This week
Justin is on the road against KC. We project 3.1 tackles, 0.2 sacks. 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
Justin has averaged 64% of his team's snaps across 20 games. That's a rotational-to-starter share. It's the number most likely to move his projection week to week, because a swing of ten points of snap share moves everything downstream with it. Opportunity is the first thing the model reads and the last thing it gives up on — production without snaps is noise, and snaps without production usually corrects.
What kind of linebacker he is
Justin has 80 tackles across 20 games — 4 a game. Add 4.5 sacks. Linebacker tackle counts track team context as much as ability — a defense that's on the field a lot, or one whose line occupies blockers, inflates them. The model reads volume against how many plays his defense actually faces rather than treating tackles as a raw total.
His last 5 games
Over that stretch Justin has 21 tackles and 1 sacks. Worth noting: his snap share is trending down — 55% lately against 64% overall. In a sport with seventeen games, a five-game window is closer to an anecdote than a trend — which is why usage, not this, anchors the number.
How the model reads him
The number on Justin is a share of a simulated game. First the matchup: possessions, field position, how each offense is likely to move. Then his slice of it, set by usage rather than by reputation. How those pieces are weighted is ours and gets re-tuned against results; the output is published and graded, which is the half you can check.
What we'd flag
Football's sample sizes are brutal — seventeen games a season means a "trend" is often four data points. Injuries and game script move usage more than talent does week to week, and a blowout can erase a starter's second half.