Gronkowski Trade-Up
Up
New EnglandpVAR
- 2010#42Rob Gronkowski93.7
Down
OaklandpVAR
- 2010#44Lamarr Houston34.3
- 2010#190Travis Goethel-0.1
Scoring the 2026 NFL Draft Trades
A best-player analysis of every pick-for-pick swap.
Every offseason, NFL teams give up two, three, sometimes four picks to climb the draft board for a player they have their eye on. Sometimes the bet pays. Kansas City moved up to ten in 2017 and took Patrick Mahomes. Atlanta climbed to six in 2011 and took Julio Jones. Buffalo moved up to seven in 2018 and took Josh Allen. Often it does not. San Francisco traded three first-round picks plus a third for the third overall slot in 2021 and used it on Trey Lance, who started four games before being shipped to Dallas for a fourth-rounder.
The natural assumption is that the team trading up should usually come out with the better player. They hold the highest pick of the deal, and the test is simply who ended up with the best one. Making the trade-down side look good would seem to require summing the value of all those later picks against the one early one. It does not. Even without summing anything, the trade-down side wins the best-player comparison more often than not. Several lower swings, taken together, beat one higher swing more often than intuition suggests.
Three case studies make it concrete. Each card lists what every pick involved became and flags the side whose best pick produced more career pVAR. New England's 2010 trade-up for Rob Gronkowski is a runaway win for the trade-up side: Gronkowski became the most productive tight end of his generation, and Oakland's two picks in return came nowhere close. Cleveland's 2011 trade-up for Phil Taylor is the inverse, but only because Kansas City's later pick (#70) became Justin Houston, an All-Pro whose career far outproduced Taylor's; their earlier pick (#26, Jonathan Baldwin) was a bust. Philadelphia's 2010 trade-up for Brandon Graham is the closest call. Graham's career outproduced any single one of Denver's three picks, but the three together (Dez Bryant, Eric Decker, and Ed Dickson) combined for considerably more pVAR than Graham alone. Under our conservative best-player methodology, the trade still counts as a successful trade-up for Philadelphia.
Phil Taylor Trade-Up
Up
ClevelandpVAR
- 2011#21Phil Taylor10.0
Down
Kansas CitypVAR
- 2011#26Jonathan Baldwin0.5
- 2011#70Justin Houston81.5
Brandon Graham Trade-Up
Up
PhiladelphiapVAR
- 2010#13Brandon Graham78.8
Down
DenverpVAR
- 2010#24Dez Bryant69.0
- 2010#70Ed Dickson7.8
- 2010#87Eric Decker47.7
How often has each side actually won?
Conservative best-player test applied to 304 historical trade-ups, 2006–2020 NFL drafts. The trade-down side has won more often than the trade-up, and the gap widens at the top of the draft.
Trade-up wins Coin flip Trade-down wins
Alln = 304
Top 10n = 17
Late R1n = 48
R2n = 76
R3n = 53
R4+n = 110
Recent-era trade-ups (2014–2020) have closed the gap from earlier years, suggesting better pricing in the modern draft.
The methodology is intentionally conservative, and the conservatism cuts in favor of the trade-up side. Pick values are not summed. Trade-value charts do not enter. The only question is which side ended up with the better single player, judged by career pVAR. The Brandon Graham case above is exactly the kind of trade this rule favors: Denver's three picks combined for considerably more pVAR than Graham, but Graham individually outproduced each one, so the deal counts as a Philadelphia win. That is by design. By scoring on best player only and ignoring the secondary picks entirely, we stack the deck for the team that traded up before the simulation even starts. The historical finding holds anyway. Across every pick-for-pick trade since 2006, the trade-up side wins this comparison 34% of the time, the trade-down side 45%, with the remaining 21% inside a five-pVAR coin-flip band where neither side's best is decisively better.
For each pick, the simulation samples 500,000 times from the careers of players historically taken at and near that slot, with closer slots weighted more heavily. In position-weighted mode it gives extra weight to same-position history. The pool covers the 2006–2020 mature draft window.
The actual player drafted does not enter the math, and the names shown on the cards are nominal. The pool for any given pick spans the full historical range of what that slot has produced, from franchise quarterbacks to outright busts. Which end a particular 2026 player will land at is the gamble a team takes when it trades up. Trades involving veterans are excluded.
Two reference tables follow. The first shows how outcomes distribute by position and draft slot. The second pairs each pVAR tier with a familiar career, so the numbers read as something concrete.
pVAR by Position and Draft Slot
2006–2020 draft classes. Sample size in parentheses.
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Offensive linemen are the most reliable returning position through the first two rounds, with the highest averages and lowest bust rates of any group. Linebackers sit at the opposite end of round one, with the highest bust rate and the lowest average. Quarterbacks are bimodal: at the top of the first round, a handful of franchise hits prop the average up while half of the picks miss outright, which is why the Avg and Median columns disagree so sharply for QBs in that bucket. Position-weighted mode carries these positional distribution differences into the simulation. Our position-agnostic mode does not, it uses only the overall distribution by pick slot.
To put the pVAR numbers in context, here's one well-known career per position and tier.
What these numbers look like in real NFL careers
Players selected from the 2006–2021 NFL Drafts.
| Pos | Solid≥20 | Good≥40 | Great≥60 | Elite≥70 | Star≥80 | HOF≥90 |
|---|---|---|---|---|---|---|
| QB | Nick Foles | Jay Cutler | Joe Flacco | Andrew Luck | Dak Prescott | Patrick Mahomes |
| WR | Jason Avant | Jeremy Maclin | DeSean Jackson | A.J. Green | DeAndre Hopkins | Calvin Johnson |
| RB | Najee Harris | Matt Forte | Alvin Kamara | Marshawn Lynch | Christian McCaffrey | Derrick Henry |
| TE | Evan Engram | Marcedes Lewis | Jimmy Graham | — | George Kittle | Travis Kelce |
| DT | Dontari Poe | Marcell Dareus | Gerald McCoy | Ndamukong Suh | Fletcher Cox | Aaron Donald |
| EDGE | Matt Judon | Elvis Dumervil | Za'Darius Smith | Maxx Crosby | Nick Bosa | Micah Parsons |
| LB | Myles Jack | K.J. Wright | Clay Matthews | Lavonte David | Fred Warner | Bobby Wagner |
| CB | Xavier Rhodes | Joe Haden | Aqib Talib | Jalen Ramsey | — | Richard Sherman |
| S | Roman Harper | Jairus Byrd | Eric Berry | Harrison Smith | Eric Weddle | — |
The Trades
One card per trade. The bar at the top of each card shows how often each pick produced the trade's best player across the simulations. The total above each side is the share of simulations in which that side's best beat the other side's.
Based on 2006–2020 pVAR outcomes at these pick slots and positions — player names are nominal.
Cowboys
#11·Caleb DownsSAF
traded for
Dolphins
#12·Kadyn ProctorOT
+ #177, #180
Who lands the best player?best of 1 DAL pick vs best of 3 MIA picks · position-weighted
45%CowboysDolphins55%
11% of simulations land within the 5-pVAR coin-flip band, where neither side's best is decisively better.
Where each side’s best pick landsaggregate = best of all picks
Cowboystheir only pickR1 · #11
Dolphinsbest of 3 picksR1 · #12R6 · #177R6 · #180
Per-pick odds by tiereach pick's chance of landing in this tier
| Pick | Player | Bust<20 | Solid20–40 | Good40–60 | Great60–70 | Elite70–80 | Star80–90 | HOF90+ | E[pVAR] |
|---|---|---|---|---|---|---|---|---|---|
| R1·11 | Caleb DownsSAF | 27% | 28% | 24% | 8% | 6% | 3% | 3% | 39.6 |
| R1·12 | Kadyn ProctorOT | 27% | 23% | 25% | 10% | 4% | 6% | 4% | 41.1 |
| R6·177 | Kevin Coleman Jr.WR | 89% | 7% | 3% | <1% | <1% | <1% | 1% | 6.5 |
| R6·180 | Seydou TraoreTE | 90% | 7% | 2% | <1% | <1% | <1% | <1% | 6.5 |
Based on 2006–2020 pVAR outcomes at these pick slots and positions — player names are nominal.
Dolphins
#27·Chris JohnsonCB
+ #138
traded for
49ers
#30·Omar Cooper Jr.WR
+ #90
Who lands the best player?best of 2 MIA picks vs best of 2 SF picks · position-weighted
47%Dolphins49ers53%
12% of simulations land within the 5-pVAR coin-flip band, where neither side's best is decisively better.
Where each side’s best pick landsaggregate = best of all picks
Dolphinsbest of 2 picksR1 · #27R5 · #138
49ersbest of 2 picksR1 · #30R3 · #90
Per-pick odds by tiereach pick's chance of landing in this tier
| Pick | Player | Bust<20 | Solid20–40 | Good40–60 | Great60–70 | Elite70–80 | Star80–90 | HOF90+ | E[pVAR] |
|---|---|---|---|---|---|---|---|---|---|
| R1·27 | Chris JohnsonCB | 41% | 24% | 24% | 5% | 3% | 2% | 1% | 30.2 |
| R5·138 | Kyle LouisLB | 84% | 11% | 4% | 1% | <1% | <1% | <1% | 8.6 |
| R1·30 | Omar Cooper Jr.WR | 42% | 24% | 20% | 6% | 4% | 3% | 1% | 30.4 |
| R3·90 | Kaelon BlackRB | 72% | 17% | 7% | 2% | 1% | 1% | <1% | 14.7 |
Based on 2006–2020 pVAR outcomes at these pick slots and positions — player names are nominal.
Panthers
#151·Zakee WheatleyDB
+ #227
traded for
Dolphins
#158·Michael TaaffeS
+ #200
Who lands the best player?best of 2 CAR picks vs best of 2 MIA picks · position-weighted
48%PanthersDolphins52%
37% of simulations land within the 5-pVAR coin-flip band, where neither side's best is decisively better.
Where each side’s best pick landsaggregate = best of all picks
Panthersbest of 2 picksR5 · #151R7 · #227
Dolphinsbest of 2 picksR5 · #158R7 · #200
Per-pick odds by tiereach pick's chance of landing in this tier
| Pick | Player | Bust<20 | Solid20–40 | Good40–60 | Great60–70 | Elite70–80 | Star80–90 | HOF90+ | E[pVAR] |
|---|---|---|---|---|---|---|---|---|---|
| R5·151 | Zakee WheatleyDB | 85% | 10% | 4% | 1% | <1% | <1% | 1% | 7.8 |
| R7·227 | Jackson KuwatchLB | 95% | 4% | 1% | <1% | <1% | <1% | <1% | 3.5 |
| R5·158 | Michael TaaffeS | 84% | 9% | 4% | 2% | <1% | <1% | <1% | 8.2 |
| R7·200 | DJ CampbellG | 88% | 8% | 3% | <1% | <1% | <1% | <1% | 6.2 |
How this works
The conservative question
The mode on this page asks one question of every trade: of all the picks involved on either side, whose best pick produced more career pVAR? It doesn't have anything to do with fair value on the trade chart, or even who is likely to get more total pVAR. Just whose best selection comes out higher. It is a deliberately narrow test that gives no credit for depth and ignores everything except the top of each side's haul. (For a longer look at how teams have actually fared at ordering the draft over the last fifteen years, see Are NFL Teams Getting Better at Drafting?.)
Why the trade-up bet is hard to score
The familiar way to evaluate a draft trade is the pick-value chart that Jimmy Johnson popularized in the early 1990s. Each pick gets a point value, the values on each side are summed, and the trade is judged fair when the sums roughly match. Better versions of the chart exist now, and the site's pick value curve writeup covers the modern alternatives. The form is the same in all of them. A value per pick, added up across each side.
That arithmetic works for matching capital at the margin, but our pVAR does not lend itself to the same operation. Career value sums in raw points, but rosters do not benefit from one elite career and three competent ones in proportion to those raw points. Two pVAR-40 careers and one pVAR-80 career do not occupy the same place on a roster, do not earn the same paycheck, and do not produce the same number of playoff appearances. The harder a metric tries to add multiple careers across a trade, the further it drifts from how a front office actually experiences the deal.
Simulating one pick
The 2006–2020 mature window has roughly 3,800 player-picks with stable pVAR. Every player in it has had five or more NFL seasons, which is long enough for the career distribution to settle. To build an outcome distribution for a given draft slot, we weight every historical player by how close his pick was to that slot, using a Gaussian kernel that runs tight near the top of the draft and widens through the late rounds (sigma climbs from 5 to 20). A simulation draws one pVAR from the weighted pool.
We resample from history rather than fit a parametric distribution because the empirical data doesn't lend itself to a clean closed form. Quarterback outcomes are bimodal: teams either hit on a quarterback or they don't, and most of the mass sits at the two extremes. The late rounds are zero-inflated. Half of the picks return nothing but every so often an Antonio Brown or Jason Kelce comes out of the sixth. A fitted distribution would smooth those edges off. Bootstrapping from the actual draws preserves the empirical distribution's shape.
Position-weighted mode tilts the draws toward the drafted player's position. With probability 0.35 a sample comes from the same-position window only. With probability 0.65 it comes from the position-agnostic window. The reason for a soft mixture rather than a hard filter is sample size. A pure-cornerback distribution at pick six has roughly twenty samples across fifteen drafts, too thin to read as signal at the tails. The mixture recovers most of the positional information without giving up the broader pool's stability.
Scoring the trade
Each trade runs through 500,000 simulations. Every pick on every side draws a pVAR independently, each side keeps its highest, and the two highs are compared. A side wins the simulation when its best is more than five pVAR ahead of the other's; anything closer counts as a coin flip.
The five-point band is a concession to noise. pVAR is calibrated to about a single point of precision, bootstrapping adds its own sampling variance, and gaps narrower than five pVAR sit inside what most observers would call a draw. Each card carries an italic note beneath its stacked bar, reporting how often the simulation lands inside the band. When that share is high, the trade is too close to call regardless of how the headline percentages tilt.
Reading the headline bar
The stacked bar at the top of each card answers the conservative question directly. Each segment is one pick, sized by the share of simulations in which it produced the trade's highest pVAR. The percentage above each side is the share of simulations in which the trade's best player came from one of that side's picks. A single-pick side shows up as a single segment; a three-pick side shows exactly how the case is distributed across its picks.
That decomposition is where the trade-back logic shows itself. Three picks each with a 10% chance of clearing 60 pVAR collectively offer roughly a 27% chance that at least one lands. That is about what a single top-20 pick produces on its own, only distributed across more independent swings. When the Bills traded back three times in the first round of the 2026 draft, that was the math they were playing.
The per-pick tier table
Each pick has its own row at the bottom of the card, broken into seven tiers: Bust (<20 pVAR), Solid (20–40), Good (40–60), Great (60–70), Elite (70–80), Star (80–90), and HOF (90+). The cells in a row sum to one and together describe the full distribution of how that pick is likely to resolve. A pick at 141 lands in the Bust tier roughly 70% of the time. A pick at 6 lands in Great-or-better closer to 35%. The heatmap moves monotonically in both pick position and tier.
What this doesn't capture
The simulation does not model the actual player drafted. The names shown on each card are nominal. They identify which player the slot turned into in the 2026 draft, but the math behind the percentages comes only from the slot itself and, in position-weighted mode, the position group. Whether a particular pick at #6 in 2026 turns out closer to Patrick Mahomes or closer to Mitchell Trubisky is the outcome the simulation is sampling over. It does not know which.
The simulation also does not model 2026 class strength, the drafting team's positional need, coaching or scheme fit, or any other team-specific factor. It pools all 32 teams across fifteen draft classes and treats picks as independent draws.
Future picks, marked with a leading tilde, are modeled at the slot they project to. No future-pick discount is applied, even though the rest of the league does discount them in practice.
Position coding uses the site's canonical groups, so interior offensive line isn't distinguished from tackle and edge rushers are collapsed into defensive end.
Background reading: Introducing pVAR, The Draft Pick Value Curve, Are NFL Teams Getting Better at Drafting?, and the original Massey & Thaler (2013).
