Is the No. 1 pick actually worth ten times a late first-rounder?
Teams treat picks like cash on draft day, and a simple chart often settles trades in seconds.
The old Jimmy Johnson table gives every slot a point total so GMs can add, subtract, and decide fast.
But analytics and surplus-value models argue the math isn’t that simple.
This post breaks down how teams actually price draft capital, compares common charts, and shows what really moves trades.
Not just the numbers on a whiteboard.
How Teams Quantify Draft Capital Using the NFL Draft Value Chart Framework
Teams treat draft picks like cash on draft day. They need a fast way to price them when someone calls with a trade. The NFL draft value chart exists to solve that exact problem. It assigns every selection a fixed point total, so a GM can run the numbers on a whiteboard and decide if a swap makes sense before the clock hits zero.
Most teams still reference the Jimmy Johnson version from the early 1990s. Pick No. 1 sits at 3,000 points, No. 10 at 1,300, and the last pick of the first round at 275. The logic is straightforward: add up the points on both sides, and whoever gets more “wins.” If you’re swapping pick 8 (1,500 points) for picks 15 (1,050) and 23 (700), you’re getting 1,750 back. That’s a 250-point gain, roughly the value of a late second-rounder. Teams lean on this math every year to anchor negotiations, even when they don’t follow it exactly.
Analytics departments have built more sophisticated versions using actual data. One well-known alternative uses Career Approximate Value from drafts between 1980 and 2005, running a regression that explains about 91.6% of the variation in pick outcomes by slot. That model assigns pick No. 1 a value around 494.6 and sets pick 94 at 100.3 as the “standard” pick. Those numbers look different from the Johnson chart because they reflect real on-field production instead of educated guessing. But the principle’s the same: you’re turning every pick into a number you can compare, so you can evaluate trades in real time.
Front offices today blend both systems. Before pulling the trigger, teams usually look at five core inputs:
- Chart points (Johnson values or something custom).
- Expected Approximate Value or wins added over a career window.
- Positional scarcity (how rare is a premium tackle or edge rusher at this spot).
- Cap and contract implications (rookie wage scale changes what the pick actually costs).
- Probability distribution of outcomes (boom-bust risk, variance by slot).
When those line up, a deal gets done. When they don’t, teams hold firm or ask for extra picks to close the gap.
The NFL Draft Value Chart: Structure, Origins, and Core Point Assignments
The chart most people call “the trade value chart” came out of the Dallas Cowboys front office in the early 1990s. Jimmy Johnson wanted a consistent system for negotiating pick swaps. The Cowboys had extra picks and needed a way to show other GMs the math was fair. Johnson’s staff assigned declining point values to every selection, with the steepest drops in the top ten. That became the baseline the league still uses.
The reason it stuck is simple: it gave teams a shared language. When a GM offers you three picks for one, both sides can add up the totals in seconds and know if the deal’s even close. The Johnson chart became NFL-standard currency because it was the first widely shared table. Once everyone adopted it, deviating too far meant fewer trade partners on the phone. Even teams using analytics internally will reference Johnson values when talking to other clubs, because it’s the common reference point.
| Pick | Johnson Value |
|---|---|
| 1 | 3,000 |
| 2 | 2,600 |
| 3 | 2,200 |
| 4 | 2,000 |
| 5 | 1,800 |
| 6 | 1,700 |
| 7 | 1,600 |
| 8 | 1,500 |
| 9 | 1,400 |
| 10 | 1,300 |
| 11 | 1,250 |
| 12 | 1,200 |
| 13 | 1,150 |
| 14 | 1,100 |
| 15 | 1,050 |
| 16 | 1,000 |
| 17 | 950 |
| 18 | 900 |
| 19 | 875 |
| 20 | 850 |
| 21 | 800 |
| 22 | 750 |
| 23 | 700 |
| 24 | 650 |
| 25 | 600 |
| 26 | 550 |
| 27 | 500 |
| 28 | 450 |
| 29 | 400 |
| 30 | 350 |
| 31 | 300 |
| 32 | 275 |
Draft Pick Valuation Through Analytics and Expected Career Value Models
Analytics teams started questioning the Johnson chart when they noticed it was wildly overvaluing early picks relative to what players actually produced. Researchers pulled historical draft data from 1980 through 2005 and measured every pick’s Career Approximate Value. That’s a stat aggregating seasons played and performance into a single career score. They computed the average CAV for each slot, compared it to the league-wide average (around 15.03), and used that gap to build an expected-value curve.
The result was a regression model with an R-squared around 0.916. Pick number alone explains roughly 91.6% of the variation in career outcomes. Under that model, the first overall pick has historically averaged about 66.7 career AV. That’s more than four times the “standard” pick. When researchers scaled those values to match the structure of the Johnson chart, pick No. 1 came out at around 494.6 instead of 3,000. The 94th pick (early third round) was set to 100.3 as the baseline. That flatter curve reflects reality: top picks are better, but not ten times better. The dropoff isn’t as steep as Johnson assumed.
Teams using analytics-based tables get a more realistic estimate of what a pick’s likely to contribute. Instead of arbitrary point totals, they’re pricing picks on expected starts, expected wins added, or career AV distributions pulled from decades of outcomes. That kind of model answers questions like “Is trading three mid-round picks for one top-ten pick actually worth it?” You can compare the combined expected value of those three picks against the single high pick’s expected value and see which package gives you more aggregate production.
Statistical Behavior Across the Draft
Variance in outcomes shrinks as you move down the board in absolute terms. Early picks have both higher expected value and wider spreads between hits and busts. But in relative terms, late picks are riskier. The coefficient of variation (standard deviation divided by mean) goes up, meaning a seventh-round pick’s far more likely to be worth zero than a first-rounder, even though the dollar difference in expected production is smaller.
Every pick carries bust risk. When analysts compute 95% confidence intervals for individual slots, those intervals include zero production for almost every spot in the draft. There’s always a meaningful chance the player contributes nothing. That’s why teams pay attention to probability distributions, not just point estimates. A pick with an expected AV of 15 might deliver anywhere from 0 to 50. Understanding that range matters when you’re deciding whether to gamble on upside or accumulate safer bets in later rounds.
Comparing Draft Value Models: Johnson, Rich Hill, AV-Based, and Surplus-Value Charts
The Johnson chart’s still the most common reference, but it’s far from the only one teams consult. Rich Hill built an alternative model in the 2000s that adjusted values based on historical Approximate Value. It produced a flatter curve. Top picks were still worth more, but the gap wasn’t as extreme. Hill’s version suggested the drop from pick 1 to pick 10 was much smaller than Johnson implied. Mid-round picks were undervalued if you judged them strictly by expected production.
Surplus-value charts take a different approach altogether. Instead of just measuring what a player might produce, they subtract the cost of his rookie contract from his expected on-field value. One widely cited surplus model converts both rookie deals and second-contract APY into percentages of the salary cap. For example, the No. 1 pick’s rookie deal might total around $41 million over four years, which works out to about $10.2 million per year. If you assume a $250 million average cap over that window (using a 7% growth rate), that’s roughly 4.1% of the cap. If that player’s second contract comes in at about 6.5% of the cap per year, his surplus is around 2.4 percentage points. That’s the difference between what he cost and what he earned.
Those surplus charts update regularly and now cover all 256 picks. They’re especially useful post-rookie-wage-scale, because the new CBA reduced top-pick salaries and made early first-rounders cheaper relative to their production. Teams using surplus models often find mid-first-round picks (around 15 to 20) generate the highest net value when you account for contract cost. You’re still getting starter-level talent but paying less than you would for a top-five pick.
Key differences among models:
Johnson values are fixed and don’t reflect actual outcomes. Purely negotiation benchmarks. AV-based charts (like the CAV regression model) price picks by historical performance and produce flatter, more realistic curves. Rich Hill and similar analyst models adjust for positional variation and historical bust rates, often reducing the premium on the very top picks. Surplus-value charts incorporate rookie contract cost and monetize the value, showing which picks deliver the best bang-for-buck under the current salary cap rules.
Applying Draft Value Charts: Real Trade Examples and Calculation Walkthroughs
The 2004 Eli Manning–Philip Rivers trade is one of the most analyzed swaps in draft history. The valuation story changes depending on which chart you use. Under the Johnson system, the Giants sent the No. 1 pick (3,000 points) to San Diego and received the No. 4 pick (2,000) plus two third-rounders and a future first-rounder. Totaling somewhere around 3,000 or slightly more, it looked roughly even. But when you run the same trade through the CAV-based chart, the Giants gave up about 855.4 in value and received around 494.6 back. That makes it look like a significant overpay for the right to pick Manning. The difference illustrates how much the old chart inflates the top end.
More recent examples show how teams actually use the numbers during draft weekend. In 2021, the Lions traded pick No. 12 to Minnesota for picks 32, 34, and 66. Under a surplus-value chart, Detroit received 100 points for No. 12 and 71 for No. 46 (total 171), while Minnesota gave up 81 + 84 + 54 (total 219). The Vikings gained 48 surplus points, roughly equivalent to the value of pick 73. That’s an early third-rounder. When you look only at expected on-field production (ignoring contract cost), the totals are 140 for Detroit versus 169 for Minnesota. A difference of 29 points or about the value of pick 92. Either way, Minnesota came out ahead by the models, though Detroit may have preferred the higher individual ceiling of pick 12.
The Jets’ trade up for Alijah Vera-Tucker in 2021 shows what a lopsided deal looks like on the chart. New York sent picks 14 and 143 (total surplus value: 119) and received pick 23, 66, and 86 (total: 184). The 65-point surplus deficit was larger than the entire surplus value of the No. 1 pick in that year’s chart. The Jets paid a massive premium to move up eleven spots. Likely because they had a specific player targeted and were willing to overpay rather than miss out.
- Pick 8 for picks 15 and 23: Johnson values are 1,500 versus 1,050 + 700 = 1,750, a gain of 250 points (roughly a late second-rounder).
- Lions–Vikings swap: Surplus totals 171 vs 219, a 48-point edge for Minnesota (≈ pick 73). On-field-only totals: 140 vs 169, a 29-point edge (≈ pick 92).
- Jets trade up: 119 vs 184 surplus, a 65-point deficit equivalent to more than the value of pick No. 1. An extreme overpay by the numbers.
Converting Player-for-Pick Trades
When a team acquires a veteran and gives up draft capital, analysts convert the picks into annual cap percentages and compare them to the player’s extension. In one prominent wide-receiver trade, the acquiring team sent 2021 picks 23 and 86, plus a 2022 first-rounder (pick 10), and received a 2022 second-rounder (pick 43). Discounting future picks at 10%, the calculation ran: 3.34% + 1.50% + (3.53% × 0.9) − (1.11% × 0.9) = roughly 7.0% of the cap per year over four years. The player signed an extension at $17.5 million APY, about 9.6% of the cap at the time. Dividing 7.0 by 9.6 suggests you need to inflate the listed extension by about 73% to capture the true cost when you include the draft picks surrendered. The “real” four-year cost was closer to $30.3 million per year in equivalent cap value. That kind of conversion helps teams decide whether acquiring a known veteran is cheaper or riskier than drafting and developing talent.
Draft Capital Strategy: Roster Construction, Salary Cap Effects, and Risk Management
The rookie wage scale introduced in the 2011 CBA fundamentally changed how teams think about early picks. Before the new rules, the No. 1 overall pick could command a contract worth more than elite veterans at his position. That made top selections risky financially. Now, first-round rookies are locked into four-year deals at controlled prices, and teams hold a fifth-year option on top-fourteen picks. That shift increased the economic value of early first-rounders relative to mid-to-late rounds, because you’re getting potential franchise talent at below-market cost.
Roster construction also forces teams to think beyond raw chart totals. You can only start eleven players on each side of the ball, so accumulating ten late-round picks doesn’t give you the same on-field utility as one elite starter. Late picks also tend to cluster near undrafted free-agent replacement value. Many seventh-rounders contribute about as much as a UDFA, so the marginal gain from an extra Day 3 pick is small. Teams that trade down aggressively need to make sure they’re not just stockpiling lottery tickets. They need to convert some of those picks into actual roster contributors, or the quantity advantage disappears.
Marginal value becomes critical when you’re deciding whether to move up or down. If you already have three starting-caliber safeties, drafting another one in the second round adds less value than the pick’s nominal chart score, because the fourth safety’s a backup. On the other hand, if you don’t have a franchise left tackle and one’s available, the positional scarcity can justify paying above chart value to move up. Teams price picks differently based on their own roster gaps, which is why “fair” trades on the chart don’t always happen. One side’s positional need makes them willing to overpay.
Future and Conditional Draft Picks: Present Value, Discount Rates, and Uncertainty Modeling
Teams routinely discount future picks by about 10% per year when evaluating trades. A 2025 second-rounder might be worth 90% of what a 2024 second-rounder’s worth, because you’re waiting an extra year to use it and you lose the chance to develop that player sooner. The discount rate can vary by team. Rebuilding clubs with multi-year timelines may discount future picks less, while win-now teams discount them more heavily because they need immediate help.
Conditional picks add another layer of complexity. If a team trades a fourth-rounder that escalates to a third if the acquired player makes the Pro Bowl, the receiving team has to estimate the probability of that condition hitting and weight the pick value accordingly. Front offices use historical data on similar conditions (snap counts, accolades, playing time benchmarks) to assign probabilities, then multiply the pick’s chart value by that probability to get an expected value. A third-rounder worth 150 points with a 30% chance of vesting is effectively worth 45 points in the trade calculation.
Modern analytics teams also run Monte Carlo simulations to model the range of outcomes when multiple conditional picks are involved. By simulating thousands of scenarios (different injury rates, performance outcomes, roster moves), they can estimate the distribution of total value and decide whether the trade’s worth the uncertainty. That kind of probabilistic modeling is standard practice now in front offices with dedicated analytics departments.
Four factors teams consider when pricing future and conditional picks:
Time preference and opportunity cost (how much roster flexibility you lose by waiting). Probability weighting for performance-based conditions (Pro Bowl, playing time, team wins). Cap-cycle alignment (whether you need cap relief now or can absorb cost later). Draft-class strength forecasts (some teams will pay more for a future pick if next year’s class is projected to be deeper).
Team-Specific Philosophies, Market Inefficiencies, and Custom Draft Capital Models
Not every team uses the same internal chart. Some franchises build proprietary models that weight positions differently, valuing quarterbacks, edge rushers, and left tackles more heavily because those spots command higher salaries in free agency and are harder to fill. Others adjust the curve based on their own drafting track record, discounting early picks if their scouting department’s historically struggled in the first round, or boosting late-round values if they’ve consistently found contributors on Day 3.
Market inefficiencies show up when teams with different internal models negotiate. If one club still uses the Johnson chart and another uses a surplus-value model, they’ll disagree on what’s “fair,” and that gap creates room to extract value. Teams known for analytics-heavy approaches can sometimes get extra picks from clubs that anchor to the old chart, simply because the less sophisticated side doesn’t realize they’re overpaying. The flip side is that teams targeting a specific player often pay a “choice value” premium. They’re not buying generic expected AV, they’re buying the ability to select the exact guy they want. That optionality’s worth more than the statistical average suggests.
Late-round picks are frequently treated as near-replacements for undrafted free agents. If your internal model shows a sixth-round pick produces only marginally more than a UDFA, you’re more willing to trade those picks away for future assets or to move up earlier in the draft. That’s one reason compensatory picks, which can’t be traded in some cases, are valued differently. They’re “free” lottery tickets with UDFA-level upside and zero trade cost.
Final Words
At the draft table, the points add up fast. We showed why teams lean on the Jimmy Johnson chart, how CAV and expected-value models reshape those figures, and how simple math (like trading pick 8 for 15+23) makes trade fairness obvious.
Those totals feed roster plans, cap math, and risk choices, and teams tweak charts for position needs or future-pick discounting.
This piece helps explain how teams value draft capital: understanding the nfl draft value chart, so you can read trades like a front office — and spot the smart moves.
FAQ
Q: How is the NFL Draft Value Chart used?
A: The NFL Draft Value Chart is used to assign point values to picks so teams can quickly compare and price trades, sum totals to judge fairness, and guide draft‑day negotiation decisions.
Q: Who was Mr irrelevant for the Chiefs?
A: Mr. Irrelevant for the Chiefs refers to the player Kansas City selected with the draft’s final pick; that title changes each year and isn’t permanently tied to one franchise.
Q: What NFL teams have the most draft capital?
A: NFL teams with the most draft capital are usually rebuilding clubs or those that traded down, holding multiple early‑round and future picks; exact leaders shift season to season based on trades.
Q: How much money did Shedeur Sanders lose?
A: The amount Shedeur Sanders reportedly lost isn’t definitively confirmed publicly; reported figures vary and depend on the specific deal or incident being referenced.