This week
Carl is on the road against DET. We project 2.6 tackles, 0.3 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
Carl has averaged 68% 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 defensive lineman he is
Carl has 7 sacks and 66 tackles across 20 games. Sacks are the stat everyone quotes and the least stable one on this page — they arrive in bunches, they depend on coverage holding up behind him, and a lineman can win every rep for a month without one. Snap count and pressure rate describe a pass rusher far better than a sack total does, which is why his projection moves less on a big game than you'd expect.
His last 5 games
Over that stretch Carl has 17 tackles and 1.5 sacks. 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 Carl 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.