A walkthrough of the four numbers I publish on every player (Dynasty Value, Current Value, Future Value, and the ๐จ Dynasty Score) and what feeds them.
The goal of the model is simple. Take everything I can reasonably know about an NFL player, fold it into one number, and lay out where that number came from so you can decide whether you trust it.
The big idea, in a paragraph
Every player runs through the same process. The inputs are the things that hold up as predictors of future NFL production: age, role, draft capital, scouting grades, NGS athleticism, college production, advanced tape grades, and live in-season usage. Each position has its own model that turns those inputs into a points-per-game estimate. From there I age the player forward three seasons, discount the future, layer in contract leverage and momentum, then compare the result to what the dynasty market is paying. The gap between my number and the market's is where most of the trade signals come from.
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My league of record
Everything on the site is built for a 12-team, full-PPR, Superflex league. The rankings still hold up in shallower or standard formats, but the raw numbers are anchored to that setup so the math stays comparable across players.
The four outputs you see on every player
Every player has four headline numbers. Three are different views of his value. The fourth, the ๐จ Dynasty Score, is the one I sort by when I want a head-to-head ranking across positions.
Current + Future = Dynasty. The ๐จ Dynasty Score sits on top, blending those views with multi-year projections and contract leverage.
Current Value
What the player gives you this season, expressed as age-adjusted projected output. "If I had to win in the next 17 weeks, how valuable is this asset?"
Future Value
The discounted sum of Year 1, Year 2, and Year 3 projections. Future seasons are worth less than today (injury risk, role uncertainty, contracts running out) and the math reflects that. The Methodology page covers how the discount works.
Dynasty Value
Current Value plus Future Value. The full long-term picture for the player as a dynasty asset.
๐จ Dynasty Score
My headline ranker. A blend of Dynasty Value, multi-year projections, recent momentum, and contract leverage. This is the column the leaderboard sorts by, and the basis for the letter grade you see next to each name.
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Why four numbers and not one?
Different teams need different reads. A contender weighing a buy-now trade should look at Current Value first. A rebuild that just sold its veteran QB should look at Future Value first. The ๐จ Dynasty Score answers the general "is this guy good?" question without caring about your build.
What goes into a value (high-level)
I don't lean too hard on any single source. The model pulls from a mix of public and paid feeds, and it weights them differently by position because what predicts WR production is not what predicts QB production.
Production signals
Play-by-play, NFL Next Gen Stats profiles, advanced tape grades (passing, pressure, receiving, rushing), and rolling-window team and offense metrics. These tell me what's happening on the field right now.
Talent priors
Draft capital, college production share, my own scouting grade, athleticism scores, size scores, and an imputed film component. These tell me what to expect over a career, especially before in-season usage settles in.
Context & leverage
Depth-chart role, contract structure (years left, APY as a percent of cap, guarantees), team offensive environment, and coach tendencies. These tell me whether the player's opportunity is sticky or fragile.
Market reality
A consensus dynasty market value from a public 12-team Superflex feed. I don't lean on it, but I always compare to it. The disagreement is the actionable part.
The pipeline, in five stages
From kicking off the run to the new leaderboard going live, the pipeline runs the same five stages every week.
Every stage writes to a stored table. Past seasons stay frozen; only the current season gets recomputed on a normal weekly run.
Ingest โ Pull fresh play-by-play, NGS, advanced grading data, depth charts, contracts, and my own scouting roster.
Score โ Run each player through his position's structural model to get an age-adjusted points-per-game estimate.
Age โ Apply the positional aging curve, then roll the player forward to project Year 1, Year 2, and Year 3.
Discount โ Discount the future so next season counts more than 2027, then layer in contract leverage and momentum.
Rank โ Compute Value Over Replacement against a positional baseline, turn it into percentiles, assign letter grades, and compare to the consensus dynasty market.
The ๐จ Dynasty Score is a blend on purpose
The headline ranking is not pure model output, and that's on purpose. I average my model's percentile with the dynasty market's percentile. It keeps the score honest when one side is temporarily off, and it stops a single perspective from being mistaken for the truth.
When the two halves disagree, that gap is what powers the Buy Low / Sell High signals on the Market Watch page.
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Where to see the disagreement
On the live values page, head over to Market Watch. Players whose model rank sits well above their market rank surface as Buy Low. Well below, Sell High.
Rookies and cold-start cases
Rookies are the hardest call in any dynasty model. They have no NFL production yet, and what tape they do have was generated against college defenses. For rookies I lean much harder on the talent-prior side (draft capital, scouting grade, athleticism, size, college production) and I widen the uncertainty band on their projection until real NFL games update it.
ExampleEarly-round rookie WRWRAge 22
Top-50 draft capital, strong college production share, elite athleticism, but zero NFL snaps. The structural model returns a confident scouting-driven baseline. Once games are played, the Bayesian update (the math that re-weights the scouting prior toward live in-season production as data accumulates) will pull that baseline around fast in the first month of the season.
Takeaway:Expect his Current Value to swing more than a veteran's in Weeks 1โ4. That's the model learning, not noise.
ExampleLate-round rookie RBRBAge 23
No draft capital, decent college efficiency, weak athleticism profile. The prior is modest. Without a depth-chart role, the Future Value stays low even if the player looks fine on tape.
Takeaway:A late-round RB needs opportunity, not just talent, to climb the board. Watch the depth chart, not just the highlights.
Why I built it this way
I wanted a system I could audit. Every player's history is stored. Every snapshot is timestamped. Every ranking can be reproduced by re-running the pipeline. If I can't trace a number back to the inputs that drove it, that's a bug to fix.
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From here
The Methodology page goes a level deeper on the math: the aging curves, the discount rate, and how a percentile becomes a letter grade. The How To Use page is the practical side: which column to read when.