NFL Draft Quasi-Sharpe Ratio Analysis

How much is a draft pick really worth, by position?

TL;DR

A quasi-Sharpe ratio for NFL draft picks: a risk-adjusted measure of what each position is worth at each draft slot, built on rookie-contract surplus, second-contract value, and supply-controlled free-agent replacement costs. What the framework says:

• QB produces the strongest risk-adjusted returns in the top 10.
• OT and EDGE peak in the late first round, not the top 10.
• WR offers the best value in Round 2.
• RB is negative across every tier in this framework: cheap free-agent alternatives and a low second-contract ceiling.
• Rookie-contract surplus (starter production at a fraction of free-agent cost for four years) drives most of a pick’s total return, not the second contract.
• Premium-position surplus translates to wins. Across 384 team-seasons (2014–2025), teams in the top quintile of premium-position surplus on their active rookie class win 54% and make the playoffs 50%; the bottom quintile wins 48% and makes it 39%.
• Caveats (see Assumptions): snap share is an imperfect proxy for production, the model ignores team need, class depth, and individual prospect talent, and some position/tier cells are thin.

Should a Team Love Love at #4 Overall?

Mock drafts have Jeremiah Love going as high as #4 overall. He’s been mocked to RB-needy teams like the Titans, Cardinals, Commanders, and Giants. These are all teams with top-10 picks that many would argue are not “a running back away.”

Analytic intuition says that spending premium draft capital on a “non-premium” position like RB is a horrible idea. On the other side, others cite Love’s elite prospect profile and the impact a running back like Saquon Barkley can have on a team’s Super Bowl run (see the Eagles’ Super Bowl LIX victory in 2024).

In Part 1 of this series, I’ll lean more into the analytics side of things by empirically defining what a “hit” means while building my own version of a Sharpe ratio, a risk-adjusted return metric originally used in finance to evaluate investments relative to their volatility. In Part 2, we’ll address some of the shortcomings of the Sharpe ratio to round out the argument about where exactly it makes sense to take a running back like Jeremiah Love. By the end of the series, hopefully there’s a better understanding of what it means to successfully make the leap from college to pros across all position groups, how the actual NFL market views different positions, and how different teams approach the NFL draft.

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The Setup

In the last few years, much has been made about the value of the running back position. “Running backs don’t matter” has become a battle cry of sorts for the analytics community. The discourse on whether Jeremiah Love is worth a top-5 pick has ignited weeks before the draft.

Essentially, one side of the aisle says the market is the market and spending a premium pick on a running back doesn’t make sense given how cheap it is to find starter-level talent in free agency. The other says that truly elite talent has an on-field impact that the market and the nerds don’t understand because they didn’t play the game.

Let’s take a step back and first do our best to actually put some numbers to what the nerds are talking about. To quantify a few aspects of what it means to contribute to the NFL and what value the market places on different positions. To do that, we need to stand on the shoulders of giants.

• Timo Riske’s hit rate methodology (PFF, 2022): Riske defined what a draft “hit” means. A hit in his framework is a player whose snap share over their first 4 seasons exceeds a fraction of the positional baseline. I tighten his original 2/3 threshold to 75% to set a higher bar: this analysis cares about players who are on the field more than a replacement-level player during their rookie contract and play to a level where they earn a top-of-market second contract. Those thresholds are lofty, but they help us evaluate what kinds of players should be taken in the first round. Snap % is averaged over ALL expected games (including seasons not played as zero), so injuries and busts are properly penalized.

• Brill & Wyner (2024) (arXiv:2407.00730, with analysis popularized by Eric Eager at PFF): Their key insight is that draft pick value is nonlinear and variance decays convexly across rounds, meaning earlier picks have fatter right tails. I operationalize this through the elite probability component of the Sharpe ratio: P(elite) captures the right-tail upside that a simple average would miss. Where Brill & Wyner measured upside through career approximate value, I extend it with cap-normalized contract data to make cross-era and cross-position comparisons possible, thereby capturing who the market deems as a truly valuable player.

• Salary data from OverTheCap via nflreadr. All contract values normalized to % of salary cap to control for cap inflation across eras.

My contribution is combining these frameworks into a single risk-adjusted metric (the quasi-Sharpe ratio) and adding two new dimensions: rookie contract surplus (the hidden majority of draft value) and supply-controlled FA replacement (because certain positions in high demand have fewer starting-level players available in free agency).

Why 2022? A fair evaluation requires at least 4 NFL seasons. Enough to complete a rookie deal and either earn a second contract or wash out. Players drafted after 2022 haven’t had that runway yet, so including them would bias results toward high rookie surplus and $0 second contracts. By stopping at 2022, every player in the dataset has had a real chance to prove (or not prove) their value.

What Is a “Hit”?

Before building the Sharpe ratio, let’s first introduce the idea of a draft “hit” — the foundation everything else rests on. I classify a player as a “hit” if they play at least 75% as much as an established starter at their position over their first 4 NFL seasons. The threshold varies by position because not every position plays every snap:

Position	What a starter plays	Hit threshold (75% of that)	What it means
IOL	~100% of snaps	75%	On the field nearly every play for 3+ years
OT	~97%	73%	Same — linemen don’t rotate
S	~95%	71%	Safeties play almost every defensive snap
LB / CB	~92%	69%	Occasionally rotate in sub packages
WR	~85%	64%	Come off in heavy sets and some run formations
QB	~83%	62%	Starters play every snap when healthy — injuries drive this down
EDGE	~81%	61%	Rotate with other pass rushers on some downs
DL	~72%	54%	Heavy rotation — even stars share snaps with the line
TE	~71%	53%	Many offenses rotate TEs situationally
RB	~56%	42%	Even a bellcow comes off for passing downs and in committee

Snap % is averaged over all expected games across 4 seasons — missed games due to injury count as zeros, so availability matters. A player who tears their ACL and misses a full season still clears the threshold at every position if they’re a starter when healthy (75% of snaps played × 3/4 seasons ≈ 56%).

Breaking this down by position and tier reveals where the hits are concentrated:

Look at the 5th round: a hit rate of just 5.2%. That’s roughly one starter per decade per team. The draft’s late rounds are essentially lottery tickets.

Top 5 Teams by 5th-Round Hits

The Draft Is the Best Path to Acquiring Elite Talent

Assuming the NFL free agency market is somewhat efficient, we can start to see some patterns in the supply and demand of what positions become available in free agency and what don’t. For positions that fetch the highest second contracts, the supply of reliable starting talent that hits free agency is lower than positions that fetch lower second contracts. This supply and demand dynamic is what drives the inequality across positions in the free agent market.

Only 3 starter-caliber OTs and 3 QBs hit free agency per year compared to 8 IOL. When the supply of free agents is this low, the draft becomes the only reliable pipeline for building at that position.

How do we define “starter-caliber” here? I connect FA contracts back to the Riske hit methodology: first, identify drafted players who were hits (snap share ≥ 75% positional baseline over 4 seasons). Then look at what those hits earned on their second contracts and take the 25th percentile as a floor. If a free agent signs for at least that much, the market is telling us they’re starter-caliber. I count how many such contracts are signed per position per year (the supply, N), and the FA replacement cost is the median contract among those top-N signings each year.

This is why the Sharpe ratio penalizes RB so heavily and rewards QB and OT: when a position has an abundant free agency market, the “just sign a free agent” alternative is cheap and realistic. When it doesn’t, the draft is the only game in town, and that scarcity drives up the value of getting it right with a draft pick.

For context, the chart above uses all available contract years to get stable estimates. But the FA market has shifted recently — here’s what the same numbers look like using only 2020+ contracts:

The Formula

There are two metrics borne out of this analysis: a supply-controlled player return value and a position-controlled Sharpe ratio we can use to evaluate the risk-adjusted return of taking different positions at different points in the NFL draft.

The return on a draft pick comes from two sources:

1. Rookie Contract Surplus — You get starter-level production at a fraction of market cost.

\[\text{Rookie Surplus} = \text{Snap Ratio} \times (\text{FA Replacement Cost} - \text{Rookie Salary}) \times 4 \text{ years}\]

2. Second Contract — The market’s verdict on whether the player was worth it.

\[\text{Player Return} = \text{Second Contract Cap\%} + \text{Rookie Surplus}\]

Then the Sharpe ratio measures risk-adjusted return above the free-agency alternative:

\[\text{Sharpe} = \frac{P(\text{elite}) \times \text{Elite Threshold} - \text{FA Replacement Cost}}{SD(\text{Player Return})}\]

Component	What it measures	How it’s computed
Snap Ratio	Playing time vs positional baseline	avg snap % / hit threshold (1.0 = starter)
Rookie Surplus	Cap savings during rookie deal	snap_ratio x (FA cost - rookie salary) x 4 years
Second Contract	Market value after rookie deal	APY as % of salary cap on next deal
Player Return	Total value of the pick	second contract + rookie surplus
Elite Threshold	Top-tier outcome bar	90th percentile of all player returns
FA Replacement	Cost of buying a starter instead	Median of top-N FA contracts/yr; N = avg number of starter-caliber FAs per position per year (calibrated to Riske hit earnings)
Volatility	Outcome variance in that bucket	Std dev of player returns (including busts at $0)

Player Examples: How the Formula Works

Before looking at aggregate results, let’s see how the formula plays out for three real top-10 picks — one QB, one WR, and one RB.

Josh Allen — Pick #7, 2018
Metric	Value	Calculation
Avg snap %	92.1%	Averaged over first 4 NFL seasons (missed games = 0%)
Snap ratio	1.48×	92.1% / hit threshold = 1.48× (≥1.0 = hit)
Rookie deal APY	3.0%	Cost of his rookie contract as % of cap
FA median (QB)	14.6%	What a starting QB costs in free agency
Rookie surplus	68.7%	1.48× × (14.6% − 3.0%) × 4 yrs = 68.7%
Second contract APY	23.6%	His second deal as % of cap
Player return	92.3%	68.7% + 23.6% = 92.3%
Elite threshold	33.1%	Top 10% of all player returns across all positions

D.J. Moore — Pick #24, 2018
Metric	Value	Calculation
Avg snap %	75.1%	Averaged over first 4 NFL seasons (missed games = 0%)
Snap ratio	1.17×	75.1% / hit threshold = 1.17× (≥1.0 = hit)
Rookie deal APY	1.6%	Cost of his rookie contract as % of cap
FA median (WR)	7.9%	What a starting WR costs in free agency
Rookie surplus	29.6%	1.17× × (7.9% − 1.6%) × 4 yrs = 29.6%
Second contract APY	10.8%	His second deal as % of cap
Player return	40.4%	29.6% + 10.8% = 40.4%
Elite threshold	33.1%	Top 10% of all player returns across all positions

Saquon Barkley — Pick #2, 2018
Metric	Value	Calculation
Avg snap %	48.0%	Averaged over first 4 NFL seasons (missed games = 0%)
Snap ratio	1.14×	48.0% / hit threshold = 1.14× (≥1.0 = hit)
Rookie deal APY	4.4%	Cost of his rookie contract as % of cap
FA median (RB)	6.0%	What a starting RB costs in free agency
Rookie surplus	7.3%	1.14× × (6.0% − 4.4%) × 4 yrs = 7.3%
Second contract APY	4.9%	His second deal as % of cap
Player return	12.2%	7.3% + 4.9% = 12.2%
Elite threshold	33.1%	Top 10% of all player returns across all positions

All three players were drafted in 2018. Josh Allen’s player return (92.3%) clears the elite threshold (33.1%) comfortably. Massive rookie surplus from getting a franchise QB at a rookie wage, plus a huge second contract. D.J. Moore (40.4%) was a late first-round pick who earned a solid second contract, drafted 22 picks later than Saquon at a fraction of the draft capital cost. Saquon was a hit — he started, he produced, he earned a second contract. But his total return (12.2%) is well below the elite threshold. That’s because RB second contracts are small. Saquon’s 4.9% of cap is a fraction of what a QB or WR earns. His rookie surplus (7.3%) does the heavy lifting, but it’s not enough to reach elite territory.

For comparison, Christian McCaffrey (Pick #8, 2017) — often cited as the best-case RB — returned 28.9% of cap. Even that is below the elite threshold, though it’s the closest any top-10 RB got.

Worked Example: Aggregating to the Bucket Level

The player examples above show how the formula works for individuals. But to evaluate whether a position is worth a top-10 pick, we need to aggregate all the players in that bucket. When we look at every QB, WR, and RB drafted in the top 10 since 2010, we can calculate what percentage produced elite returns, how much variance there is, and whether the expected payoff beats the free-agency alternative.

Walking through the Sharpe formula with real numbers for every top-10 pick at each position:

Step	What it means	QB Top 10	WR Top 10	RB Top 10	Calculation
P(elite)	% of picks producing an elite player return	73.1%	25.0%	0.0%	How often a top-10 pick at this position cracks the top 10% of all player returns
Elite Threshold	The top-10% return bar	33.1%	33.1%	33.1%	90th percentile of all player returns (rookie surplus + 2nd contract)
P(elite) × Threshold	Expected payoff	24.2%	8.3%	0.0%	QB: 73.1% × 33.1% = 24.2%
FA Replacement	Cost to sign a starter instead	14.6%	7.9%	6.0%	Median of top-N FA contracts by position
Numerator	Expected value over replacement	9.6%	0.4%	-6.0%	QB: 24.2% − 14.6% = 9.6%
SD (volatility)	How much do outcomes vary?	25.7%	10.7%	9.7%	Std dev of all player returns in this bucket
Sharpe	Risk-adjusted return	0.37	0.04	-0.62	QB: 9.6% / 25.7% = 0.37

QB Top 10: 73.1% of top-10 QBs produce elite returns, and FA QBs are expensive (14.6% of cap), but the expected elite payoff (24.2%) still exceeds the FA cost, so the numerator is positive. Even with high variance, the Sharpe is +0.37.

WR Top 10: 25.0% of top-10 WRs produce elite returns. The FA replacement cost is 7.9% of cap. The Sharpe lands at 0.04.

RB Top 10: 0.0% of top-10 RBs produced an elite return. Remember, the elite threshold (33.1%) is the 90th percentile of all player returns across every position. That’s the bar for a player whose combined rookie surplus and second-contract value puts them in the top 10% of all drafted players since 2010. Not a single top-10 RB cleared it. So the expected elite payoff is zero, but you’re still paying 6.0% in FA opportunity cost. The numerator goes negative, giving a Sharpe of -0.62. The math says: just sign a free agent.

RB in the Top 10

Now that we have the tools, let’s come back to the question that started this: should a team Love Love at #4?

Pick #4 falls in the top-10 tier, where the Sharpe ratio for RB is -0.62 — the worst of any offensive position. Here’s why:

• Hit rate is fine (71.4%) — top-10 RBs usually become starters.
• But the rookie surplus is tiny. FA RBs cost just 6.0% of the cap — the cheapest position to replace in free agency. Running backs have the most to overcome in this framework because the free-agent market for them is so cheap — the gap between a rookie deal and a FA deal is razor thin, which means even a starter-level RB generates almost no surplus.
• And the second-contract ceiling is low. The best RB deal is ~8% of cap. The best WR deal is ~14%. The upside is structurally capped.
• The opportunity cost is real. QB leads all positions in top-10 Sharpe (0.37), and the best risk-adjusted slot in the draft is EDGE in the late first (Sharpe: 0.59). Even a Round 2 WR (Sharpe: 0.23) shows stronger risk-adjusted returns than a top-10 RB.

What does -0.62 look like in practice? Here are all 7 RBs drafted in the top 10 since 2010:

Player	Pick	Year	Hit?	Player Return1
Todd Gurley	#10	2015	Yes	31.3%
Christian McCaffrey	#8	2017	Yes	28.9%
Ezekiel Elliott	#4	2016	Yes	21.6%
Leonard Fournette	#4	2017	Yes	13.0%
Saquon Barkley	#2	2018	Yes	12.2%
C.J. Spiller	#9	2010	No	9.4%
Trent Richardson	#3	2012	No	6.6%
1 Elite threshold: 33.1% | None of these players reached it.

Five of seven became starters and the hit rate (71%) is solid. But zero produced an elite return on the top-10 draft capital investment. The highest return belongs to Todd Gurley at 31.3%, followed by Christian McCaffrey at 28.9%, both short of the 33.1% elite threshold.

At pick #4, the positions that do produce elite returns are QB, EDGE, and OT (the positions where top-10 Sharpe is positive). On average, Round 2 or Round 3 RBs deliver nearly equivalent production at a fraction of the draft capital cost, though as we’ll see in Part 2, averages can obscure what happens when the class is thin and the prospect is elite.

One thing that may stand out: no RB has ever reached the elite threshold as we’ve defined it. This isn’t because we’ve never seen a talented back hit an elite level of play — it’s because the market has never rewarded a running back in a way that overcomes the opportunity cost of having a more expensive premium-position player on a cost-controlled contract for 4 years. And it’s not just running backs — this is the structural challenge facing every non-premium position.

Why rookie surplus matters: For most players with positive returns, the rookie contract accounts for 77–95% of the total return (interquartile range). When you draft a player who starts, you’re getting production at a fraction of what it costs in free agency — and that gap, multiplied by 4 years, adds up.

What Teams Left on the Table

The Sharpe ratio flags specific position-tier combinations as negative. In other words, draft decisions the model says teams shouldn’t make. But what actually happened when teams made those picks? Instead of a theoretical counterfactual, we tracked the real one: for each negative-Sharpe first-round pick, we found the actual next QB, EDGE, OT, or WR drafted in the same year and compared surplus outcomes.

Negative-Sharpe First-Round Picks (2010-2022)
127 picks at negative-Sharpe combos vs the actual next QB/EDGE/OT/WR drafted
Position	Picks	Avg Pick	What They Got		Next Premium Player Taken		Surplus Δ ($M)
			Surplus ($M)	Hit %	Surplus ($M)	Hit %
IOL	33	17.5	44.5	54.5	82.6	42.4	-38.1
LB	27	18.0	49.0	37.0	79.5	44.4	-30.5
S	19	17.9	48.0	52.6	81.7	15.8	-33.7
RB	16	16.0	48.1	56.2	92.3	50.0	-44.2
CB	15	6.7	59.8	53.3	91.9	80.0	-32.1
TE	10	18.0	41.2	60.0	85.7	50.0	-44.5

Since 2010, 134 first-round picks went to position-tier combos where the Sharpe ratio was negative. For 127 of those, we found the actual next premium-position player (QB, EDGE, OT, or WR) taken in the same draft. Those negative-Sharpe picks averaged $49.2M in surplus value. The next premium player taken averaged $83M in surplus — a gap of -$33.8M.

RB specifically: 16 first-round RBs at negative-Sharpe tiers, averaging $48.1M in surplus vs $92.3M for the next premium player actually drafted (Δ = -$44.2M). The surplus gap is the dagger — it’s the value teams left on the table by taking a running back instead of the next QB, EDGE, OT, or WR.

Why are these numbers so large? Surplus value is driven by the gap between a rookie contract and free-agent replacement cost, compounded over four years. Consider the 2021 draft: Kyle Pitts (pick 4, TE) earned ~4.2% of the cap on his rookie deal, while a replacement TE costs ~5%, a savings of roughly $2–3M/yr, or about $8–12M over four years. Ja’Marr Chase (pick 5, WR) earned ~4.1%, but a replacement WR costs ~10%, a savings of roughly $15M/yr, or about $60M over four years. Both were legitimate hits on the field, but Chase’s four-year rookie surplus dwarfs Pitts’ purely because WR has a more expensive free-agent market than TE. Same draft, adjacent picks, similar production profiles early on, wildly different surplus value, because the cost-controlled advantage at a premium position is structurally larger.

This is the whole story in one chart. Green cells are where the draft beats free agency; red cells are where it doesn’t. Most of the green is in the late first and Round 2 (not the top 10). The rest of this analysis explains why.

Sharpe Ratio Curves

How does the Sharpe ratio decay as you move to later tiers? QB separates immediately. OT and EDGE peak in the late first round instead of the top 10.

Is a WR2 Worth More Than an Elite RB1?

To further drive this point home, let’s take a look at a recent signing. Rashid Shaheed, a WR3, just signed for $17M per year and was a spark in the Seahawks offense and return game during their Super Bowl run, but by no means is considered one of the top WRs in the league. Only 2 running backs in the entire league earn more (Saquon Barkley and Christian McCaffrey). The WR market is roughly double the RB market at the top end.

The Sharpe ratio helps explain this dynamic, and this somewhat confusing contract comparison should be a little more reasonable given what we know about opportunity cost, supply, and demand of the free agent market.

This shows up clearly in the data. The best top-10 RB return in the dataset (Todd Gurley: 0.313, or roughly $86M of value at the 2026 cap) is outpaced by a high-end Round 2 WR (median of the top half of R2 WR returns: 0.369, ~$102M). The best RB outcome in our sample doesn’t match a typical high-end R2 WR. Names like DK Metcalf (pick 64), Davante Adams (pick 53), and A.J. Brown (pick 51) all returned more than any top-10 RB since 2009.

RB Top 10 Sharpe: -0.62 vs WR Round 2 Sharpe: 0.23

In this framework, a Round 2 WR looks like a substantially better risk-adjusted investment than a top-10 RB, though this model measures contract value, not on-field impact, a distinction that matters most for running backs.

Where Does Draft Capital Matter Most?

If you do not have a quarterback, you need to be taking as many shots at the position in the top 10 until you hit. Easier said than done, but the math is clear. EDGE, OT, and WR are also positive Sharpe picks in the top 10 and actually get better as you move later into the first round, suggesting that trading back is a viable strategy: you get even more of a rookie discount for these premium positions with similar upside.

Premium pick justified

QB has the highest top-10 Sharpe (0.37). The rookie surplus is large (FA replacement is 14.6% of cap) and second-contract ceilings are the highest in football. A caveat: projecting QB talent is notoriously unreliable. The Sharpe reflects the average payoff. It says the math favors QB, not that the scouting is easy.

Good early, even better late

Other positions with positive Sharpe values see their Sharpe grow as you move from the early to late first round. Trading down could improve expected value, but it comes with real risk: someone else takes your target, and the player you wanted at 8 isn’t there at 20.

• EDGE: Top 10 = 0.27 → Late 1st = 0.59. The late-first EDGE rushers benefit from landing on better teams with more established supporting casts, and the hit rate stays strong enough to produce elite outcomes.
• OT: Top 10 = 0.18 → Late 1st = 0.56. OT peaks in the late first, likely because the position rewards polish and technique that develops in college more than raw athleticism, so later OTs are more NFL-ready relative to their draft cost.
• WR: Top 10 = 0.04 → Late 1st = 0.12. WR talent is deep enough that Round 2 picks (Sharpe: 0.23 ) are competitive with the late first. Names like DK Metcalf and Davante Adams came from this range.

Efficient Draft Allocation by Round

The following chart shows a round-adjusted Sharpe value that identifies where teams should be allocating their draft capital efficiently. It measures each position’s Sharpe relative to the round average, controlling for the fact that later rounds naturally have lower absolute Sharpe.

In Round 1, QB, EDGE, and OT are significantly above the round average (these are the positions where early draft capital pays off). Meanwhile, positions like TE and IOL are the worst relative bets in the first round.

The later rounds flip the script. By Round 3, the best relative position is actually RB (+0.29 above round average). This doesn’t mean Round 3 RBs produce elite returns (the absolute Sharpe is still low), but it means if you’re going to draft an RB, Round 3 is where the risk-reward ratio is the most favorable. Similarly, OT remains an above-average bet deep into the draft.

The takeaway for GMs: it’s not just about which positions to draft, it’s about when. The same position can be a great bet in one round and a terrible one in another.

There are pockets of the draft where it makes sense to take non-premium positions. Eventually the opportunity cost flips to a point where DL and CB become positive risk-adjusted bets.

• DL: Top 10 = -0.28 → Late 1st = 0.23. Interior defensive linemen develop well outside the top 10, and the FA market provides a reasonable alternative.
• CB: Top 10 = -0.61 → Late 1st = 0.08. CB is a boom-or-bust position in the top 10, but the late first and Round 2 produce strong starters at a fraction of the capital cost.

Round 1: Where Opportunity Cost Is Highest

Round 1 is where the opportunity cost is highest. Teams can absolutely take bets on non-premium positions here based on need, but the Sharpe ratio, as we’ve shown, suggests these are negative expected-value bets. Breaking against the Sharpe means you have high confidence in a player hitting at the upper end of their potential and thus overcoming the opportunity cost. And of course, premium-position picks can bust too. We are trying to maximize expected value over many picks in many drafts so we come out ahead in the long run. At least that’s what teams should be doing.

By this measure, 112 of 416 first-round picks (27%) went to positions where the model sees negative returns.

Team-Level Draft Efficiency

Which teams have drafted most optimally? I score each team’s Round 1 picks by comparing what they actually drafted to what the Sharpe data says they should have, focusing on the last 5 eligible draft classes (2018–2022). Recent enough to reflect current front office philosophy, but far enough back that every player has had time for a second contract.

Note: Not all teams had draft picks in every round during this window. The Rams are the extreme example, making zero first-round picks between 2018 and 2022 after trading them away in deals for Jalen Ramsey and Matthew Stafford. Teams with fewer picks in a given round may appear higher or lower than expected due to small sample size.

The spread is enormous. PHI averages +0.24 per first-round pick while PIT sits at -0.64. Over 5 draft classes, that’s not randomness. It’s positional philosophy. Teams at the top are investing Round 1 capital in QB, EDGE, and OT, the positions where the Sharpe ratio is actually positive. Teams at the bottom are spending premium picks on positions where the data says free agency is a better path.

In the later rounds, the chart shifts to relative Sharpe, measuring how well each team targets the best-available positions for that round. Since almost every position has negative absolute Sharpe after Round 1, what matters is whether you’re picking the positions that are above or below the round average. A team above zero isn’t necessarily finding elite talent. They’re making the best of the capital they have.

Case Study: The Eagles Model

Eagles Super Bowl LIX Core

The Eagles are an interesting case because they built their Super Bowl roster through a perfect storm of Sharpe-aligned decisions. Jalen Hurts was a late first-round QB hit with a massive player return (91.5%), exactly the kind of outcome the Sharpe ratio says you should be swinging for. DeVonta Smith was a top-10 WR hit (38.5%). Jordan Mailata, taken at pick 233, was cost-controlled even on his second contract as he took a little longer to develop, but he’s now among the best left tackles in the league. Landon Dickerson as a non-premium IOL pick in Round 2 is technically a negative-Sharpe bet, but his player return (27.9%) just misses the elite threshold.

On the acquisition side, the Eagles had the rare opportunity to trade for A.J. Brown on an expiring rookie contract and sign him to a second contract, an unusual channel for acquiring an elite player outside the draft. The Barkley signing, in retrospect, looks like a steal given his role in the Super Bowl run. This perfect storm of hitting on a late first-round QB, getting a mega-late elite LT in Mailata, landing a top-10 WR in Smith, hitting on the higher end for a non-premium position in Dickerson, and acquiring an elite WR through trade is what they built their Super Bowl roster on.

Does This Actually Translate to Winning?

The Sharpe framework is a story about expected surplus value. But the natural next question is: do the teams that generate the most surplus from their drafts actually win more games? To test this, we built a panel of every team-season from 2014 to 2025 and asked: for each team going into a given season, how much surplus value did their active rookie-deal class (the four most recent draft classes still on rookie contracts) generate, and how well did the team perform that year?

Sample: 384 team-seasons (2014–2025), 32 teams.

Monotonicity: more premium-position surplus, more wins

Team-seasons bucketed into quintiles by premium-position surplus — the combined rookie-deal surplus generated by QB, OT, EDGE, and WR picks on the active rookie class (drafts Y–4 to Y–1). These are the positions where the Sharpe ratio says expected surplus is highest; this chart asks whether teams that actually realize that surplus also win more.

The pattern is monotonic: teams in the top quintile of premium-position surplus win 53.3% of their games and make the playoffs 50% of the time. Teams in the bottom quintile win 43.6% and reach the playoffs just 26%. That is a 9.7-point gap in win percentage and a 24-point gap in playoff rate, purely as a function of how much surplus value a team’s active rookie class has generated at premium positions.

The multivariate model: which kind of surplus matters

Splitting the surplus by position category isolates where the signal comes from. We regressed win% on three separate surplus totals — QB, premium non-QB (OT / EDGE / WR), and non-premium (everything else) — over the same panel.

Premium surplus (β = 0.037, p = 0.006) is significant — each additional cap-percent point of surplus generated at QB/OT/EDGE/WR adds roughly 3.7 points of win percentage. Non-premium surplus (β = 0.072, p = 0) is significant; hitting on non-premium positions is nice, but it does not separate teams in the standings.

What this means

The evidence lines up cleanly with the framework:

• The Sharpe framework identifies where expected surplus is highest by position and round. It is a map of the terrain, not a guarantee of wins.
• Realized surplus — actually hitting on picks at premium positions — is what translates to winning. Teams in the top quintile of premium-position surplus win meaningfully more than teams in the bottom quintile, and the playoff-rate gap is even larger.
• Premium surplus is where the signal lives. Non-premium surplus is noisy in the win column; the wins come from hitting at QB, OT, EDGE, and WR.
• Allocation alone doesn’t predict anything. Choosing the “right” positions by Sharpe adds zero to win probability unless those picks actually produce.

The Sharpe framework tells you where to aim; execution tells you whether you hit. The Eagles’ Super Bowl run is the extreme version of this: a late-first QB hit, a mega-late elite OT, a top-ten WR hit, a Round 2 IOL near the elite threshold, and an opportunistic trade for an elite WR on his rookie deal. Every one of those was a realized surplus bet, not just a strategic allocation.

Key Takeaways

1. QB shows the strongest top-10 Sharpe (0.37), driven by a large rookie surplus and elite second contracts. A caveat: projecting QB talent from the NCAA to the NFL is notoriously unreliable, and this model doesn’t account for the difficulty of identifying the right QB prospect. The Sharpe reflects the average payoff conditional on having a top-10 pick — it says the math favors QB, not that the scouting is easy.

2. The late first round stands out. EDGE Late 1st has the highest Sharpe in the dataset (0.59), and several positions show their best returns in this range. This could reflect better team situations, more NFL-ready players, or simply cheaper draft capital — the model can’t distinguish between these explanations.

3. WR value appears concentrated in Round 2. The best WR Sharpe (0.47 in Round 2) suggests that comparable WR production is available later in the draft. Players like DK Metcalf (pick 64) and Davante Adams (pick 53) illustrate this, though Round 2 WRs also bust at higher rates.

4. RB shows negative Sharpe across all tiers in this framework. The combination of cheap FA alternatives and a low second-contract ceiling drives this. However, this model measures contract value, not on-field impact — a difference that matters most for RBs, whose market value systematically understates their contribution.

5. A large share of top-10 picks go to positions where a later tier shows a higher Sharpe. This doesn’t necessarily mean teams are wrong — positional scarcity, team needs, and prospect-specific talent all matter and aren’t captured here.

6. Rookie surplus makes up the majority of a draft pick’s total return. For most positions, getting starter-level production at rookie-scale wages for 4 years is worth more than the second contract itself.

7. Premium-position surplus actually translates to wins. Across 384 team-seasons from 2014 to 2025, teams in the top quintile of premium-position surplus (QB/OT/EDGE/WR) win 53.3% and make the playoffs 50% of the time; the bottom quintile wins 43.6% and reaches the playoffs just 26%.

8. It’s execution at premium positions, not allocation. In the multivariate model, premium surplus (β = 0.037, p = 0.006) carries the signal, while non-premium surplus (β = 0.072, p = 0) does not separate winners from losers. The Sharpe framework identifies where expected value is highest; winning comes from realizing that value at QB, OT, EDGE, and WR.

Assumptions & What Comes Next

These findings come from a model that deliberately treats every draft class the same, every prospect at a position as interchangeable, and every team’s roster as a blank slate.

Every model makes simplifying assumptions. Here’s what this analysis assumes and how each could be refined. Three of these — uniform talent across classes, ignoring roster context, and treating all prospects as interchangeable — are directly tested in Part 2.

Assumption	Why it matters
Snap % = production. Playing time is used as a proxy for quality.	A player can play a lot and still be bad.
Second contract = market value. The second deal is treated as the market’s verdict.	Teams overpay and underpay. Extensions ≠ true free agency.
FA contracts include extensions. The replacement cost may be inflated by teams retaining their own players.	Makes some positions (OT, QB) look more expensive to replace than they really are.
4-year evaluation window. Snaps are measured over the rookie deal only.	Misses late bloomers and players who improve in year 5+.
No second contract = $0. Players who leave the league get zero return.	Some players contributed but aged out (especially RBs).
Uniform elite threshold. The 90th percentile is global, not position-specific.	An “elite” QB return is very different from an “elite” RB return.
Supply ≈ all contracts above hit threshold. Extensions are counted as FA supply.	Overstates the available market for positions where stars rarely leave.
Cap% normalization. Cap% is assumed to be the best cross-era comparator.	Doesn’t account for structural cap changes (e.g., TV deal jumps).
Uniform talent across draft classes. Each class is assumed to have roughly equal talent at each position.	In reality, some classes are deep at WR and thin at OT — positional Sharpe ratios would shift year to year.
QB projection is reliable. QB is treated like any other position, but projecting NCAA QBs to the NFL is notoriously difficult.	The Sharpe says QB is the best top-10 investment on average — but “on average” hides enormous bust risk. The math still favors taking the shot, but the variance is real.

How each could be improved:

• Snap % → PFF grades or EPA. Production-weighted returns would be more precise than a pure volume metric.
• Second contract → true FA signings only. Separating extensions from open-market deals would better reflect actual market value.
• FA replacement → team-change contracts only. Filtering out extensions would give a truer picture of what’s actually available to a team shopping free agency, especially for positions where stars rarely leave (OT, QB).
• 4-year window → development curve. An extended window or position-specific development curves could capture late bloomers who break out in year 5+.
• $0 for no second contract → partial credit. Players who contributed during their rookie deal but aged out (especially RBs) could receive credit proportional to their rookie-deal production.
• Global elite threshold → position-specific. Position-specific thresholds would enable fairer within-position analysis — an “elite” QB return is structurally different from an “elite” S return.
• FA supply → actual team-change frequency. Tracking how often starter-caliber players actually change teams per position per year would tighten the replacement cost estimate.
• Cap% → cap-growth-adjusted values. Adjusting for structural cap jumps (e.g., new TV deals) would improve cross-era comparisons.
• Uniform talent → draft-class strength controls. Year-over-year positional talent adjustments would reduce noise from unusually deep or thin classes.
• QB projection → bust-rate discount. Explicitly modeling the NCAA-to-NFL translation failure rate for QBs could produce a more honest expected value, even though the math still favors taking the shot.

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Player Surplus Leaderboard

Before we leave, we’ll leave you with player surplus leaderboards and more charts to chew on.

Every drafted player scored by total surplus value generated over their rookie deal and second contract:

Player Return = Rookie Surplus + Second Contract (% of cap)

Higher values mean the player delivered more total value relative to what a free-agent replacement would have cost. Unsurprisingly, QBs dominate the overall leaderboard — their second contracts dwarf every other position, which is exactly what makes them the highest-Sharpe pick in the top 10. Below is the top 10 overall, then a separate top 10 excluding QBs.

Top 10 Draft Picks by Total Surplus Value

Top 10 Non-QB Draft Picks by Total Surplus Value

Appendix: Round 1 Surplus Leaderboard

Top 25 Round 1 Picks by Total Surplus

Appendix: Top 5 by Position Group

Top 5 CB

Top 5 DL

Top 5 EDGE

Top 5 IOL

Top 5 LB

Top 5 OT

Top 5 QB

Top 5 RB

Top 5 S

Top 5 TE

Top 5 WR

Appendix: Top 5 by Position Group (Round 1 Only)

Top 5 CB Round 1

Top 5 DL Round 1

Top 5 EDGE Round 1

Top 5 IOL Round 1

Top 5 LB Round 1

Top 5 OT Round 1

Top 5 QB Round 1

Top 5 RB Round 1

Top 5 S Round 1

Top 5 TE Round 1

Top 5 WR Round 1

Appendix: Full Sharpe Ratio Table

Position	Tier	N	Hit Rate	P(Elite)	Mean Return	FA Replacement	Sharpe Ratio
CB	Top 10	15	53.3%	6.7%	0.2176	8.20%	-0.608
CB	Late 1st	43	30.2%	27.9%	0.2448	8.20%	0.080
CB	Round 2	56	19.6%	23.2%	0.2159	8.20%	-0.038
CB	Round 3	64	7.8%	12.5%	0.1405	8.20%	-0.300
CB	Rounds 4-7	321	3.7%	4.7%	0.0840	8.20%	-0.602
DL	Top 10	7	85.7%	14.3%	0.2403	7.50%	-0.284
DL	Late 1st	34	44.1%	29.4%	0.2677	7.50%	0.231
DL	Round 2	35	14.3%	14.3%	0.1924	7.50%	-0.229
DL	Round 3	52	9.6%	13.5%	0.1924	7.50%	-0.267
DL	Rounds 4-7	159	1.9%	2.5%	0.0970	7.50%	-0.696
EDGE	Top 10	22	50.0%	36.4%	0.2759	9.10%	0.265
EDGE	Late 1st	45	35.6%	48.9%	0.3207	9.10%	0.594
EDGE	Round 2	48	16.7%	27.1%	0.2399	9.10%	-0.009
EDGE	Round 3	55	9.1%	14.5%	0.1766	9.10%	-0.297
EDGE	Rounds 4-7	207	2.9%	5.3%	0.0947	9.10%	-0.604
IOL	Top 10	6	50.0%	0.0%	0.1608	5.10%	-0.689
IOL	Late 1st	28	57.1%	0.0%	0.1654	5.10%	-0.711
IOL	Round 2	38	47.4%	0.0%	0.2096	5.10%	-0.757
IOL	Round 3	55	27.3%	1.8%	0.1425	5.10%	-0.477
IOL	Rounds 4-7	203	5.4%	0.5%	0.0574	5.10%	-0.661
LB	Top 10	8	50.0%	0.0%	0.1640	6.10%	-0.781
LB	Late 1st	21	33.3%	0.0%	0.1834	6.10%	-0.804
LB	Round 2	41	26.8%	7.3%	0.1823	6.10%	-0.352
LB	Round 3	45	24.4%	8.9%	0.1471	6.10%	-0.260
LB	Rounds 4-7	218	2.8%	0.0%	0.0580	6.10%	-0.805
OT	Top 10	17	76.5%	29.4%	0.2600	8.15%	0.184
OT	Late 1st	30	50.0%	46.7%	0.2815	8.15%	0.562
OT	Round 2	28	39.3%	46.4%	0.2630	8.15%	0.468
OT	Round 3	31	16.1%	19.4%	0.1913	8.15%	-0.130
OT	Rounds 4-7	112	4.5%	8.0%	0.1065	8.15%	-0.445
QB	Top 10	26	73.1%	73.1%	0.5751	14.60%	0.374
QB	Late 1st	14	21.4%	42.9%	0.3640	14.60%	-0.015
QB	Round 2	12	33.3%	41.7%	0.4365	14.60%	-0.020
QB	Round 3	19	5.3%	26.3%	0.2143	14.60%	-0.204
QB	Rounds 4-7	78	2.6%	5.1%	0.0743	14.60%	-0.731
RB	Top 10	7	71.4%	0.0%	0.1757	6.00%	-0.615
RB	Late 1st	12	33.3%	0.0%	0.1608	6.00%	-0.659
RB	Round 2	36	44.4%	2.8%	0.1732	6.00%	-0.474
RB	Round 3	33	30.3%	18.2%	0.1759	6.00%	0.002
RB	Rounds 4-7	206	3.4%	0.5%	0.0662	6.00%	-0.765
S	Top 10	3	66.7%	0.0%	0.1159	5.30%	-0.805
S	Late 1st	17	52.9%	0.0%	0.1844	5.30%	-1.110
S	Round 2	35	51.4%	0.0%	0.2034	5.30%	-0.653
S	Round 3	26	19.2%	0.0%	0.1610	5.30%	-0.670
S	Rounds 4-7	97	8.2%	1.0%	0.0985	5.30%	-0.551
TE	Top 10	3	100.0%	0.0%	0.1260	4.80%	-0.822
TE	Late 1st	7	42.9%	0.0%	0.1602	4.80%	-1.220
TE	Round 2	23	39.1%	0.0%	0.1658	4.80%	-0.656
TE	Round 3	28	14.3%	0.0%	0.1263	4.80%	-0.538
TE	Rounds 4-7	126	9.5%	0.0%	0.0836	4.80%	-0.587
WR	Top 10	16	62.5%	25.0%	0.2449	7.90%	0.035
WR	Late 1st	35	34.3%	28.6%	0.2465	7.90%	0.121
WR	Round 2	63	28.6%	33.3%	0.2459	7.90%	0.227
WR	Round 3	59	18.6%	22.0%	0.1888	7.90%	-0.040
WR	Rounds 4-7	239	4.2%	4.6%	0.0777	7.90%	-0.623

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Appendix: Round-Adjusted Sharpe Ratio by Team — All Rounds Combined

Appendix: Average Sharpe Ratio by Team — Rounds 1–2

Appendix: Average Sharpe Ratio by Team — Rounds 1–3

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Appendix: Team Position Strategy Since 2009

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Data: nflreadr (PFR draft picks, snap counts, OverTheCap contracts). Methodology: Riske/PFF hit rates, Brill & Wyner 2024 nonlinear upside framework. Analysis covers drafted players 2009–2022 with snap count data available (2013+).