LA SF 64.5 43.9–95.4 draftkings 91.5 47.6% -2.4 pts
A.J. Brown WR1 PHI WAS 58.0 36.2–81.8 draftkings 59.5 50.1% -0.2 pts
Keenan Allen WR1 LAC ARI 27.0 7.7–41.6 — — — —
Mack Hollins WR1 NE SEA 36.0 19.1–58.1 — — — —
Courtland Sutton WR1 DEN KC 42.5 24.2–63.9 — — — —
DK Metcalf WR1 PIT ATL 47.5 26.9–63.9 — — — —
Terry McLaurin WR1 WAS PHI 43.5 25.9–62.4 — — — —
Brandon Aiyuk WR1 SF LA 38.0 18–60.2 — — — —
Justin Jefferson WR1 MIN GB 54.0 28.2–70.2 — — — —
Jerry Jeudy WR1 CLE JAX 32.0 17.9–49.4 — — — —
Nico Collins WR1 HOU BUF 44.5 24.4–76.1 — — — —
Jaylen Waddle WR1 MIA LV 34.0 18.2–54.4 — — — —
Ja'Marr Chase WR1 CIN TB 62.5 43.4–90.8 — — — —
Amon-Ra St. Brown WR1 DET NO 63.5 43–100.3 — — — —
Drake London WR1 ATL PIT 46.0 32.7–67.1 — — — —
Chris Olave WR1 NO DET 62.0 38.6–83.7 — — — —
George Pickens WR1 DAL NYG 46.5 22.5–78 — — — —
Khalil Shakir WR1 BUF HOU 36.5 18–58.1 — — — —
Garrett Wilson WR1 NYJ TEN 42.5 25–59.2 — — — —
Wan'Dale Robinson WR1 NYG DAL 54.5 34.8–75.6 — — — —
Christian Watson WR1 GB MIN 39.5 19.4–66.9 — — — —
Michael Wilson WR1 ARI LAC 52.0 34.4–77.1 — — — —
Tre Tucker WR1 LV MIA 35.5 15.9–51.2 — — — —
Parker Washington WR1 JAX CLE 47.0 29.8–70.7 — — — —
Josh Downs WR1 IND BAL 21.5 9–40.7 — — — —
Zay Flowers WR1 BAL IND 54.0 31–80.1 — — — —
Rashee Rice WR1 KC DEN 53.0 34.2–64.3 — — — —
Rome Odunze WR1 CHI CAR 35.5 18.1–61 — — — —
Tetairoa McMillan WR1 CAR CHI 48.5 26–66.9 — — — —
Elic Ayomanor WR1 TEN NYJ 25.0 6.4–51 — — — —
Deebo Samuel Sr. WR2 WAS PHI 29.0 14.8–43.5 draftkings 30.5 50.1% -0.4 pts
Romeo Doubs WR2 GB MIN 33.0 15.9–48.2 draftkings 33.5 50.1% +0.3 pts
Mike Evans WR2 TB CIN 46.0 24.2–69.1 draftkings 47.5 50.1% +0.1 pts
Cooper Kupp WR2 SEA NE 30.0 11.9–45 draftkings 28.5 50.1% +0.1 pts
DeAndre Hopkins WR2 BAL IND 13.5 0.5–27.7 — — — —
Davante Adams WR2 LA SF 38.0 21.6–73.2 — — — —
Amari Cooper WR2 BUF HOU 15.5 1.2–26.8 — — — —
Stefon Diggs WR2 NE SEA 26.0 12.1–43.1 — — — —
Tyreek Hill WR2 MIA LV 44.5 25.4–62.1 — — — —
Marquez Valdes-Scantling WR2 PIT ATL 14.5 0.9–25.3 — — — —
Jakobi Meyers WR2 JAX CLE 37.0 16.1–47.3 — — — —
Michael Pittman WR2 IND BAL 25.0 8.9–45.2 — — — —
Jauan Jennings WR2 SF LA 26.5 13.1–44.4 — — — —
Darnell Mooney WR2 ATL PIT 20.5 7.8–42.5 — — — —
CeeDee Lamb WR2 DAL NYG 49.5 25.2–71.1 — — — —
Tee Higgins WR2 CIN TB 39.0 20.7–66.8 — — — —
DeVonta Smith WR2 PHI WAS 42.5 21.2–62.8 — — — —
Jameson Williams WR2 DET NO 46.5 23.3–74.9 — — — —
Quentin Johnston WR2 LAC ARI 32.0 17.3–55.4 — — — —
Xavier Hutchinson WR2 HOU BUF 19.5 6.9–37.5 — — — —
Marvin Mims Jr. WR2 DEN KC 14.5 3.5–29.7 — — — —
Jordan Addison WR2 MIN GB 23.5 5.6–43 — — — —
Malik Nabers WR2 NYG DAL 60.0 36.3–80.7 — — — —
Devaughn Vele WR2 NO DET 27.5 13.8–40.5 — — — —
Jalen Coker WR2 CAR CHI 34.5 19.4–54 — — — —
Marvin Harrison Jr. WR2 ARI LAC 25.0 13.8–42.9 — — — —
Adonai Mitchell WR2 NYJ TEN 36.0 14.8–49.8 — — — —
Xavier Worthy WR2 KC DEN 22.5 8.3–39.5 — — — —
Chimere Dike WR2 TEN NYJ 18.5 6.5–45.5 — — — —
Luther Burden III WR2 CHI CAR 31.0 14.7–55.6 — — — —
Methodology. A LightGBM quantile model (q10–q90) trained on nflverse player-game data since 2021 projects each receiver's yardage and receptions distribution from role (target/air-yards share, snaps, depth chart, injuries), team tendencies (pass rate over expected, pace), opponent pass defense (EPA, pressure), and game environment (Vegas totals, spread, venue). Quantiles become a full probability curve, calibrated against three seasons of real closing lines, so P(over) is priced for the actual book line. Edges compare our probability to the de-vigged sharp market; validation is strictly walk-forward (the model never sees the week it predicts). Predictions freeze when published and grade automatically after games — history and CLV are tracked on every flagged edge.