How Offensive Measures Projects Players

Most fantasy rankings start with a name and a feeling. We start with a question: how many chances is this player going to get, and what does he tend to do with them? Everything in our model is built around that order. Opportunity first, then efficiency, then the context that shapes both. The result is a projection you can trace back to inputs, not vibes.

Opportunity comes first

Targets, carries, routes run, and trips inside the red zone are the foundation. There is a simple reason for that: volume is the most stable, most predictable thing a player carries from one season to the next. A receiver's target share or a back's touch count tells you far more about next year than a highlight reel does. So before we ask whether a player is good, we ask how central he is to his offense.

Efficiency, but earned and regressed

On top of opportunity we layer efficiency: yards per target, yards after the catch, value added per play. These separate the players who merely get volume from the ones who turn it into points. But efficiency is noisier than volume. A great catch rate or a gaudy yards-per-carry can be a real skill or just a lucky season. We treat it accordingly, leaning on what is likely to repeat rather than chasing last year's outliers.

Context changes the math

No player produces in a vacuum. A new teammate can siphon targets; a coaching or scheme change can reshape a backfield; an offensive line can make or break a running game. We account for the competition for touches and for the situations that quietly raise or lower a player's ceiling, the things that don't show up if you only look at last year's stat line.

Availability and age

A projection is only useful if the player is on the field. We shrink expectations for time missed and for the recovery curve after serious injuries, and we fold in the realities of aging: when production tends to rise, plateau, and decline by position. The aim is an honest floor as much as an exciting ceiling.

Touchdowns regress

Touchdowns win fantasy weeks, but they're among the most volatile stats year to year. Rather than assume last season's total repeats, we pull a player's scoring back toward what his usage (goal-line work, red-zone targets, total opportunity) says he should expect. That keeps us off the players due to come back to earth, and onto the ones primed to bounce up.

Mostly math, with a human check

The model is mostly mathematical and automated. The projections come out of the data, not out of a room full of opinions. But we don't run it blind. We watch where our numbers deviate sharply from consensus, because a big gap is often a signal that something happened off the stat sheet: a quiet trade, a camp report, a role that just changed. When a deviation traces back to a real narrative the data hadn't seen yet, we account for it. The rest of the time, we trust the math.

Tested, not asserted

Every change we make has to earn its place. Before anything ships to the board, we backtest it against seasons the model never trained on, and we run it multiple times to make sure the result isn't a fluke. If an idea doesn't beat the benchmark consistently, it doesn't make the cut, no matter how good the story sounds.

The point

We're not trying to be contrarian, and we're not trying to match consensus. We're trying to be right, and to show our work along the way. That's the whole idea behind Offensive Measures: data-driven projections you can actually reason about.