Methodology
The inputs are public. The weights are not. Here is everything in between.
The promise
Every ranking on this site is published as a dated post and never edited afterward. The change column is computed against the prior week's file, not typed, which means it cannot be quietly adjusted to look smarter in hindsight. If a call was wrong, the archive says so. Corrections get their own box; the original stays where it was.
That is the whole idea. Plenty of people publish rankings. Very few show you how far each player moved, why, and what they said last week.
How a rank gets made
The model starts with the team, not the player. Each of the 32 offenses gets a projected season total for rushing attempts, pass attempts, yards and touchdowns, seeded from Vegas win totals and last season's rates, regressed toward the league. Those totals are the ceiling. No amount of optimism about a player can add carries the offense doesn't have.
Each player is then assigned a share of his team's totals — his slice of the carries, his slice of the targets. A share, multiplied by a team total, becomes a stat line. A stat line, scored in half-PPR, becomes points per game. Points per game becomes the rank.
The shares are where judgment lives, and the model is honest about that. A returning starter inherits last season's role by default. A player on a new team, or a rookie, has no default worth trusting — those are set by hand, and the write-up says so.
What the metrics do
Four proprietary metrics carry the football rankings. None of them touches the rank directly. Each one multiplies a specific stat, which is a deliberate constraint: because every metric acts on a named input, every rank change traces back to a number, and the number traces back to a reason. That is the sentence in the movers sidebar every week.
OTS — Opportunity Trend Score
The recent trend in a player's share of team opportunity, normalized across the league. Opportunity means targets for receivers and tight ends, carries plus targets for running backs. A rising OTS means the role is growing before the box score shows it. OTS scales projected volume, and it works at every skill position, which makes it the spine of the system.
RCS — Role Clarity Score
Running backs only. Volume metrics tell you how much a back is used. RCS tells you what kind of back the team thinks it has, by measuring his share across the situations that matter — early downs, passing downs, the red zone, and the two-minute drill. A back who owns early downs but disappears on third down is a different asset from one who stays on the field in all four. RCS is scored 0–100 and labeled Workhorse, Feature Back, Role Player or Specialist. It scales projected rushing touchdowns, because goal-line trust is what the label is really measuring.
SFS — Scheme Fit Score
Receivers and tight ends. Compares a player's own profile — his share of air yards, his depth of target, what he does after the catch — against the tendencies of the offense he plays in. It matters most after a coordinator change or a trade, which is exactly when rankings move the furthest and explanations are thinnest. SFS scales projected receiving yards.
PGHI — Passing Game Health Index
Quarterbacks. A composite of the passing environment — how often the offense throws relative to expectation, how the protection is trending, whether the weapons are on the field, and how fast the offense plays. It answers whether things are getting better or worse for the quarterback before his stat line reflects it. PGHI scales projected passing touchdowns.
RVI — Role Volatility Index
Basketball. Combines minutes variance, usage variance, active-game rate and lineup stability into a weekly score. A player averaging 32 minutes with wild night-to-night swings is a different asset from one steady at 32. Fantasy basketball is unusually exposed to this problem, and nobody tracks it as a weekly signal.
What stays private
The inputs above are public. Every one of them is built from play-by-play and roster data that anyone can download. What is not public is how they are weighted — the coefficients, the regression targets, how much each metric is trusted in September versus November. That is the work, and it stays on this side of the page.
This is not a secrecy fetish. It is the difference between telling you what the model looks at and handing you the model. The first is what makes a ranking worth trusting. The second is what makes it worth copying.
Where judgment comes in
The model produces a number for every player. The published rank is that number, plus a visible adjustment when I disagree with it — measured in points per game, not rank spots, and always with a written reason. An adjustment without a reason is a bug the workbook flags before anything publishes.
The annual Flag Plant is the purest version of this. It is a rookie call made on film and instinct, published before the season, graded honestly after it. The model has an opinion. So do I. When they disagree, you see both.
Tiers and formats
Tiers are drawn where the points-per-game cliffs actually are, not at even intervals. The gap between tiers is the useful information; the tier number is just a label.
Football rankings default to half-PPR redraft. Basketball defaults to nine-category roto. Superflex, dynasty and points-league variants are planned.