49ers Draft Intelligence: A Statistical Analysis of the Shanahan/Lynch Era

Introduction

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The San Francisco 49ers, under head coach Kyle Shanahan and general manager John Lynch, have built one of the NFL’s most competitive rosters since 2017. But 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 2017-2024 to quantify their drafting efficiency and identify statistically significant patterns.

Methodology

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Data Collection

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The dataset comprises n = 58 draft picks with the following variables:

Statistical Framework

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I employ three custom metrics to evaluate draft performance:

Metric 1: 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

For the 49ers sample:

$$\bar{SDS} = \frac{1}{n}\sum_{i=1}^{n} SDS_i = -3.0$$

$$s_{SDS} = \sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(SDS_i - \bar{SDS})^2} = 28.7$$

The negative mean indicates the 49ers tend to reach slightly, drafting players an average of three positions earlier than consensus rankings suggest.

95% Confidence Interval for Mean SDS

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$$CI_{95%} = \bar{SDS} \pm t_{0.025, n-1} \cdot \frac{s}{\sqrt{n}}$$

$$CI_{95%} = -3.0 \pm 2.002 \cdot \frac{28.7}{\sqrt{58}} = [-10.5, 4.5]$$

Since this interval contains zero, we cannot conclude the 49ers systematically reach or find steals at a statistically significant level ($\alpha = 0.05$).

Metric 2: Outcome-Adjusted Value (OAV)

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OAV measures realized value relative to positional expectations:

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

Where $E[AV|round]$ represents the expected career AV by round based on 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

Metric 3: Jimmy Johnson Trade Efficiency (JTE)

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The JTE score measures trade value extracted per transaction:

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

Where $V$ represents Jimmy Johnson chart values. For the 49ers:

$$JTE = \frac{5165.1 - 5534.0}{3} = -122.97$$

This negative score indicates a net loss of ~123 draft value points per major trade.

Probability Analysis

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Hit Rate Distribution

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

Observed:

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

Maximum Likelihood Estimate:

$$\hat{p}_{MLE} = \frac{k}{n} = 0.293$$

95% Wilson Score Confidence Interval:

$$CI_{95%} = \frac{\hat{p} + \frac{z^2}{2n} \pm z\sqrt{\frac{\hat{p}(1-\hat{p})}{n} + \frac{z^2}{4n^2}}}{1 + \frac{z^2}{n}}$$

With $z = 1.96$:

$$CI_{95%} = [0.191, 0.418]$$

We can be 95% confident the 49ers’ true hit rate lies between 19.1% and 41.8%.

Hypothesis Test: Are the 49ers Better Than League Average?

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Null Hypothesis: $H_0: p = 0.30$ (league average hit rate) Alternative: $H_1: p \neq 0.30$

Test Statistic:

$$z = \frac{\hat{p} - p_0}{\sqrt{\frac{p_0(1-p_0)}{n}}} = \frac{0.293 - 0.30}{\sqrt{\frac{0.30 \cdot 0.70}{58}}} = -0.116$$

P-value: $P(|Z| > 0.116) = 0.908$

Conclusion: We fail to reject $H_0$. There is no statistically significant evidence that the 49ers draft better or worse than league average ($p = 0.908 » 0.05$).

Conditional Probability Analysis

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Let’s examine conditional hit rates:

P(Hit | Round 1): $$P(Hit|R1) = \frac{3}{9} = 0.333$$

P(Hit | Round 2): $$P(Hit|R2) = \frac{4}{8} = 0.500$$

P(Hit | Round 5): $$P(Hit|R5) = \frac{5}{11} = 0.455$$

P(Hit | Round 3): $$P(Hit|R3) = \frac{1}{7} = 0.143$$

Chi-Square Test: Is Hit Rate Independent of Round?

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Expected values under independence: $E_{ij} = \frac{R_i \cdot C_j}{n}$

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

Degrees of freedom: $df = (7-1)(2-1) = 6$

Critical value: $\chi^2_{0.05, 6} = 12.59$

Conclusion: $\chi^2 = 6.84 < 12.59$, so we fail to reject independence. However, the low p-value ($p \approx 0.34$) suggests a trend worth monitoring with more data.

Regression Analysis

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Linear Model: Career AV vs. Draft Position

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I fit a simple linear regression to examine if draft position predicts career value:

$$AV_i = \beta_0 + \beta_1 \cdot Pick_i + \epsilon_i$$

Results:

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

Interpretation: For every 10 picks later in the draft, expected career AV decreases by 1.1 points. This is statistically significant ($p < 0.001$).

Model Fit:

• $R^2 = 0.194$ (draft position explains ~19% of variance in career AV)
• $R^2_{adj} = 0.180$
• RMSE = 14.8

The low $R^2$ indicates substantial unexplained variance - draft position alone is a weak predictor of NFL success, which aligns with the inherent uncertainty in player evaluation.

Residual Analysis: Identifying Outliers

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Players with standardized residuals $|r_i| > 2$:

Kittle and Purdy are extreme positive outliers - their production far exceeds what draft position would predict.

Logistic Regression: Predicting Hit Probability

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For binary classification (Hit = 1, Miss = 0):

$$\log\left(\frac{P(Hit)}{1-P(Hit)}\right) = \beta_0 + \beta_1 \cdot Pick + \beta_2 \cdot SDS$$

Results:

Interpretation: Neither draft position nor SDS are statistically significant predictors of hit probability at $\alpha = 0.05$, though both show expected directional effects (earlier picks and steals slightly increase hit probability).

Expected Value Analysis: 2026 Draft

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Using the OAV framework, I calculate expected value for potential picks:

Pick #11 Expected Value

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$$E[Value|Pick = 11] = \sum_{outcome} P(outcome) \cdot Value(outcome)$$

Based on historical round 1 distributions:

$$E[AV|Pick = 11] = 15.92$$

Trade Value Optimization

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Using Jimmy Johnson values and Bayesian decision theory:

Stay at #11:

• Value: 1,250 points
• E[AV]: 15.92
• Certainty equivalent: 1,250

Trade up to #5:

• Cost: 1,250 + 470 = 1,720 points
• E[AV]: 18.4
• Net expected gain: +2.48 AV
• Cost per AV point: 189.5 JJ points

Trade down to #20:

• Receive: 850 + ~400 in additional picks
• E[AV] at #20: 13.1
• E[AV] from additional pick (~#50): 8.2
• Total E[AV]: 21.3
• Net expected gain: +5.38 AV

Optimal Strategy: The expected value calculation favors trading down, accumulating picks in high-efficiency rounds (2 and 5).

Monte Carlo Simulation: Draft Strategy Outcomes

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To further validate the trade-down strategy, I ran a Monte Carlo simulation with 10,000 iterations:

Simulation Parameters

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def simulate_draft_outcome(pick_position, n_simulations=10000):
    outcomes = []
    for _ in range(n_simulations):
        # Sample from historical outcome distribution
        outcome = np.random.choice(
            ['Elite', 'Solid', 'Average', 'Below_Avg', 'Bust'],
            p=get_probability_by_pick(pick_position)
        )
        av = sample_av_given_outcome(outcome)
        outcomes.append(av)
    return outcomes

Results

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*Combined probability of at least one Elite player from two picks.

Value at Risk (VaR) Analysis

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Using the 5th percentile as a risk measure:

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

The trade-down strategy has the best downside protection - even in the worst 5% of scenarios, you’re likely to get some value from multiple picks.

Bayesian Analysis: Updating Beliefs

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Prior Distribution on Hit Rate

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Before observing data, assume a Beta prior on the hit rate:

$$p \sim Beta(\alpha_0, \beta_0) = Beta(3, 7)$$

This reflects a prior belief of ~30% hit rate with moderate uncertainty.

Posterior Distribution

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After observing 17 hits in 58 picks:

$$p | data \sim Beta(\alpha_0 + k, \beta_0 + n - k) = Beta(20, 48)$$

Posterior Mean: $$E[p|data] = \frac{20}{68} = 0.294$$

Posterior 95% Credible Interval: $$[0.193, 0.409]$$

The Bayesian analysis confirms our frequentist findings - the 49ers’ hit rate is consistent with league average.

Posterior Predictive: 2026 Draft

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What’s the probability the 49ers hit on their first-round pick in 2026?

$$P(\text{Hit in 2026}) = \int_0^1 p \cdot f(p|data) dp = E[p|data] = 0.294$$

There’s approximately a 29.4% chance the 49ers find an Elite or Solid player with the #11 pick.

Correlation Analysis

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Correlation Matrix

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Key findings:

• Strong negative correlation between pick position and career AV ($r = -0.44$)
• Very strong correlation between AV and Pro Bowls ($r = 0.89$)
• Weak correlation between SDS and outcomes - being a “steal” doesn’t strongly predict success

Summary Statistics

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Descriptive Statistics by Outcome

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ANOVA: Does Outcome Depend on Draft Round?

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$$H_0: \mu_{AV,R1} = \mu_{AV,R2} = … = \mu_{AV,R7}$$

F-statistic: $F = 3.42$ P-value: $p = 0.006$

We reject $H_0$ at $\alpha = 0.05$. Career AV significantly differs across rounds (as expected).

Post-hoc Tukey HSD:

• Round 1 vs Round 7: $p < 0.01$ (significant)
• Round 2 vs Round 7: $p < 0.05$ (significant)
• Round 1 vs Round 2: $p = 0.42$ (not significant)

The Elite Six

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From a statistical perspective, these six players represent extreme positive outliers:

Kittle and Purdy are both beyond 3 standard deviations from expected - true black swan events.

Conclusions

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Statistical Findings

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1. Hit rate is league average: $\hat{p} = 0.293$, 95% CI [0.191, 0.418], not significantly different from 0.30

2. Reaching doesn’t predict failure: Mean SDS for Elite players is -10.3 (reaches), suggesting the 49ers’ board may be more accurate than consensus

3. Round matters, but less than expected: Draft round explains only ~19% of variance in career AV ($R^2 = 0.194$)

4. Trade down maximizes expected value: Monte Carlo simulation shows E[AV] = 21.3 for trade-down vs 15.9 for staying put

5. Variance is the enemy: The trade-down strategy also minimizes downside risk (VaR improvement of +3.0)

Recommendations for 2026

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Based on the statistical analysis:

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

For any risk aversion parameter $\lambda > 0$, trading down dominates staying pat or trading up.

Specific recommendation: Trade pick #11 for picks in the #18-25 range plus a 2nd or 3rd round selection, targeting high-efficiency rounds where the 49ers have historically excelled.

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 and ANOVA. Data sources: Pro Football Reference and Jimmy Johnson Trade Value Chart.