Header images sourced from PFF/Getty Images (Photo by Peter G. Aiken/Getty Images) and the Associated Press (AP Photo/Matt Durisko).

Introduction

Pass-rush evaluation in football has increasingly moved beyond raw sack totals, but there is still substantial disagreement regarding which metrics actually best represent pass-rush ability. While sacks remain the most publicly recognized statistic for edge defenders, modern tracking data has introduced additional measures such as pressures, quarterback hits, hurries, pass-rush wins, and pressure rate. However, many of these metrics are themselves imperfect and inconsistently defined.

One of the major challenges in pass-rush analysis is that concepts like “pressure” are not standardized across the industry. Different data providers define pressures differently, and even within charted datasets, quarterback hits and hurries may involve subjective interpretation. Some pressures occur because a defender cleanly defeats a blocker, while others may result from quarterback movement, offensive breakdowns, or cleanup opportunities created by teammates.

Because of this ambiguity, an important question emerges:

Which pass-rush metrics actually reflect repeatable individual skill?

This study examines the year-to-year stability and predictive power of several commonly used NFL edge pass-rush metrics in order to determine which statistics are most sustainable over time and which are most useful for projecting future sack production.

Rather than focusing exclusively on whether sacks are “lucky” or “random,” the goal of this paper is to identify which pass-rush metrics consistently persist across seasons and which metrics appear heavily influenced by volatility.

Data Collection and Methodology

The dataset for this study was constructed using NFL edge defender data from the 2016 through 2024 seasons.

Player statistics were collected primarily through PFF Premium Stats and exported into season-by-season CSV datasets. Each yearly dataset included both volume and efficiency-based pass-rush metrics for qualifying EDGE defenders.

The datasets were cleaned and merged in Python using player names and consecutive-season matching. To improve stability and reduce small-sample volatility, players were required to record at least 200 pass-rush snaps in consecutive seasons to qualify for inclusion.

After applying the snap threshold and matching players across seasons, the final dataset produced 457 consecutive-season observations.

The following metrics were analyzed:

• Pass Rush Usage % — Percentage of snaps spent rushing the passer

• Pass Rush Win Rate — Percentage of pass-rush reps resulting in a win

• Pressures — Total quarterback pressures generated

• Pressure Rate — Pressures per pass-rush snap

• Sacks — Total sacks recorded

• Sack Rate — Sacks per pass-rush snap

• Sack Conversion Rate — Percentage of pressures converted into sacks

• Pass Rush Wins — Total pass-rush wins generated

• Wins Per Snap — Pass-rush wins per snap

• Expected Sacks — Estimated sack total based on pressure generation

• Sack Residual — Actual sacks minus expected sacks

• Pass Rush Grade — Overall pass-rush performance grade

Because many pass-rush statistics lack uniform standards across data providers, this study uses PFF’s charting definitions for all pass-rush metrics analyzed. Accordingly, pressures, hurries, quarterback hits, pass-rush wins, and related statistics should be interpreted within the context of PFF’s proprietary grading and tracking system.

Year-to-year stability was measured using Pearson correlation coefficients.

A Pearson correlation coefficient measures how strongly two variables move together. In this study, it measures how closely a player’s Year 1 performance in a metric is aligned with their Year 2 performance in the same metric.

Correlation values range from:

• 1.0 = perfect positive relationship

• 0 = no relationship

• -1.0 = perfect negative relationship

Higher correlations indicate greater year-to-year stability and repeatability.

In addition to correlation analysis, single-variable linear regression models were created to test how strongly each Year 1 metric predicted Year 2 sack production. Predictive power was measured using R² values.

Pass-Rush Usage Stability

Pass-rush usage percentage produced one of the strongest year-to-year relationships in the dataset.

The observed correlation was:

• r = 0.687

This suggests that pass-rush deployment is highly role-dependent and remains relatively stable across seasons.

Players who were primarily used as pass-rush specialists in one season generally maintained similar responsibilities in subsequent years. This finding is important because pass-rush opportunity heavily influences production totals. Edge defenders with consistently high pass-rush usage naturally accumulate more opportunities to generate sacks and pressures than hybrid defenders with larger run-defense roles.

The stability of usage reinforces the importance of contextualizing production metrics relative to opportunity.

Pass-Rush Win Rate Stability

Pass-rush win rate emerged as one of the strongest indicators of repeatable pass-rush performance.

The year-to-year correlation was:

• r = 0.629

Among the metrics studied, win rate showed significantly greater persistence than raw sack production.

This finding suggests that consistently defeating blockers is a sustainable skill that translates across seasons more reliably than final sack outcomes.

Unlike sacks, which depend on whether the quarterback is ultimately brought down, win rate isolates the initial pass-rush victory itself. Because of this, it may better capture underlying pass-rush talent independent of external variance.

Pressure Rate Stability

Pressure rate also demonstrated meaningful year-to-year persistence.

The observed correlation was:

• r = 0.521

This indicates that quarterback disruption is relatively sustainable over time.

Edge defenders who consistently generated pressure in one season generally continued doing so the following year.

Compared to sacks, pressures occur more frequently and therefore may provide a larger and more stable sample of pass-rush effectiveness.

This supports the idea that pressure generation may be a stronger representation of underlying pass-rush skill than sack totals alone.

Sack Stability

Sack production demonstrated moderate year-to-year stability.

The observed sack correlation was:

• r = 0.402

This suggests that sacks do contain meaningful signals regarding pass-rush ability, but they are substantially noisier than several process-oriented metrics.

Compared to win rate and pressure generation, sack production appears more sensitive to contextual factors such as quarterback movement, coverage timing, play design, and teammate influence.

While sacks remain valuable outcomes, the weaker stability suggests they may not fully capture sustainable pass-rush performance on their own.

Sack Residual Stability

One of the most important findings of the study involved sack residuals.

Sack residuals were calculated by subtracting expected sacks from actual sacks. Positive residuals represent players who exceeded expected sack production relative to their underlying pressure totals.

The year-to-year correlation for sack residuals was:

• r = 0.060

This relationship was extremely weak.

Players who dramatically overperformed expected sack totals in one season generally did not sustain that same level of overperformance in the following seasons.

This finding strongly supports regression toward expected production levels.

Importantly, the result does not imply that sacks are meaningless. Instead, it suggests that unusually efficient sack finishing above expected levels may be highly unstable year to year.

Sack Conversion Rate Stability

Sack conversion rate produced the weakest relationship in the study.

The observed correlation was:

• r = 0.001

This indicates almost no year-to-year persistence.

Players who converted pressures into sacks at extremely high rates in one season generally did not maintain those same conversion rates the following year.

The finding suggests that pressure-to-sack conversion may be heavily influenced by variance, situational context, and play-level randomness.

As a result, sack conversion efficiency alone appears to provide limited value as a sustainable evaluation metric.

Predicting Future Sack Production

To evaluate predictive strength, Year 1 metrics were individually tested as predictors of Year 2 sack production using linear regression models.

The strongest predictive metrics were:

• Pass Rush Wins — R² = 0.229

• Expected Sacks — R² = 0.218

• Pressures — R² = 0.218

• Pass Rush Grade — R² = 0.176

• Wins Per Snap — R² = 0.165

• Pass Rush Win Rate — R² = 0.164

• Sacks — R² = 0.162

• Pressure Rate — R² = 0.147

Several outcome-oriented metrics performed substantially worse:

• Sack Rate — R² = 0.070

• Sack Residual — R² = 0.004

• Sack Conversion Rate — R² = 0.001

The predictive hierarchy consistently favored process-oriented pass-rush metrics over outcome-based efficiency statistics.

Pass-rush wins, expected sacks, and pressures all outperformed raw sack totals when projecting future sack production.

Limitations

Several limitations should be acknowledged.

First, many pass-rush statistics rely on charting and grading systems that involve some subjectivity. Metrics such as pressures, hurries, and pass-rush wins are not universally defined across data providers, and different charting systems may classify pass-rush outcomes differently.

Second, the analysis does not fully isolate contextual variables such as defensive scheme, surrounding talent, coverage quality, offensive competition, or quarterback tendencies.

Third, correlation does not prove causation. A stable metric is not automatically a perfect representation of skill, but rather a metric that tends to persist more consistently over time.

Additionally, while several metrics demonstrated stronger stability than others, none of the observed relationships were overwhelmingly strong. Even the strongest correlations still leave substantial year-to-year unexplained variance, highlighting the inherently volatile and context-dependent nature of pass-rush production. As a result, these findings should be interpreted as evidence of relative stability rather than definitive predictive certainty.

Finally, the study focused primarily on single-variable relationships. More advanced multivariable modeling could potentially improve predictive performance and further separate stable pass-rush traits from contextual variance.

Conclusion

This study examined the year-to-year stability and predictive power of multiple NFL edge pass-rush metrics using consecutive-season data from 2016 through 2024.

The results consistently showed that process-oriented metrics such as pass-rush win rate, pressures, and pressure rate were substantially more stable than sack conversion metrics and sack overperformance measures.

Meanwhile, sack residuals and sack conversion rate demonstrated almost no persistence across seasons.

The findings suggest that sustainable pass-rush evaluation may be better achieved through repeatable disruption metrics rather than isolated sack outcomes alone. However, the difficulty of predicting future pass-rush production may also reflect the importance of factors beyond pass-rush skill itself, particularly player health and long-term availability.

Overall, the study supports a broader analytical framework in which consistent pressure generation and blocker-defeating ability provide stronger indicators of long-term pass-rush talent than volatile sack efficiency metrics.

Image: Las Vegas Raiders (raiders.com)

Abstract

Sack totals are widely used to evaluate pass-rush performance in the NFL, yet they are inherently low-frequency and outcome-driven. This study examines whether sack production scales nonlinearly with pressure generation among edge defenders. Using a dataset of 769 player-seasons from 2021-2024, a Bill James-inspired Pythagorean-style model was applied to test for nonlinear relationships between pressures and sacks. Model performance was evaluated using mean squared error (MSE) across multiple exponent values. Results indicate that the optimal exponent is x = 1, implying a strictly linear relationship between pressure generation and sack output. Nonlinear transformations significantly increased prediction error. These findings suggest that sack totals are primarily a function of pressure volume and variance, rather than a distinct or nonlinear skill, with implications for player evaluation and contract decision-making.

1. Introduction

Sacks are a primary metric for evaluating pass-rush performance, but they are infrequent outcomes influenced by factors beyond the defender’s control. Pressures, by contrast, capture consistent disruption and provide a more stable measure of performance. This distinction raises a central question: whether sack production scales proportionally with pressure generation or whether higher pressure produces nonlinear increases in sacks.

2. Data and Methodology

The dataset consists of 769 edge defender player-seasons from 2021 through 2024, sourced from PFF Premium data and filtered to include only observations with at least 200 pass-rush snaps. For each player-season, pressures (P), pass-rush snaps (S), and sacks (K) are recorded.

The baseline model assumes a constant conversion rate between pressures and sacks:

With c = 0.154 representing the pooled pressure-to-sack conversion rate across the dataset.

To test for nonlinear scaling, a Pythagorean-style model is applied:

Model performance is evaluated using mean squared error (MSE) over values of x ∈ {1, 1.5, 2, 2.5, 3}. All computations are implemented in Python using pandas for data processing and matplotlib for visualization.

3. Results

The linear model (x = 1) produces the lowest mean squared error (5.65). Increasing the exponent substantially increases prediction error, indicating that nonlinear transformations degrade model performance.

Predicted sack totals from the linear model closely align with observed values across the dataset.

Residual variation increases with expected sack totals, indicating greater volatility in high-production seasons.

4. Discussion

The results indicate that sack production scales linearly with pressure generation. There is no evidence that higher pressure levels produce disproportionate increases in sack output. Instead, sack totals are well approximated by pressure volume multiplied by a constant conversion rate.

At the individual level, deviations from expected production reflect variance rather than systematic differences in ability. For example, Maxx Crosby’s 2021 season generated high pressure but fell well below expected sack totals, illustrating how sack outcomes can diverge from underlying performance. Such deviations reinforce the view that sacks are a noisy outcome rather than a stable measure of pass-rush ability. His following seasons saw increases in sack production, consistent with the model’s implications, though predictive performance is not directly tested in this study.

5. Conclusion

This study finds that a linear model best describes the relationship between pressures and sacks among NFL edge defenders. Nonlinear models do not improve predictive accuracy. Sack production is therefore best understood as a function of pressure, volume, and variance, reinforcing the importance of process-based metrics in player evaluation.

6. Future Work

Future research may examine year-over-year predictive relationships or incorporate contextual variables to better understand sack conversion variation. This model also provides a framework for identifying underperformers and overperformers in the free-agent market, highlighting potential buy-low candidates and players likely to be overvalued.

Image: Oregon Athletics / GoDucks.com, cropped and edited by author

Introduction

Single-season pass-rush metrics are often noisy and can vary significantly from year to year, which makes them difficult to use as reliable predictors of future performance. Because of this variability, statistical inference can be used to estimate a reasonable range for a player’s underlying pass-rush efficiency rather than assuming that a single season’s results perfectly reflect their ability.

By constructing a confidence interval around a player’s pressure rate, we can estimate a range for their true pass-rush ability and translate that range into a projected interval for future production. This approach provides a more uncertainty-aware way to evaluate pass-rush performance than relying on a single observed statistic.

Case Study: Matayo Uiagalelei

For this analysis, I will use Matayo Uiagalelei of the Oregon Ducks. Uiagalelei is a useful case study because he accumulated a large number of pass-rush snaps at a major program and has chosen to return to Eugene for his senior season, making his future performance particularly relevant to the project.

During the 2025 season, Uiagalelei recorded 47 pressures across 338 pass-rush snaps.

Methodology

To estimate Uiagalelei’s underlying pass-rush efficiency, a 95% confidence interval for his pressure rate is constructed using the sample proportion method.

First, the observed pressure rate is calculated:

p-hat = 47 / 338
p-hat = 0.13905

Next, the standard error of the sample proportion is calculated using the formula:

SE = sqrt[(p-hat(1 − p-hat)) / n]

Substituting the values gives:

SE ≈ 0.0188

Using the standard 95% confidence interval formula

p-hat ± 1.96(SE)

we obtain:

0.13905 ± 1.96(0.0188)

Confidence Interval = (0.1022, 0.1759)

Interpretation of the Confidence Interval

This means we are 95% confident that Uiagalelei’s true underlying pressure rate lies between approximately 10.2% and 17.6%.

Although his observed pressure rate during the 2025 season was about 13.9%, statistical uncertainty suggests that his true pass-rush efficiency likely falls somewhere within this broader range.

Projecting Future Production

To convert this efficiency range into a projection for next season, we assume Uiagalelei receives the same number of pass-rush snaps (338).

Multiplying the lower and upper bounds of the interval by the projected snap count gives:

338 × 0.1022 = 34.5
338 × 0.1759 = 59.5

This produces a projected pressure range of approximately 35 to 59 pressures.

If Uiagalelei receives a similar number of pass-rush opportunities and his underlying pass-rush ability remains within the estimated range, his pressure production next season would likely fall somewhere between roughly 35 and 59 pressures.

Limitations

This projection should not be interpreted as a guaranteed outcome. Future performance can vary due to factors such as opponent strength, scheme changes, role adjustments, health, and normal year-to-year variation in pass-rush outcomes.

In addition, this analysis relies on data from a single season, which may not fully capture a player’s long-term performance level.

Conclusion

Single-season pass-rush metrics can provide useful information, but they are often influenced by randomness and limited sample sizes. By constructing a confidence interval around pressure rate, analysts can estimate a plausible range for a player’s true pass-rush efficiency and convert that range into a more realistic projection for future production.

While this approach does not eliminate uncertainty, it provides a more statistically grounded way to evaluate pass-rush performance.

Abstract

Sack totals often dominate how NFL edge rushers are evaluated. They headline box scores, shape public perception, and frequently drive free-agent pricing decisions. That being said, sacks are among the noisiest outcomes in football; they are low-frequency events, highly sensitive to variance, and only weakly representative of underlying pass-rush process. Despite this, teams routinely commit significant cap resources to edge defenders coming off sudden sack spikes, implicitly treating these outcomes as evidence of long-lasting improvement.

Using recent NFL free-agent contracts, this study shows that edge rushers who experience large sack increases immediately before free agency receive substantially higher multi-year compensation and stronger contract guarantees than comparable non-breakout players, despite failing to deliver superior post-contract pass-rush efficiency. The results suggest the presence of a False Breakout Premium in the EDGE free-agent market, where teams systematically overpay for outcome-driven performance that does not persist.

1. Introduction

In order to evaluate whether recent sack breakouts are priced efficiently in free agency, this study analyzes a sample of NFL edge rushers who signed free-agent contracts during the 2018–2025 offseasons. Breakouts are defined as large increases in sack production, an increase of five or more sacks from the previous season, occurring in the year immediately preceding contract signing. To distinguish outcome-driven spikes from changes in role or opportunity, breakout seasons are restricted to cases in which pass-rush snap volume increased by no more than 25 percent from the prior year.

Contract values are normalized as a percentage of the league salary cap to account for variation in market conditions across offseasons and to allow for direct comparison across contracts signed in different years. The analysis then examines whether the elevated compensation awarded to breakout players is justified by post-contract pass-rush efficiency, measured using pressure rate and sack rate in the first season following signing.

This analysis proceeds in three stages. First, differences in contract commitment are evaluated by comparing the likelihood that breakout and non-breakout players receive multi-year versus single-year agreements. Second, conditional on multi-year commitment, compensation levels are compared using median salary-cap–adjusted average annual value and guaranteed compensation. Finally, post-contract performance is assessed by comparing first-year pass-rush efficiency outcomes between the two groups. This structure separates teams’ willingness to commit from the magnitude and security of compensation awarded and allows pricing decisions to be evaluated directly against realized on-field performance.

2. Data and Sample Construction

The sample consists of NFL edge rushers who signed free-agent contracts between 2018 and 2025. To focus on market-relevant contracts, the analysis excludes low-end deals below a minimum salary-cap threshold. Edge defenders are identified based on primary alignment and pass-rush usage rather than listed position alone.

Single-year and multi-year contracts are treated separately throughout the analysis. Single-year deals often reflect short-term risk management, prove-it contracts, or role uncertainty, whereas multi-year contracts represent a clearer expression of long-term valuation. As a result, compensation comparisons are restricted to multi-year contracts, ensuring that pricing outcomes reflect sustained commitment rather than short-term optionality.

2. Data and Sample Construction

2.1 Sample Construction

The sample consists of NFL edge rushers who signed free-agent contracts between 2018 and 2025. To focus on market-relevant contracts, the analysis excludes low-end deals below a minimum salary-cap threshold. Edge defenders are identified based on primary alignment and pass-rush usage rather than listed position alone.

Single-year and multi-year contracts are treated separately throughout the analysis. Single-year deals often reflect short-term risk management, prove-it contracts, or role uncertainty, whereas multi-year contracts represent a clearer expression of long-term valuation. As a result, compensation comparisons are restricted to multi-year contracts, ensuring that pricing outcomes reflect sustained commitment rather than short-term optionality.

2.2 Data Sources

Contract terms, average annual value, guarantees, and signing-year salary cap figures were obtained from Spotrac. Pass-rush statistics, including pressures, sacks, and pass-rush snap counts, were sourced from Pro Football Focus (PFF) Premium data. Salary cap figures were used to normalize compensation across offseasons to allow for direct comparison of contracts signed in different years.

3. Measuring Post-Contract Performance

Post-contract performance is evaluated using pass-rush efficiency metrics rather than aggregate production totals. Specifically, pressure rate and sack rate in the first season following contract signing are used to capture per-opportunity pass-rush effectiveness. These rate-based measures reduce the influence of snap volume, team context, and game environment, providing a clearer signal of individual performance than raw sack totals alone.

Focusing on first-year post-contract outcomes allows the analysis to assess whether the market’s valuation of breakout players is immediately validated. If sack breakouts reflect durable improvement, breakout players should be expected to outperform non-breakout peers on a per-snap basis shortly after signing.

4. Results

4.1 Contract Commitment

Breakout players are significantly more likely to receive multi-year contracts than non-breakout players. Of the breakout sample, 9 of 11 players (81.8%) received multi-year deals, compared to 65 of 104 non-breakout players (62.5%). Conversely, only 18.2% of breakout players signed single-year contracts, compared to 37.5% of non-breakout players.

This pattern indicates that teams display greater confidence in players coming off sack breakouts at the commitment margin.

4.2 Pricing and Guarantees

Among players who received multi-year contracts, breakout players were compensated at substantially higher levels. The median salary-cap–adjusted average annual value (APY) for breakout players was 6.76% of the salary cap, compared to 3.73% for non-breakout controls—an increase of approximately 81%.

This pricing premium extended beyond headline APY. Breakout players also received meaningfully stronger guarantees, with a median guaranteed compensation equal to 8.77% of the salary cap, compared to 4.66% for non-breakout players. The elevated guarantees suggest that teams expressed not only a willingness to pay more annually, but also greater confidence in the persistence of breakout performance.

The distribution of salary-cap–adjusted APY visually reinforces this pricing premium, with breakout players exhibiting a clear upward shift in typical compensation relative to non-breakout controls.

4.3 Post-Contract Performance

Despite receiving significantly higher compensation, breakout players did not deliver superior post-contract performance. In the first season following signing, breakout players recorded a median sack rate of 1.04%, compared to 1.85% for non-breakout players. Similarly, breakout players posted a lower median pressure rate (9.9%) than non-breakout controls (11.33%). The distribution of first-season pressure rates further illustrates this result, showing substantial overlap between breakout and non-breakout players and no upward shift in typical post-contract pass-rush efficiency for breakout signings.

The consistency of this performance shortfall across both outcome-based (sack rate) and process-based (pressure rate) measures suggests that the pricing premium associated with breakout seasons is not supported by realized on-field efficiency.

5. Discussion

Taken together, the results indicate that NFL teams systematically overweight recent sack outcomes when pricing edge defenders in free agency. Sack breakouts are associated with higher likelihood of long-term commitment, substantially higher annual compensation, and significantly stronger guarantees. However, these investments are not matched by superior post-contract pass-rush efficiency.

This pattern is consistent with outcome bias in a market characterized by positional scarcity and competitive bidding. Because edge rushers are both valuable and difficult to acquire, teams may place disproportionate weight on salient but noisy signals such as sack totals. When such signals are mistaken for durable improvement, contract prices reflect optimism that is not borne out on the field.

Importantly, these findings do not imply that breakout players are ineffective or un-rosterable. Rather, they suggest that the magnitude and security of the compensation awarded following breakout seasons exceeds the incremental performance delivered after signing. In this sense, the inefficiency lies in pricing rather than player quality.

While the preceding results highlight systematic patterns in how edge defenders are priced following breakout sack seasons, several limitations should be noted. This analysis focuses on first-season post-contract performance and does not evaluate longer-term outcomes, which may differ for individual players. The sample is restricted to recent free-agent cycles and excludes contract extensions, which may reflect different negotiation dynamics and information environments. Although rate-based metrics reduce the influence of snap volume and team context, they cannot fully isolate individual performance from scheme, role, or supporting cast. Accordingly, the results characterize typical market outcomes rather than deterministic player trajectories.

6. Conclusion

This study provides evidence of a False Breakout Premium in the NFL EDGE free-agent market. Edge rushers coming off sharp sack increases are more likely to receive multi-year contracts, are paid significantly higher shares of the salary cap, and receive stronger guarantees than comparable non-breakout players. However, these players do not deliver superior post-contract pass-rush efficiency, indicating that teams systematically overpay for outcome-driven performance that does not persist.

For teams, the results highlight the importance of prioritizing process-based evaluation over noisy outcome measures when allocating long-term cap resources. For analysts and fans, the findings reinforce the limitations of sack totals as a standalone indicator of defensive performance. More broadly, the study illustrates how even sophisticated markets can misprice talent when volatile but salient signals dominate evaluation.

Box-score statistics omit crucial contextual information. A player’s role can be more important than a raw sack total; pass-rush snap count provides necessary context for determining whether production reflects efficiency or simply volume of opportunity. Variance further complicates evaluation, as isolated finishing plays can disproportionately influence perception. A single high-visibility sack should not be weighted equivalently to repeated, down-to-down disruption when assessing pass-rush value.

The Big Ten offers an effective environment to examine edge rushers because it emphasizes pro-style offensive structures and trench-oriented play. Big Ten offenses often feature heavier offensive lines and less schematic manipulation, which limit manufactured pressure and highlight individual pass-rush responsibilities. This environment allows for a cleaner evaluation of one-on-one pass-rush effectiveness.

To illustrate why sack totals alone can misrepresent pass-rush impact, this case study examines a targeted sample of current Big Ten edge rushers with clearly defined roles. Rather than ranking players or predicting future performance, this analysis is intentionally descriptive and focuses on how opportunity, role, and consistency provide context for common statistics. The objective is not to diminish the value of sacks, but to demonstrate how their meaning changes when evaluated alongside usage and disruption.

Rather than introducing complex models, this comparison relies on four core data points:

• Pass-rush snaps - This directly reflects opportunity and usage.

• Sacks - The most visible outcome.

• Pressures - This better represents consistent disruption.

• Pressure Rate - Shows impact based on snap count.

No single metric is evaluated independently; each functions as part of a contextual framework.

Viewed collectively, these metrics reveal how sack totals alone can distort pass-rush evaluations. Players with high snap volumes may generate consistent pressure without producing eye-catching sack numbers, while others accumulate sacks on fewer opportunities due to role, scheme, or finishing variance. Without accounting for opportunity and down-to-down disruption, sack totals risk overstating the impact of some players while understating the contributions of others who affect the quarterback more consistently.

Table 1 illustrates these dynamics within the Big Ten sample. While players such as Gabe Jacas and Anthony Smith post impressive double-digit sack totals, their production occurs across markedly different snap counts than players such as Zach Durfee and Aidan Hubbard, reflecting varied defensive roles. If evaluation were limited to sacks alone, Matayo Uiagalelei’s production would appear significantly inferior despite comparable overall pressure output. This pattern is repeated across the sample: although sack totals differ between Durfee and Hubbard, their pressure rates demonstrate how outcome-based evaluation alone fails to capture true pass-rush impact.

Taken together, these contrasts reinforce the central idea of this case study: sack totals describe outcomes, while opportunity and pressure illuminate the process behind those outcomes. When pass-rush production is viewed through the combined lens of usage and disruption, it becomes clear that sacks alone are insufficient for capturing a defender’s true impact on the quarterback.

Historically, sack totals played a disproportionate role in pass-rusher evaluation, particularly before pressure-based metrics were widely available. In earlier draft eras, finishing plays were often treated as a direct indicator of pass-rush impact, leading evaluators to overestimate the translatability of sack-heavy production while undervaluing players whose impact was driven by consistent pressure rather than box-score results.

This dynamic is evident in the Tampa Bay Buccaneers’ decision-making process leading to the selection of Gaines Adams fourth overall in the 2007 NFL Draft following a final collegiate season defined by double-digit sack production at Clemson. Adam’s evaluation emphasized visible sack production and athletic traits, yet his professional career was marked by limited pass-rush impact and inconsistent pressure generation. The selection shows how sack-driven evaluation can overstate translatable impact when disruption-consistency and role-sustainability are insufficiently weighted.

The lessons drawn from earlier sack-centric evaluations align directly with the patterns observed in this Big Ten sample. Just as historical evaluations often treated sack totals as a direct indicator of pass-rush impact, the Big Ten data illustrate how players with similar pressure output can arrive at markedly different sack totals depending on role, snap distribution, and finishing variance. In both contexts, sack production reflects outcomes rather than process. When opportunity and disruption are accounted for, the apparent gaps created by sack totals narrow significantly, reinforcing the idea that evaluation errors arise not from sacks themselves, but from interpreting them without sufficient contextual grounding.

This case study does not argue that sacks are meaningless, but rather that they require context in order to be interpreted correctly. Opportunity, role, and consistency all influence how sacks accumulate over a season. Sacks describe outcomes; pressure and usage help explain the process. While limited in scope and descriptive by design, this case study underscores the importance of pairing visible outcomes with underlying usage when evaluating pass rushers.