49ers 2026 Draft Class: Statistical Analysis & Historical Grade Comparison

Introduction

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The San Francisco 49ers, under head coach Kyle Shanahan and general manager John Lynch, completed the 2026 NFL Draft with 8 selections and no first-round pick. Their first pick, wide receiver De’Zhaun Stribling at 33rd overall. Opened a class that addressed multiple positional needs across both lines and secondary, supplemented by eight undrafted free agents.

However, how effective have they actually been in the draft?

Using custom statistical metrics, probability theory and the Jimmy Johnson Trade Value Chart. I analyzed every 49ers draft pick from 2017–2025 to quantify their drafting efficiency and identify statistically significant patterns.

Building on the statistical framework from the 2017–2025 analysis, this article evaluates the 2026 class using the same SDS, OAV and JTE metrics. Then provides year-by-year class grades for the entire Shanahan/Lynch era (2017–2026).

The 2026 Draft Class

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Drafted Players

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Undrafted Free Agents Signed

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Methodology

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The dataset for this article comprises:

• Historical sample (2017–2025): $n = 58$ draft picks
• 2026 class: $n = 8$ draft picks (all TBD/rookie status)
• Combined Shanahan/Lynch era: $n = 66$ draft picks

Metric 1: Selection Differential Score (SDS)

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The SDS quantifies the deviation between draft position and pre-draft consensus:

$$SDS_i = R_i - P_i$$

Where:

• $R_i$ = Pre-draft rank for player $i$
• $P_i$ = Actual draft pick position for player $i$

Interpretation:

• $SDS > 0$: Value pick (drafted later than ranked)
• $SDS < 0$: Reach pick (drafted earlier than ranked)
• $SDS = 0$: Fair value pick

SDS Analysis: 2026 Class

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Individual SDS values for the 2026 class:

2026 class summary statistics:

$$\bar{SDS}{2026} = \frac{1}{8}\sum{i=1}^{8} SDS_i = \frac{73}{8} = +9.13$$

$$s_{SDS,2026} = \sqrt{\frac{1}{7}\sum_{i=1}^{8}(SDS_i - 9.13)^2} = 9.30$$

This is a substantial positive shift from the historical mean of $\bar{SDS}_{2017-2025} = -3.0$, indicating the 2026 class was skewed toward value picks rather than reaches.

95% Confidence Interval for 2026 Mean SDS

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$$CI_{95%} = \bar{SDS} \pm t_{0.025,, 7} \cdot \frac{s}{\sqrt{n}} = 9.13 \pm 2.365 \cdot \frac{9.30}{\sqrt{8}}$$

$$CI_{95%} = 9.13 \pm 7.77 = [1.36,; 16.90]$$

Since this interval does not contain zero, we can conclude the 2026 class represents a statistically significant departure toward value picking ($\alpha = 0.05$). This marks the first 49ers draft class in the Shanahan/Lynch era where the mean SDS confidence interval excludes zero on the positive side.

Hypothesis Test: Is 2026 SDS Different from Historical Mean?

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$$H_0: \mu_{SDS,2026} = -3.0 \quad \text{(historical mean)}$$ $$H_1: \mu_{SDS,2026} \neq -3.0$$

$$t = \frac{\bar{SDS}_{2026} - \mu_0}{s / \sqrt{n}} = \frac{9.13 - (-3.0)}{9.30 / \sqrt{8}} = \frac{12.13}{3.29} = 3.69$$

P-value: $P(|T_7| > 3.69) = 0.008$

Conclusion: We reject $H_0$ at $\alpha = 0.05$. The 2026 class SDS is significantly higher than the historical Lynch/Shanahan mean ($p = 0.008$). The 49ers drafted with notably greater value in 2026 relative to their prior pattern.

Metric 2: Outcome-Adjusted Value (OAV)

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OAV measures realized value relative to positional expectations:

$$OAV_i = \frac{AV_i}{E[AV \mid round_i]} \cdot \left(1 + \frac{SDS_i}{100}\right)$$

Expected career AV by round (historical NFL averages):

OAV Interpretation:

• $OAV > 1.5$: Exceptional value
• $1.0 \leq OAV \leq 1.5$: Met or exceeded expectations
• $0.5 \leq OAV < 1.0$: Underperformed
• $OAV < 0.5$: Significant underperformance

Projected OAV: 2026 Class (Baseline Projections)

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Since all 2026 picks are rookies ($AV_i = 0$ currently), I use Projected OAV based on round-level expected value modulated by SDS:

$$\widehat{OAV}_i = 1.0 + \frac{SDS_i}{50}$$

This baseline projects performance relative to round expectation, where each 50-point positive SDS implies one standard unit above expectation.

Class mean projected OAV: $\bar{\widehat{OAV}}_{2026} = 1.19$

Six of eight picks project at or above expectations. The two reaches — Romello Height (EDGE) and Carver Willis (OT) — both represent positional investments at premium positions where scheme fit may compensate for consensus undervaluation.

Metric 3: Jimmy Johnson Trade Efficiency (JTE)

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JTE scores net trade value extracted per transaction:

$$JTE = \frac{\sum V_{received} - \sum V_{sent}}{n_{trades}}$$

2026 Draft Capital: Actual vs. Projected

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The 49ers entered the 2026 off-season projected to hold pick #27 (Round 1). The final draft card — with no Round 1 selection — reflects pre-draft maneuvering:

Projected (pre-draft):

The 49ers traded away Round 1 (680 JJ) and received additional picks, converting from 7 selections to 8 while accepting a net JJ loss of −98 points:

$$JTE_{2026} = \frac{1{,}180 - 1{,}278}{1} = -98.0 \text{ JJ pts}$$

This is a modest negative, substantially better than the era’s historical JTE of −122.97 per major trade event. The 49ers traded capital (single 1st) for volume (multiple mid-round picks), which aligns with the statistically optimal multi-pick strategy.

Pick volume benefit:

$$P(\text{At least one hit from 8 picks}) = 1 - (1 - 0.294)^8 = 0.931$$

Trading from 7 picks to 8 increases the probability of finding at least one Elite or Solid player from 91.8% to 93.1% — a marginal but directionally correct move.

Draft Class Value Grading: 2017–2026

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Composite Grade Score (CGS)

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I define a Composite Grade Score to enable year-over-year comparison:

$$CGS_y = 0.45 \cdot \frac{HR_y}{0.30} + 0.30 \cdot \frac{\bar{SDS}y + 20}{40} + 0.25 \cdot \frac{JJ_y}{JJ{max}}$$

Where:

• $HR_y$ = hit rate for year $y$ (Elite + Solid / total picks)
• $0.30$ = league average hit rate benchmark
• $\bar{SDS}_y$ = mean SDS for year $y$ (normalized to $[-20, +20]$ range)
• $JJ_y$ = total JJ draft capital used
• $JJ_{max} = 2{,}015$ (2019 class, Bosa era)

Weights reflect the primacy of actual outcomes (45%), draft efficiency (30%), and capital invested (25%).

Letter grade mapping:

Year-by-Year Draft Class Summary

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*2026 CGS uses projected OAV and SDS only; HR component defaults to league average (0.30) pending outcomes.

Class-by-Class Breakdown

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2017 — Grade: C

The inaugural Shanahan/Lynch class was defined by failure at the top and genius at the bottom. Three of the first four picks (Thomas, Foster, Beathard) became busts, yet George Kittle falling to pick 146 in Round 5 remains the greatest steal of the era.

$$CGS_{2017} = 0.45 \cdot \frac{0.125}{0.30} + 0.30 \cdot \frac{-8.4 + 20}{40} + 0.25 \cdot \frac{1{,}842}{2{,}015} = 0.188 + 0.087 + 0.228 = 0.27$$

2018 — Grade: B−

Fred Warner at pick 70 is the hidden gem of this class, projecting as an eventual All-Pro. McGlinchey provided solid starting value at tackle for several seasons. Dante Pettis (Round 2) and Kentavius Street (Round 4, injury) were the misses that dragged the grade down.

$$CGS_{2018} = 0.45 \cdot \frac{0.286}{0.30} + 0.30 \cdot \frac{5.2 + 20}{40} + 0.25 \cdot \frac{1{,}520}{2{,}015} = 0.429 + 0.189 + 0.188 = 0.46$$

2019 — Grade: B+

The Bosa class. Trading up to secure the second overall pick for a generational pass rusher, while also finding Deebo Samuel in Round 2, makes this the second-best capital deployment of the era on a pure outcome basis. Deebo Samuel has since departed the roster — his Solid classification reflects production earned during his tenure in San Francisco before his exit.

$$CGS_{2019} = 0.45 \cdot \frac{0.40}{0.30} + 0.30 \cdot \frac{-9.8 + 20}{40} + 0.25 \cdot \frac{2{,}015}{2{,}015} = 0.600 + 0.077 + 0.250 = 0.64$$

The negative mean SDS (−9.8) reflects the “reach” optics of picking Bosa at #2 (ranked ~4th on many boards), but outcomes validate the deviation.

2020 — Grade: B−

Two first-round picks and a combined JJ investment of 1,895 returned Aiyuk (solid) and Kinlaw (disappointing). The ceiling of this class was never reached. Jauan Jennings in Round 7 quietly became a legitimate contributor.

$$CGS_{2020} = 0.45 \cdot \frac{0.286}{0.30} + 0.30 \cdot \frac{-2.1 + 20}{40} + 0.25 \cdot \frac{1{,}895}{2{,}015} = 0.429 + 0.134 + 0.235 = 0.48$$

2021 — Grade: D

The Trey Lance trade was the most consequential and statistically catastrophic transaction of the Lynch/Shanahan era. Surrendering three first-round picks for a player who accumulated minimal AV before being traded represents an expected value destruction of historic proportions.

$$CGS_{2021} = 0.45 \cdot \frac{0.0}{0.30} + 0.30 \cdot \frac{-18.3 + 20}{40} + 0.25 \cdot \frac{1{,}105}{2{,}015} = 0.000 + 0.013 + 0.137 = 0.10$$

Trade cost quantification:

$$\Delta JJ_{Lance\ trade} = V_{received} - V_{sent}$$

The 49ers sent picks 12, 43, and a 2022 and 2023 first-round pick to acquire pick 3. Using Jimmy Johnson values:

$$V_{sent} \approx 1{,}400 + 580 + 950 + 780 = 3{,}710 \text{ JJ pts}$$ $$V_{received} = 2{,}200 \text{ JJ pts (pick 3)}$$ $$\Delta JJ = 2{,}200 - 3{,}710 = -1{,}510 \text{ JJ pts}$$

This single transaction destroyed more draft capital than any two prior drafts combined.

2022 — Grade: B-

The greatest capital-efficiency class of the era. With limited draft capital (no Round 1 due to the Lance trade), the 49ers drafted Drake Jackson, Danny Gray, and closed the board with Brock Purdy at pick 262 — the single highest-OAV selection in 49ers history and arguably NFL Draft history.

$$CGS_{2022} = 0.45 \cdot \frac{0.60}{0.30} + 0.30 \cdot \frac{12.4 + 20}{40} + 0.25 \cdot \frac{960}{2{,}015} = 0.900 + 0.243 + 0.119 = 0.81$$

Purdy’s OAV:

$$OAV_{Purdy} = \frac{35}{E[AV \mid R7]} \cdot \left(1 + \frac{SDS_{Purdy}}{100}\right) = \frac{35}{2.0} \cdot \left(1 + \frac{+82}{100}\right) = 17.5 \cdot 1.82 = 31.85$$

This is the highest OAV of any 49ers pick in the dataset — a 3-standard-deviation outlier equivalent to Kittle’s.

2023 — Grade: C+

A disappointing class with no confirmed hits. Jake Moody was cut before contributing meaningfully as a starter, Ji’Ayir Brown has not established himself as the answer at safety, and Cameron Latu never developed into a reliable contributor. The 49ers’ second pick (Round 3, #99) at kicker proved costly when Moody was released. This class reflects the depth-of-roster cost of the Lance trade era.

$$CGS_{2023} = 0.45 \cdot \frac{0.0}{0.30} + 0.30 \cdot \frac{3.8 + 20}{40} + 0.25 \cdot \frac{1{,}340}{2{,}015} = 0.000 + 0.179 + 0.166 = 0.35$$

2024 — Grade: B-

First full class with post-Lance capital restored. Ricky Pearsall (Round 1) is active on the roster. Isaac Guerendo has emerged as a legitimate backfield contributor. Dominick Puni has stepped into a starting guard role on the offensive line — the early standout of this class. Grade pending full development across 2025 and 2026 seasons.

2025 — Grade: Incomplete

The 2025 class is too early to evaluate. All players remain TBD. The 49ers held 11 picks, their largest class of the era, led by EDGE Mykel Williams at pick 11 — the first Round 1 pick since Ricky Pearsall in 2024.

2026 — Grade: B (Projected)

$$CGS_{2026}^{proj} = 0.45 \cdot \frac{0.30}{0.30} + 0.30 \cdot \frac{9.13 + 20}{40} + 0.25 \cdot \frac{1{,}180}{2{,}015} = 0.450 + 0.218 + 0.146 = 0.52$$

The 2026 grade is projected using the league-average hit rate as the baseline (since all players are TBD), a strong SDS component (+9.13 mean, highest in class history), and modest capital input (no Round 1).

Key observations:

• Highest mean SDS of any Shanahan/Lynch class: +9.13
• No first-round pick significantly caps the capital ceiling
• 6 of 8 picks project as value (positive SDS)
• EDGE (Height) and OT (Willis) are positional reaches that fit scheme needs
• 8 UDFAs is a substantial depth haul

Probability Analysis

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Historical Hit Rate Distribution (2017–2025)

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Let $X$ be the number of hits (Elite or Solid) in $n$ picks. Assuming independence, $X \sim Binomial(n, p)$.

Observed (2017–2025):

• Hits: $k = 17$ (6 Elite + 11 Solid)
• Total: $n = 58$
• $\hat{p}_{2017-25} = \frac{17}{58} = 0.293$

Wilson Score 95% CI:

$$CI_{95%} = \frac{0.293 + \frac{1.96^2}{116} \pm 1.96\sqrt{\frac{0.293 \cdot 0.707}{58} + \frac{1.96^2}{4 \cdot 58^2}}}{1 + \frac{1.96^2}{58}} = [0.191,; 0.418]$$

2026 Class Hit Probability

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Using the posterior from 2017–2025 data:

$$p \mid data \sim Beta(20, 48)$$ $$E[p \mid data] = \frac{20}{68} = 0.294$$

P(at least one hit from 8 picks):

$$P(X \geq 1) = 1 - (1 - 0.294)^8 = 1 - (0.706)^8 = 1 - 0.069 = 0.931$$

P(at least one Elite from 8 picks) (using Elite-only rate $\hat{p}_{elite} = 6/58 = 0.103$):

$$P(\text{Elite}) = 1 - (1 - 0.103)^8 = 1 - 0.897^8 = 1 - 0.421 = 0.579$$

There is a 93.1% probability the 49ers find at least one starter-quality player and a 57.9% probability of finding an Elite contributor from the 2026 class.

Conditional Hit Rate by Round (Full Era, 2017–2026 projected)

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The 2026 class contains 2 Round 3 picks — historically the weakest round for this organization (11.1% hit rate). This is a risk factor worth monitoring.

Chi-Square Test: Round vs. Hit Rate Independence (Updated 2017–2025)

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$$\chi^2 = \sum \frac{(O_{ij} - E_{ij})^2}{E_{ij}} = 6.84, \quad df = 6$$

$$\chi^2_{0.05,6} = 12.59 > 6.84$$

We fail to reject independence. Round alone does not significantly predict hit probability in this sample.

Regression Analysis

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Updated Linear Model: Career AV vs. Draft Position (2017–2025)

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$$\hat{AV} = 22.4 - 0.11 \cdot Pick$$

Residual Analysis — All-Time Outliers:

Kittle and Purdy are both beyond 3 and 2.8 standard deviations, respectively — true black swan events that define the 49ers’ scouting identity.

2026 Picks: Predicted AV by Regression

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Total predicted AV (2026 class):

$$\sum \hat{AV}_{2026} = 80.3$$

$$E[\text{combined AV}] = 80.3 \text{ (regression baseline)}$$

This is comparable to a single high-quality Round 1 pick combined with a solid Round 2. Given the trade-down from pick 27, the 49ers accepted a minor ceiling reduction in exchange for 8 at-bats instead of 7.

UDFA Analysis

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The 49ers’ eight undrafted free agents represent additional roster construction beyond the draft itself.

UDFA Hit Rate (Historical)

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For context, the 49ers have found meaningful contributors via UDFA in recent cycles. The baseline UDFA hit rate (making 53-man roster in year 1) for the NFL is approximately:

$$P(\text{UDFA makes roster}) \approx 0.18$$

P(at least one UDFA makes 53-man roster from 8 signings):

$$P(X \geq 1) = 1 - (1 - 0.18)^8 = 1 - 0.82^8 = 1 - 0.204 = 0.796$$

UDFA scouting value (JJ equivalent):

UDFAs carry no pick cost. Each successful UDFA represents pure positive SDS — effectively an infinite value pick. If even one of the eight UDFAs (Grimes, Pauling, Dinkins, Eason, Thompson, Kamara, Stroman, Bouwmeester) reaches average NFL starter status, the effective hit rate for the full 2026 class rises above the 30% league average.

Notable UDFA profiles:

• Wesley Grimes / Will Pauling (WR): Depth at a priority position. Shanahan’s system is historically UDFA-WR friendly (see Jauan Jennings, 2020 Round 7).
• Khalil Dinkins (TE): Developmental TE fits the 49ers’ two-TE base scheme.
• Jalen Stroman (SS): Safety depth with Hufanga as the model for late-round/UDFA hits at this position.
• Jack Bouwmeester (P): Specialists are deterministic; if healthy and accurate, punters make rosters at near-100% rates.

Positional Distribution Analysis

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2026 Class by Position

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Positional Value Assessment

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$$PV_{pos, round} = E[AV \mid pos, round] \cdot \frac{1}{Salary_{pos}}$$

2026 positional grade:

Three picks land at positions where their round is below the optimal window (Height, Willis, Cruz). All three are offensive or defensive linemen — a deliberate strategy to build depth at premium-cost positions through draft capital rather than free agency.

Monte Carlo Simulation: 2026 Class Outcomes

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Running 10,000 simulations for the 2026 draft class:

Simulation Parameters

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def simulate_2026_class(n_simulations=10000):
    picks = [
        {"pick": 33, "round": 2, "sds": 15},
        {"pick": 70, "round": 3, "sds": -5},
        {"pick": 90, "round": 3, "sds": 20},
        {"pick": 107, "round": 4, "sds": 11},
        {"pick": 127, "round": 4, "sds": -5},
        {"pick": 139, "round": 5, "sds": 13},
        {"pick": 154, "round": 5, "sds": 9},
        {"pick": 179, "round": 6, "sds": 15},
    ]
    total_avs = []
    for _ in range(n_simulations):
        total_av = sum(
            sample_av(p["round"], p["sds"]) for p in picks
        )
        total_avs.append(total_av)
    return total_avs

Results

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Value at Risk (VaR) Analysis

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$$VaR_{0.05} = \text{5th percentile of simulated total AV}$$

The 8-pick structure provides strong downside protection. Even worst-case scenarios yield meaningful roster contributions (VaR = 18 AV across the class).

Bayesian Analysis

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Updated Prior and Posterior (2017–2025)

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$$p \mid data \sim Beta(20, 48)$$ $$E[p \mid data] = 0.294, \quad 95% \text{ CI} = [0.193, 0.409]$$

Posterior Predictive: 2026 Expected Hits

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$$E[\text{hits}_{2026}] = 8 \cdot E[p \mid data] = 8 \cdot 0.294 = 2.35$$

$$P(\text{exactly 2 hits}) = \binom{8}{2}(0.294)^2(0.706)^6 = 28 \cdot 0.086 \cdot 0.124 = 0.300$$

$$P(\text{exactly 3 hits}) = \binom{8}{3}(0.294)^3(0.706)^5 = 56 \cdot 0.025 \cdot 0.176 = 0.247$$

The most probable outcome is 2–3 contributing players from this class, which would align with a B-grade performance.

Era-Level Summary Statistics

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Shanahan/Lynch Era (2017–2025): Key Metrics

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Correlation Matrix (2017–2025)

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The weak correlation between SDS and outcomes ($r = -0.21$) remains the central finding: being a “value pick” on paper does not strongly predict career AV. Scouting accuracy matters more than consensus deviation.

ANOVA: Career AV by Draft Year

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Testing whether draft year (class) significantly affects outcomes:

$$H_0: \mu_{AV,2017} = \mu_{AV,2018} = \ldots = \mu_{AV,2023}$$

F-statistic: $F = 2.18$ P-value: $p = 0.042$

We reject $H_0$ at $\alpha = 0.05$ — draft year does explain a statistically significant portion of AV variance, which is consistent with the known boom/bust pattern (2022 Purdy class vs. 2021 Lance class).

Conclusions

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2026 Class Assessment

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1. Strongest SDS of the era: Mean SDS of +9.13 is historically high, confidence interval excludes zero — the 49ers drafted with measurable positive value relative to consensus in 2026.

2. Volume strategy is correct: Trading from 7 to 8 picks at a cost of −98 JJ points increases hit probability from 91.8% to 93.1%. Mathematically sound.

3. Positional risk at OT and EDGE: Willis (#127) and Cruz (#179) are late for their position tier. Success will depend heavily on scheme fit in Shanahan’s outside zone system.

4. Two high-ceiling picks: Stribling (WR, 6'4"+, #33) and Prysock (CB, #139, +13 SDS) have the profile of contributors who can exceed positional expectations.

5. Round 3 history is a warning: The 49ers are 1 for 9 (11.1%) in Round 3 historically. Both Height (#70) and Black (#90) face the toughest historical round for this franchise.

Optimal Decision Formula

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$$\text{Optimal Draft Strategy} = \arg\max_{strategy} \left[E[AV] - \lambda \cdot Var[AV]\right]$$

For the 2026 class, the 49ers implemented a volume-over-ceiling strategy — accepting the loss of a Round 1 ceiling pick in exchange for 8 at-bats and a statistically exceptional SDS profile. If one or two of those at-bats become Elite/Solid contributors, the class will justify itself. If Round 3 continues its historical failure rate for this organization, the grade will settle in the C range.

2026 class final projected grade: C — pending player development over the 2026 and 2027 seasons.

Analysis conducted using Python with pandas, numpy, scipy, and matplotlib. Statistical methods include frequentist hypothesis testing, Bayesian inference, linear and logistic regression, Monte Carlo simulation, ANOVA, and composite scoring models. Data sources: Pro Football Reference, Jimmy Johnson Trade Value Chart, ESPN draft consensus rankings.