How Signals builds its redraft rankings.

Fantasy football has never had a shortage of opinions and noise. What it's always lacked is signals — the kind of clear, contextual insights that tell you not just what the numbers are, but what they actually mean for your team, right now.

That's the idea Signals is named for, and it's why we're publishing this guide. If we're going to ask you to trust our rankings on draft day — and on the Tuesday morning in October when your RB2 lands on injured reserve — you deserve to know exactly how they're made. Not a black box, not "our experts feel good about this guy." A real methodology you can read, understand, and make the judgement call for yourself.

Maybe the fastest way to explain what Signals is, though, is to tell you what we don't do. We don't watch film. We don't have a favorite team, a favorite player, or a take we're committed to defending. We don't rank based on vibes — although, in the spirit of full disclosure, we did once test whether vibes could be modeled, running sentiment analysis on football chatter to see if hype itself carried predictive signal (it didn't survive the backtest — more on our reject pile later). Every input to our model earned its place by proving, against years of historical seasons, that it actually predicts what happens next.

And still: data can only get so far. There will always be outliers, and there will always be silent signals — the thing you noticed in a preseason game, the beat writer you trust, the gut read no model can see. That's why Signals doesn't just allow you to override our rankings — we encourage it. Pin a player where you believe he belongs and the whole board reshapes around your call. Because we've all had that one player we nailed while the market slept on him, and there is no better feeling in fantasy football. Our job is to hand you the best possible starting point. The final board is yours.

Here's the short version, and then we'll walk through every piece:

Signals projects teams first, then players. We build a projection of every NFL offense as a whole — how many plays it will run, how often it throws and runs, how it scores — and then allocate that team's opportunity to the players competing for it. Prior player volumes, efficiency, age and injury outlook, and market wisdom refine the result. Because every player lives inside a team ecosystem, when reality changes — an injury, a trade, a breakout — the whole picture updates together, automatically.

But before we walk through how the model is built, let's start with what it's for — because a ranking is only as useful as its fit to your league.

Your league, your rankings — and why we draft by value, not by rank

The first thing to understand about a Signals board: we never store a generic fantasy point total and call it a day. The model produces a full, league-independent statistical projection for every player — the yards, catches, touchdowns, and games. Your league's actual scoring settings are applied to that stat line to produce your rankings. Half-PPR versus full PPR, superflex, tight-end premium, your league's exact roster requirements — these aren't footnotes, they reorder the board.

But scoring your league correctly is only half of it. The deeper question is how you should draft — and here we're proud to stand on the shoulders of giants.

The framework is called Value Based Drafting (VBD), and it isn't ours. It was created in the 1990s by Joe Bryant, the founder of Footballguys, and it remains, in our view, the single most important idea in fantasy football strategy. (Credit where it's due twice over: our own team first came to know VBD years ago through the work of the legendary Reddit user u/beersheets, whose spreadsheets taught a generation of drafters to think in value. This is our attempt to pay that forward.)

VBD's core insight: a player's value isn't how many points he scores — it's how many points he scores above what a freely available replacement at his position would give you. A quarterback projected for 350 points sounds far more valuable than a running back projected for 240, until you notice that the QB you could grab off waivers scores 280 while the replacement-level RB scores 130. The running back is the scarce asset. Drafting by raw points — or by consensus rank lists and ADP, which mostly measure popularity and cost, not value — systematically misses this. ADP tells you when a player is likely to be picked; it cannot tell you whether picking him there is right. If you're familiar with baseball's WAR — wins above replacement — this is the same idea wearing a football helmet: measure every player against the freely available alternative, and scarcity takes care of itself.

The hard part of VBD has always been pinning down replacement level, and this is where Signals goes further than a rule of thumb:

• We read your actual roster rules — how many starters your league requires at each position, and every flex slot you have to fill.
• We resolve your flex spots with history, not guesswork. A flex isn't "a WR slot with extra steps" — so we use the historically optimized mix of positions that actually fills flex spots in lineups like yours to determine how deep each position truly starts in your league.
• That pinpoints replacement level per position, for your league specifically — the last player who'd realistically be in a starting lineup, which is the honest baseline every starter should be measured against.
• Then every player is valued by his gap above that baseline. This is what lets the board weigh a top tight end against a mid-tier running back against an elite quarterback on one common scale — the scarcity of each position is priced in naturally, because it falls straight out of the replacement math rather than being bolted on as an opinion.

Finally, tiers: we draw tier breaks where the data shows real gaps in value — not at tidy, arbitrary intervals. When the board shows a tier cliff, that's the model telling you the drop-off is real, and it's usually the single most actionable thing on the screen come draft day.

So that's the output: a value-ranked, tiered board built for your league's exact rules. The rest of this guide is about the projections underneath it — how we get every player's stat line right in the first place.

Why teams first?

Fantasy points are, at their core, opportunity × efficiency. A wide receiver scores because he gets targets and does something with them. A running back scores because he gets carries. And here's the crucial part: opportunity is not a player stat. It's a team budget.

An NFL offense only runs so many plays. It only throws so many passes. There are only so many targets, carries, and red-zone trips to go around. Any projection system that builds player forecasts one at a time — this guy gets 140 targets, that guy gets 120, the new rookie gets 90 — will quietly project more footballs than the team actually possesses. Individually, every one of those projections can sound reasonable. Added together, they describe an offense that can't exist. Ja'Marr Chase can dominate targets in Cincinnati, or Tee Higgins can command a massive share, or the tight ends and backs can eat — but every one of those stories eats from the same plate, and August analysis has a way of serving each of them a full meal.

We think this is one of the most common ways fantasy analysis can go wrong, and it's rarely anyone's fault. When you evaluate players one at a time, optimism compounds invisibly. Nobody is checking whether the sum of everyone's breakout case fits inside 17 games of real football.

So we made the team the unit of account. In the Signals model, every team's opportunity is conserved — the shares have to add up. If you want to be more excited about one player, the model has to be correspondingly less excited about a teammate. That constraint sounds restrictive, but it's actually where a lot of our best calls come from: it forces every projection to answer the question "okay, but where do the touches come from?"

Step 1: Projecting the team ecosystem

Before we project a single player, we project all 32 offenses. For each team, the model builds an offensive ecosystem — a full statistical profile of the unit in real football units: total plays, pass/run balance, pass attempts, targets, carries, yardage, and how the team's scoring breaks down between touchdowns and field goals.

Several ingredients go into that team projection:

• The team's own recent history, weighted across multiple seasons. Not every team stat is equally "sticky" year over year — and we've measured that stickiness from nearly a decade of historical data rather than guessing at it (specifics below).
• Continuity. A team returning its coaching staff and quarterback is a very different forecasting problem than one breaking in both. The model explicitly treats stable and turned-over situations differently, because history says it should.
• The league-wide environment. Scoring, pace, and pass rates drift across the whole NFL from season to season. Every team projection is anchored to where the league actually is, not where it was three years ago. (Fun finding from our testing: the best forecast of next season's league-wide environment is simply last season's — every fancier trend and blend we tried failed to beat it.)
• The betting markets. Vegas win totals and implied team scoring are one of the most honest forecasts on earth — real money disagrees with lazy analysis very quickly. We use market expectations as a corroborating input on team quality, and we lean on them hardest exactly where team history is least reliable: teams with a new coach or a new quarterback.

What actually persists year to year (we checked)

"Some stats are sticky and some aren't" is easy to say, so here's what our year-over-year testing across recent NFL seasons actually found:

• Identity survives; luck doesn't. How often a team throws, and how it distributes targets, are among the most persistent things a team does — when the coaching staff and quarterback return, they're the closest thing to a fingerprint an offense has. Scoring level also carries over meaningfully in stable situations.
• Turnover changes everything. Fire the coach or change the quarterback, and persistence collapses across the board — pass rate loses much of its predictive power, and a team's scoring level retains only a fraction of it. A new regime really is close to a new team, and the model treats it that way (which is where the betting market input takes over the load).
• Some stats barely repeat at all. Interception rate is nearly noise year to year even when everything stays stable — a hard lesson for anyone projecting a QB's "turnover problem" forward — remember when Josh Allen had an interception problem? And after a coaching change, a team's split between touchdowns and field goals is statistically indistinguishable from a coin flip, so we regress it fully to league norms.

That table of measurements — not intuition — sets how much the model trusts each piece of a team's past.

The output is a projected ecosystem for every offense: the size of the pie each team will actually have to hand out.

Step 2: Allocating opportunity to players

Now the players enter — and this is where the model earns its keep.

Every player on a roster makes what we internally call a bid for their team's opportunity: a claim on targets, carries, and dropbacks based on their own body of evidence. The strength of that bid depends on who the player is:

• Returning players lean on the role they actually held on this team — measured carefully, using the weeks when the offense was at full strength, so a star teammate's injury absence doesn't distort what a player's real role looks like. Christian McCaffrey is the canonical case: in his injury-marred seasons, a naive season average made his role look modest, while the weeks he actually played told the truth — he was the offense. We measure the weeks, not the average.
• Arrivals via free agents and trades import the role they held on their old team, adjusted for how well volume historically travels with a player to a new situation. It travels — but not perfectly, and the model knows the difference. Saquon Barkley's move to Philadelphia in 2024 is the pattern working as intended: an established bellcow role traveled with him into a better ecosystem, and the result was a 2,000-yard season. Derrick Henry carried his role to Baltimore the same year and did the same thing. (One thing you won't find in the bid: the size of the contract. We tested whether acquisition cost adds predictive power once you account for the role a player already showed on the field — it added essentially nothing for the players who matter. The tape of usage beats the price tag.)
• Rookies bid based on draft capital and our prospect grades — the same scouting model that powers our prospect rankings, built from college production, athletic profiles, and draft pedigree. Draft capital does real work here, because teams tell you their plans with their picks: when Atlanta and Detroit spent top-15 selections on Bijan Robinson and Jahmyr Gibbs, those picks were workload announcements, and the model prices them that way.

Then the conservation math runs. All those bids get reconciled against the team's actual projected volume. Locked-in, high-conviction roles are protected near their established workloads, because our testing consistently shows that established top roles are the most reliably projected thing in football. A Ja'Marr Chase target share is not the thing you trim to make room for a camp darling. The squeeze, when there is one, falls on the murky middle and the deep bench, which is exactly where committee noise and camp hype live.

The result is a set of player workloads that are individually evidence-based and collectively possible. Every target we project for one player is a target we genuinely took from the team's pool — and from everyone else competing for it.

Step 3: Turning opportunity into points

Volume is most of the battle, but not all of it. Once the model knows a player's opportunity, a player-level layer projects what he does with it. Each player's own efficiency history is blended with positional norms, with the blend depending on how much evidence he's banked — a three-year track record moves the needle a lot; six hot games move it a little. This is disciplined regression to the mean, the unglamorous thing that keeps a model honest. De'Von Achane averaged nearly eight yards per carry as a rookie; no responsible model projects that to repeat, and the sustainable path to his value was always volume growth, not a repeat of the outlier rate.

Here's what the model actually looks at, position by position — and why:

Quarterbacks

• Pass attempts and dropbacks — the QB's slice of the team's passing pool. Volume is the foundation of every fantasy stat line, and for the QB it's nearly the whole team's passing volume.
• Yards per attempt — the core passing-efficiency stat, and one of the more stable ones. Notably, our testing found that fancier accuracy metrics (completion percentage over expected, on-target rate) predict next season's fantasy output worse than plain yards per attempt — so we kept the boring stat that works.
• Touchdown rate — projected primarily from volume and team scoring environment rather than last season's TD total, because TD rates swing hard year to year.
• Interception rate — regressed almost entirely to league norms, because next season's INT rate is essentially unpredictable (see the reject pile). Nobody "is" a 15-interception quarterback (with the possible exception of one of our favorites, Jameis Winston).
• Rushing volume — the fantasy cheat code. A quarterback's rushing role is among the most persistent traits he owns year to year, and rushing production is worth its weight in gold in fantasy scoring. A running QB has a floor a pure pocket passer simply doesn't.

Running backs

• Carries and share of the team's rush pool — the dominant driver of RB value, allocated through the team ecosystem.
• Yards per carry — used, but heavily regressed: YPC is one of football's noisiest stats, driven as much by blocking and one long run as by the back himself. (We even tested a charted "rushing yards over expected" metric as a de-noiser; it failed to add anything.)
• Receiving role: targets and receptions — the PPR floor, and something more: our testing confirms receiving production is far more resilient to bad team context than rushing is. A pass-catching back can survive a losing team; a pure grinder usually can't — think 2025 De'Von Achane vs Ashton Jeanty.
• Goal-line and touchdown work — projected from volume and team scoring, cross-checked against sportsbook TD lines, and deliberately not projected from last year's short-yardage role, which our testing found doesn't repeat reliably.
• Career stage — aging backs fade late in seasons and young workhorses ascend; more on age below.

Wide receivers

• Targets and target share — the king stat. Target volume is roughly as stable year to year as anything in football, which is exactly why the opportunity layer, not the efficiency layer, does most of the work at WR.
• Catch rate and yards per target — blended with positional norms in proportion to track record.
• Touchdown rate — regressed toward volume-implied expectation, sanity-checked against Vegas player TD lines. Raheem Mostert's 21-touchdown 2023 is the eternal reminder: TD spikes are real when they happen and almost never repeat.
• Development stage — the famous "year-3 breakout" window, which our testing sharpened into something more specific (below).

Tight ends

• Targets and role establishment — tight end is the most role-driven position in fantasy. An established featured role is gold; without one, history says to be skeptical, and the model treats unproven depth TEs with exactly that skepticism.
• Receiving efficiency composite — yards per team dropback plus yards-after-catch over expectation, the single strongest charted predictor we found for next-season TE production. Athletic TEs who create after the catch carry their value forward.
• Touchdown dependence — TE scoring leans disproportionately on TDs, which makes the position streaky by nature; the model prices the volatility rather than pretending it away.

Step 4: Age, development, and career arcs

Players don't produce on a flat line across their careers, and the shape of the curve is different at every position. Here's what our historical testing actually shows — including where the data is strong and where we're honest about hedging:

• Quarterbacks age best. Production holds essentially flat through the early 30s; measurable decline doesn't begin until around 34, and even then it's a slope, not a cliff. Our own curve originally started the fade too late — the data pushed us to begin it at 34, where retention drops meaningfully and keeps sliding toward 40.
• Running backs face the earliest wall. The cliff shows up around age 28: year-over-year retention drops sharply and the bust rate roughly doubles. RBs peak young — their best fantasy seasons overwhelmingly come in their early-to-mid 20s. And yes, Derrick Henry keeps making this paragraph look silly, which is exactly the point: the curve is a cohort average, not a verdict on any individual. Outliers are why the override button exists.
• Wide receivers decline latest and gentlest. WRs are broadly stable through 31, with the real drop arriving around 32 — the shallowest, most forgiving curve of the skill positions.
• Tight ends are the murkiest, and we'll say so. TEs develop late — rookie TEs almost never matter — yet aggregate production retention starts slipping earlier than you'd expect, with a small number of legends dragging the late-career averages up. Travis Kelce personally forced a fix here: our curve originally went flat in the late 30s, quietly treating a 37-year-old like a 35-year-old, and the board over-ranked him against every other signal until we extended the curve. TE samples are small and star-skewed; we hold this curve more loosely than the others.

Growth is a curve too, not just decline:

• The WR "year-3 breakout" is real — but it's more precise than the folklore. When we tested it, the signal wasn't the year number; it was finishing year 2 hot. A receiver who closes his second season on a tear disproportionately carries it into year 3. Amon-Ra St. Brown is the archetype: a torrid rookie December, a star by year 2, elite from then on.
• Year-2 running backs with premium draft capital are systematically underrated — including, historically, by us. Once a rookie season exists, models tend to throw away the draft-capital signal — which is how Jahmyr Gibbs could be projected as a mid RB2 into year 2 and finish as the overall RB2, and Bijan Robinson could be projected RB9 and finish RB4. Our model now keeps trusting premium capital into year 2, because the backtest says the market keeps under-doing it.
• Young quarterbacks improve more than models expect — from below. The QBs coming off rough rookie years (the Justin Fields and Trevor Lawrence year-2 leaps) are historically under-projected. Meanwhile the feared "sophomore slump" after a great rookie year barely exists in the data: for every C.J. Stroud-style fade there's a Justin Herbert and a Joe Burrow who just kept climbing — a cohort so small and so two-sided that we refused to ship a slump penalty at all (see the reject pile). We lift the under-rated bottom; we leave the top alone.
• Ascending young players get the benefit of the doubt. A player 23 or younger with a real NFL track record is still on the way up, and the model tilts accordingly.

One honest caveat over all of it: age curves are built on the players who survived long enough to be measured — decliners get cut, which flatters the averages. We treat every curve as a prior to be updated by current-season evidence, never a sentence to be served.

Step 5: Availability — projecting the games, not just the player

A projection of per-game production is only half a season projection. The other half is how many games the player actually plays — and this is an area where we've invested heavily in automation.

Signals runs a continuous injury and availability pipeline:

• Player status feeds and news are scanned multiple times every day.
• When a report includes a stated timeline ("expected to miss six weeks"), the model uses it directly.
• When it doesn't, we apply standard recovery windows by injury type and severity — a high-ankle sprain, a hamstring, and a torn Achilles are very different absences, and the model treats them that way, including distinguishing a fresh injury from a player rehabbing an old one.

Here's where the team-first architecture pays off in a way that still makes us smile: when a player's games drop, we don't have to write a single special rule to boost his teammates. His share of the team's volume shrinks with his availability, the conservation math re-runs, and his opportunity flows to the players who'd actually absorb it — the backup, the secondary receivers, the whole room. Think of Christian McCaffrey's 2024 injury: the instant his games projection would drop, Jordan Mason's workload projection would rise to absorb the vacated carries — not because anyone wrote a McCaffrey rule, but because San Francisco's carries had to go somewhere, and the model already knows where. Redistribution isn't a feature bolted on top of the model. It's what the model is.

Significant injury news doesn't wait for a weekly refresh, either. It triggers a rebuild, so the rankings you see reflect the football that's actually going to be played.

Step 6: The market check

We're a data-driven shop, which means we hold two ideas at once: our model sees things the consensus misses, and the consensus knows things our model can't.

Average draft position across major platforms is the distilled opinion of millions of drafters — including people reacting to beat reporting, camp observations, and context no statistical model ingests. Ignoring that would be arrogance, not analysis. So the final step of every projection is a calibrated blend against market consensus.

Two things make our version of this unusual:

1. We measured what a preseason rank is actually worth. Using years of historical preseason rankings matched against final results, we know what the typical player drafted at any given slot has genuinely gone on to score — not the fantasy of what the #1 pick could do, but the honest expectation of what that draft slot has historically delivered. That measured curve, not vibes, is what the model blends toward.
2. The market is a check, never the driver. The blend anchors our projections to reality at the top of the board, where consensus is most reliable — but through the middle rounds, where leagues are won, the model is deliberately allowed to disagree. When you see a player ranked meaningfully above or below his market cost on a Signals board, that's not noise we forgot to clean up. That's the whole point.

In season: a model that lives with your league

Draft-day rankings are a prior, not a prophecy. Once real games start, the model shifts into rest-of-season mode, and the same architecture keeps working:

• Team ecosystems update weekly, blending the preseason expectation with what each offense is actually doing — with recent weeks counting more than September. Evidence accumulates at different speeds for different things: raw volume stabilizes fast, efficiency takes longer, and the model's trust grows accordingly.
• Breakouts earn their way in. A player posting a huge role for a month isn't dismissed as a fluke or blindly extrapolated — his growing share of a measurable team pie pulls his rest-of-season projection up in proportion to the evidence. Puka Nacua's 2023 is the template: the record-setting September box scores were loud, but the durable signal underneath was a massive, immediate target share in a healthy offense — a role, not a hot streak. That's the kind of breakout this architecture is built to catch early: not by chasing points, but by watching the team's pie get re-sliced in real time.
• Injuries redistribute in near real time, exactly as they do in the preseason — the moment a starter's outlook changes, his teammates' projections move with it.

How we keep ourselves honest

A methodology page is easy to write. Discipline is harder, so here's ours:

• Everything is backtested before it ships. Every proposed model change replays against years of historical seasons — using only information that was actually knowable at the time — and has to beat the existing model on accuracy before it's promoted. No leakage, no hindsight. And a seat in the model isn't tenure: when the foundation improves, existing signals get re-tested against it, and the ones the upgrade made redundant are retired.
• We reject most of our own ideas. Our internal ledger of tested-and-rejected model changes is longer than the list of ones we shipped. Plenty of intuitively appealing adjustments simply don't survive contact with the data, and we'd rather kill a clever idea than ship a plausible one.
• We track our disagreements with the consensus and audit the biggest ones by hand, every season. Sometimes we find a bug. Sometimes we find conviction. Both outcomes make the model better.

From the cutting-room floor

Since we keep bragging about the reject pile, it's only fair to show you some of it. Every one of these sounded smart. Every one of these failed the test:

• Modeling the vibes. We ran sentiment analysis on football chatter to see if hype itself predicted anything the model didn't already know. It didn't. The market inputs already price the hype — by the time everyone's talking about a player, his ADP heard it first.
• QB accuracy metrics. Completion percentage over expected and on-target rate feel like they should be the holy grail of QB projection. In our testing they predicted next-season fantasy output worse than plain old yards per attempt — the advanced stat lost to the boring one, so the boring one plays.
• Predicting interceptions. Next season's INT rate is statistically indistinguishable from noise — the year-to-year correlation is essentially zero. Nothing we tried predicts it, so instead of pretending, we regress everyone toward the norm and move on.
• Last year's goal-line role. "He'll score because he gets the short-yardage work" — except short-TD share barely repeats year to year, and it didn't predict next season's touchdowns. TD equity follows volume and offense quality, not last year's goal-line depth chart.
• The RB receiving boost that flunked on Derrick Henry. Weighting RBs by receiving role made projections more accurate on average — and systematically demoted generational pure rushers. A model that's more accurate about the middle and wrong about Henry is a worse model (just adjust Henry the outlier yourself). Rejected.
• Strength of schedule. We compute it. We don't apply it to season projections — because when we measured it, August's projected schedule strength made rankings worse at every position. Preseason defensive projections just aren't reliable enough to move a season-long call.
• Late-season role as a crystal ball. "Watch how the backfield finished the year" is folk wisdom we tested four different ways — and the full-season role beat the late-season split every single time. The case that inspired the test refuted it best: Kyren Williams closed 2024 with the strongest stretch of his season, and his role collapsed the following year anyway. Committees re-form every offseason; a hot December is mostly a noisier version of the season it ended.
• The QB sophomore slump. After C.J. Stroud's year-2 fade, we tested whether great rookie QB seasons should be automatically discounted. The qualifying cohort was tiny — a handful of players — and split both ways: one true slump against multiple Herbert/Burrow-style continuations. We won't ship a rule built on a sample that small, and we'll say so plainly rather than dress it up. (What did validate: lifting the under-projected improvers coming off rough rookie years. That one shipped.)
• Paying for the price tag. Does a big free-agent contract predict volume? Once you account for the role the player already showed on the field, acquisition cost added essentially nothing for top-of-board players. Teams tell you their plans with usage; the contract mostly repeats what the film already said.
• The vacated-target windfall. When an alpha receiver leaves, the incumbent who stays behind must be the winner, right? On average, no — incumbents slightly decline after an alpha departs, because the offense that lost its best player got worse. The famous exceptions are real talents announcing themselves — and the market tends to flag those in advance better than any blanket rule we could write.

We keep this list because it's the difference between a model and a narrative. Anyone can tell you why their rankings are right. We'd rather show you the ideas we killed to get here.

We're a small team building the fantasy platform we always wanted — one that connects your roster to your actual league context and gives you a clear verdict, whether you check it once a week or every day. We'd rather show our work and improve it in the open than pretend any model is finished.