This week
Justin is hosting GB. We project 61.4 receiving yards on 8.2 targets. The number comes out of a drive-level simulation of the game rather than a season average — it asks how many possessions his side is likely to get, what they do with them, and how much of that flows through him.
How much he's on the field
94% — that's Justin's average snap share over 20 games. That's a full-time role. Snap share at this level is the most stable input we have — it changes slowly, and it survives a bad game in a way that yardage never does. This is the number to check first on any football page. Everything else here is downstream of it.
What kind of receiver he is
Justin has caught 100 of 169 targets (59%) for 1252 yards and 2 touchdowns — 7.4 yards per target. Yards per target is the honest efficiency number here, because catch rate mostly measures how far downfield he's used: a slot receiver on screens will always catch more of them than a deep threat, without being better.
His last 5 games
Over that stretch Justin has 252 total yards. A handful of games is thin evidence in a seventeen-game season. It nudges the projection; it doesn't drive it.
How the model reads him
Justin's projection starts with the game, not with him. We simulate the matchup drive by drive — how many possessions each side gets, how far they travel, how often they score — and then ask what share of that flows through him given his role. That ordering matters: a productive player on a team that runs 55 plays a game has a lower ceiling than a lesser one on a team that runs 70.
What we'd flag
Read this knowing the limits. A seventeen-game season makes every rate noisy; game script can invert a player's usage inside one afternoon; and depth-chart changes often surface too late to be in the projection.