What if the same draft trade looks like a steal and a rip-off depending on which chart you use?
Teams assign point values to each pick so GMs can judge trades fast.
But different charts, like Jimmy Johnson’s gut-based scale, Harvard’s data model, or the surplus-value method, can send opposite signals on the same deal.
This post breaks down how those charts work, why they disagree, and when a pick swap really makes sense.
By the end you’ll know how teams calculate fair pick swaps and what to watch on draft day.
How the Draft Trade Value Chart Works for NFL Teams
Draft trade value charts put a number on every pick so teams can figure out whether a swap makes sense. Here’s the basic idea: a GM looks up the point value for each pick in a proposed deal, adds up both sides, and compares the totals. If they’re close, it’s fair. If one team’s way ahead on points, they either back out or ask for more to even things up.
Pick values come into play the second trade talks start. Say a team wants to jump from 31 to 18. The chart tells them if tossing in pick 74 makes it work. Back in 2013, the Cowboys sent 18 for 31 and 74. Under the old Jimmy Johnson chart, pick 18 was worth 900 points and 31 plus 74 came to 820, so Dallas gave up value. But the Harvard chart scored 18 at 249.2 and the return package at 321.4, meaning the Cowboys actually won. Same trade, opposite conclusions, all because the two charts weight early picks differently.
Different systems can tell you totally different things about the same deal. The Johnson chart runs on gut feel, the Harvard model uses Career Approximate Value regression, and surplus frameworks measure on-field production against what you’re paying on rookie deals. Teams pick whichever fits their style and tweak from there.
What teams look at in real time:
- Pick point values from their chosen chart
- Total value on both sides of the swap
- Adjustments for position scarcity and what they need right now
- Roster fallout, cap space, and how long it’ll take to develop the player
Origins of the NFL Draft Trade Value Chart: The Jimmy Johnson System
Jimmy Johnson rolled out the first widely used draft trade value chart around 1989 when he got to Dallas. He stuck fixed point values on every pick, with the scale tilted hard toward the top. Johnson’s setup gave the first overall pick 3,000 points, the tenth about 1,300, and the 32nd around 590. The steep drop reflected his belief that elite early picks delivered way more talent than mid rounders, so they were worth multiple selections. The thing caught on fast because it gave everyone a common language for draft day haggling. By the mid-90s, most front offices were pulling up the Johnson scale when they built trades.
Criticism started piling up once statistical analysis became standard in football. Modern regression work keeps showing that top picks don’t generate as much surplus value as Johnson’s chart suggests. The chart doesn’t have a statistical backbone because Johnson built the numbers on feel, not data. Analytics driven charts now make it clear that the salary drop from early to mid round picks is sharper than the performance drop, so later first and second round picks often give you more bang for your buck. Even with all that, the Johnson chart’s still around. Plenty of teams reference it alongside newer models when they’re hammering out deals.
Point Values and Draft Slot Worth Explained
Point values put a number on how much production teams can expect from each slot. Harvard style charts calculate this using Career Approximate Value regression, mapping historical performance to pick number. In the Harvard model, pick one scores about 494.6 chart points, reflecting an expected career AV near 74. Pick 94 scores around 100.3 and carries an expected AV of about 15. The gap shows that top picks deliver way more career production on average, but the edge shrinks fast as you move through the draft.
Surplus value charts measure expected production minus rookie contract costs, then scale everything so the first overall pick equals 100 for easy comparison. The first pick in a recent draft year costs roughly 4.1 percent of the salary cap, or about 10.2 million per season based on a 250 million average cap. That same pick produces expected value equal to 6.5 percent of the cap, creating a surplus of 2.4 percent. Later first and early second round picks generate higher surplus because salaries drop faster than performance. A late first might cost 2 percent of the cap while delivering 4 percent in production, doubling the surplus margin compared to the top selection.
| Pick Number | Example Chart Value | Expected Production |
|---|---|---|
| 1 | 494.6 (Harvard) / 100 (Surplus) | CAV ~74 / 6.5% of cap |
| 10 | ~350 (Harvard) / ~70 (Surplus) | CAV ~50 / 5.2% of cap |
| 32 | ~210 (Harvard) / ~55 (Surplus) | CAV ~30 / 4.1% of cap |
| 94 | 100.3 (Harvard) / ~30 (Surplus) | CAV ~15 / 2.5% of cap |
The diminishing returns pattern means first round picks bring the best absolute talent, but second rounders often give you the best return on investment. Teams chasing championships still tend to prefer top ten picks because elite players create outsized impact. But teams building depth or watching their budget usually find better efficiency by trading down and stacking mid round selections.
Modern Variations: Harvard, Surplus Value, and Rich Hill Approaches
The Harvard chart uses Career Approximate Value regression to build a statistically solid pick valuation system. The model regresses historical CAV against overall pick number, producing an expected value curve with an R squared near 0.91599. That fit means about 91.6 percent of CAV variance gets explained by pick position, making the curve a strong predictor of average outcomes. The chart turns those expected CAV values into standardized points so teams can compare picks across rounds on a consistent performance scale.
Surplus value charts work differently. They focus on the economic gap between rookie contracts and second contract earnings. The methodology leaves out quarterbacks because their wildly different value would mess up results for other positions. The chart calculates the difference between what a player earns on their rookie deal and what they get paid on their second contract, treating that gap as a measure of on-field production. Then it smooths values to create curves showing the relationship between pick number and expected surplus. Rich Hill’s approach modernizes the original Johnson system while keeping a similar structure, updating point totals to reflect current player performance without ditching the familiar scale many teams still rely on.
Key differences across leading charts:
- Johnson uses informal judgment and leans hard on top selections
- Harvard relies on CAV regression with strong statistical fit (R² = 0.91599)
- Surplus value measures rookie contract cost versus second contract earnings
- Rich Hill updates Johnson’s framework with modern performance data
- All models produce different conclusions about identical trades, sometimes favoring opposite sides
Real NFL Trade Examples Using the Value Charts
The 2022 Lions Vikings trade shows how surplus value analysis breaks down real deals. Detroit sent pick 12 to Minnesota for picks 32, 34, and 66. Under the surplus value chart, the Lions got picks totaling 171 points while the Vikings received a package worth 219. Minnesota gained surplus value equal to pick 73, meaning they paid a premium to move up and grab a specific player they wanted. The deal favored Minnesota in chart terms, but Detroit collected multiple assets that could address several roster needs. That’s why teams sometimes accept point deficits for positional flexibility.
The 2021 Jets Vikings trade came out way more lopsided. New York sent picks 14 and 143 to Minnesota for picks 23, 66, and 86. The Jets got 119 total points while the Vikings received 184, a 65 point gap. That overpayment exceeded the value of the first overall pick in the surplus model, making it one of the most unbalanced trades of the draft class. The Jets probably prioritized consolidating picks to lock in a player they believed would beat his expected value, but the chart says they paid steep for that conviction.
The 2013 Cowboys trade shows how different charts flip fairness conclusions. Dallas traded pick 18 for picks 31 and 74. The Johnson chart valued 18 at 900 points and the return package at 820, suggesting the Cowboys lost value. The Harvard chart scored pick 18 at 249.2 and the package at 321.4, indicating the Cowboys gained value. The opposing conclusions happen because the Harvard model assigns relatively more value to mid round picks, while the Johnson chart heavily discounts anything outside the top twenty.
| Trade | Chart Evaluations | Winner by Model |
|---|---|---|
| 2022 Lions–Vikings (12 for 32+34+66) | Lions 171 pts / Vikings 219 pts (Surplus) | Vikings by 48 pts |
| 2021 Jets–Vikings (14+143 for 23+66+86) | Jets 119 pts / Vikings 184 pts (Surplus) | Vikings by 65 pts |
| 2013 Cowboys (18 for 31+74) | Johnson: 900 vs 820 / Harvard: 249 vs 321 | Johnson: gave value / Harvard: gained value |
For deeper analysis of surplus value trade examples, see the full NFL Draft Value Chart breakdown.
Limitations and Criticisms of Draft Trade Value Charts
The Johnson chart overvalues early picks, giving disproportionate points to top ten selections that historical production data doesn’t back up. The Harvard chart fixes that flaw with regression analysis but has its own quirks, including an unexplained bump at pick 215 in earlier versions and inconsistencies in how the linear decline gets presented across different publications. All statistical models carry built in variance because draft picks are probabilistic, not locks. The Harvard chart includes 95 percent confidence intervals for every pick that extend down to zero, meaning any selection can be a complete bust no matter what its expected value says.
Surplus value models kick out quarterbacks because their extreme outlier value would wreck the entire framework. A franchise quarterback on a rookie contract delivers exponentially more surplus than any other position. You can’t create a unified chart that fairly compares QB picks to non QB picks. Position specific returns vary wildly even within non QB groups. Running backs and safeties show the lowest positional returns across draft rounds, while pass rushers (both edge and interior) and offensive tackles hold value better and deliver higher surplus production. Charts that ignore positional differences risk treating a second round running back as equivalent to a second round edge rusher when the data clearly shows the edge rusher consistently outperforms.
Major weaknesses across all chart types:
- Johnson lacks statistical foundation and overweights top picks
- Harvard shows internal variance and version to version inconsistencies
- All models include wide confidence intervals, so any pick can fail
- Position specific value differences often get ignored, especially quarterback premium and running back devaluation
How Teams Use Trade Value Charts in Front Office Strategy
Teams run internal chart variations that blend traditional models with their own adjustments. Most front offices today lean closer to Harvard style regression than the original Johnson scale, but almost nobody uses a single published chart without tweaking it. General managers layer their own scouting grades, positional value assessments, and roster context over the baseline point totals. A team desperate for an edge rusher might accept a point deficit to move up and lock in their top rated pass rusher. A team with multiple holes might demand point surplus when trading down to collect extra picks.
Charts help structure negotiations by giving both sides a neutral starting point, but they don’t make final decisions. During live draft day calls, the chart lets everyone quickly check whether a proposed swap’s in the right ballpark. If the point totals are close, teams shift to talking about positional fit, prospect evaluations, and future pick considerations. If the gap’s big, the team on the short end either demands more compensation or walks. Smart negotiators know the other team’s internal chart might value picks differently, creating chances to find deals where both sides think they won.
What teams add beyond chart totals:
- Immediate roster needs and positional scarcity in the current draft class
- Probability of prospect success based on internal scouting grades
- Salary cap impact, including fifth year option value for first rounders and contract extension timelines
Final Words
Teams still start with point totals — Johnson, Harvard and surplus-value — to judge trades. We ran through what those point assignments mean, how picks turn into value, and why different charts reach different calls.
Charts speed up negotiations but aren’t the final word — positional need, salary and roster context change the math. Use them as a framework, not a rule.
NFL draft trade value chart explained: it’s a handy, clear tool to compare packages and spot overpays. Use it smartly, and teams come away with better deals.
FAQ
Q: What is the NFL draft trade value chart and why do teams use it?
A: The NFL draft trade value chart is a points table assigning values to picks, and teams use it to quickly compare offers, guide negotiations, and judge whether a trade is roughly fair.
Q: How are pick point values calculated?
A: Pick point values are calculated either by fixed assignments (Johnson) or statistical models using historical production, approximate value, or rookie contract economics to estimate expected player output per slot.
Q: What’s the difference between the Jimmy Johnson, Harvard, and surplus-value charts?
A: The Jimmy Johnson chart uses fixed, top-heavy points; Harvard uses regression on historical production; surplus-value compares rookie contract cost to expected on-field value and earnings potential.
Q: How do teams use the chart during trade negotiations?
A: Teams use the chart by totaling points on each side, then adding context—roster need, positional scarcity, and cap effects—to decide whether to accept, tweak, or reject the deal.
Q: Can the same trade look fair on one chart and unfair on another?
A: The same trade can look fair on one chart and unfair on another, as seen with the Cowboys’ 2013 example and Jets–Vikings deals; different models weigh picks and positions very differently.
Q: What are the main limitations of draft trade value charts?
A: Draft trade value charts overvalue top picks, ignore position differences and wide confidence ranges, often exclude quarterbacks in surplus models, and can give inconsistent results for later picks.
Q: How should teams adjust chart values for positional or roster needs?
A: Teams should adjust chart values by factoring in positional scarcity, immediate roster fit, injury history, and long-term cap impact to reflect real team priorities beyond raw points.
Q: Do charts show how much a pick is worth in salary cap or production terms?
A: Charts estimate expected production; surplus-value links picks to rookie contract cost and potential surplus, but no chart gives a direct, fixed cap-dollar value for a pick.
Q: Which chart do most teams use today?
A: Most teams use bespoke internal charts, often leaning toward Harvard-style analytics, but they treat all charts as negotiation tools rather than absolute rules when making draft decisions.