Modern NFL pass rush evaluation rests on three distinct analytical approaches: continuous-time player tracking, detailed event charting of moves and stunts, and context-adjusted pressure rates. Each answers a different question—how much disruption a rusher generates over the course of a play, which techniques actually work, and how much pressure a player creates once his situation is accounted for. Together, they replace the noisy snapshot of sack totals with a process-oriented picture of performance.
Beyond the Sack: The Evolution of Pass Rush Analytics
Sacks have long been the currency of pass rush reputation, but they are a poor standalone measure of skill. A sack depends on coverage holding up, on the quarterback holding the ball, and on teammates occupying blockers—factors largely outside any single rusher's control. That variability is why evaluators have shifted toward pressure rate, which counts how often a rusher disrupts the quarterback relative to his pass rush opportunities and, as SumerSports explains in its scouting analysis, functions as a more stable measure of a pass rusher's underlying impact.
The shift is also a shift in philosophy: from outcomes to process. A rusher who consistently wins his matchup but arrives a half-second late to a throw-away is doing his job; a sack total will never show it. Analytics providers report that NFL teams want exactly this richer understanding of blocking and rushing interactions, which is why the field has grown beyond box-score counting into three complementary methodologies.
Continuous-Time Player Tracking: Measuring 'STRAIN'
The first approach uses player tracking data—the same underlying feed behind the league's Next Gen Stats. A research team at Loyola University Chicago, led from its Department of Mathematics and Statistics and its Center for Data Science and Consulting, published a metric called STRAIN in 2023 that evaluates NFL pass rushers at the continuous-time, within-play level. Their stated motivation was that existing pass rush metrics are either discrete-time quantities or based on subjective judgment.
The distinction matters. A discrete metric asks whether a pressure happened; a continuous-time metric tracks how effectively a rusher is collapsing the pocket throughout the entire play, capturing his contribution even when no recorded event results. That makes STRAIN-style tracking useful for evaluating individual pass rusher effectiveness and each lineman's role in generating collective pressure—information that a sack, pressure, or hurry count flattens into a single binary outcome.
Detailed Charting: Unpacking Moves and Stunts
The second approach is human charting. Sports Info Solutions, whose Alex Vigderman documented the effort in July 2026, has charted blocking assignments, block types, pass rush moves, and stunts for every pass play since the 2023 season. Analysts record up to three sequenced pass rush moves for every rusher on each play, though multi-move rushes occur at a low rate and tend to come on longer-developing plays, so first-move use carries most of the analytical weight.
Where this data pays off is in tactical analysis. Vigderman reports that SIS used entropy—a measure of variability—to test whether less predictable rushers are more successful, and found that at least on the extremes, the least predictable edge rushers form a "murderer's row" of top performers. The finding cuts both ways: Will Anderson Jr. and Chris Jones show that elite rushers can succeed without a diverse plan. Charting also captures stunt usage and blocker assignments, letting teams study the interactions between rushers and blockers rather than just the end result.
Context-Adjusted Pressure Rates: Accounting for Game Situations
The third approach adjusts pressure rate for play-level context. SumerSports built a model that produces "Pressure Rate Over Expectation"—a player's pressure rate adjusted for game and play-level factors such as blitzes and stunts. Applied to the 2024 draft class, it showed Byron Murphy II, Jared Verse, and Laiatu Latu leading in adjusted pressure rate, while Latu, Verse, and Chris Braswell led in pressure generated in non-blitz, non-stunt situations—what SumerSports calls non-manufactured pressure.
The distinction is the point. A rusher who pressures only when a stunt or blitz manufactures a free lane is a different player from one who wins one-on-one, and unadjusted pressure rates blur them together. SumerSports also notes the model's limits: adding too many variables risks multicollinearity that inflates performance, and factors like a player's age at the time of each play and his alignment still matter for interpretation.
Comparing Advanced Pass Rush Analytics Approaches
The three methodologies are complements, not competitors, and the table below contrasts them by underlying data, output, and strategic application.
| Analytical Approach | Key Metric(s) | What It Measures | Primary Use Case |
|---|---|---|---|
| Continuous-time player tracking | STRAIN (Loyola University Chicago, 2023) | Pressure exerted over the entire course of a play, not just at discrete events | Evaluating individual rusher effectiveness and contribution to overall defensive pressure |
| Detailed event charting | SIS pass rush detail data (moves, block types, stunts, charted since 2023) | Tactical execution: which moves and move sequences succeed, and how predictable a rusher's plan is | Coaching technique, identifying tendencies, and analyzing rusher-blocker interactions |
| Context-adjusted rates | Pressure Rate Over Expectation (SumerSports) | Pressure rate adjusted for play-level context such as blitzes and stunts | Scouting and comparing players on non-manufactured, one-on-one pressure |
Synthesizing Insights for Player Evaluation and Scheme Design
Put together, the three approaches cover the full evaluation stack. Tracking data establishes how much disruption a rusher generates independent of recorded events; charting data explains the technique behind that disruption and flags predictability; context adjustment separates individual skill from scheme-manufactured production. A team evaluating a draft prospect, a trade target, or its own rotation can triangulate all three rather than leaning on any single number.
The synthesis also informs scheme design. Charting reveals which stunts and move combinations actually produce wins, while context-adjusted rates show which players can be trusted in solo matchups versus which need manufactured lanes. The evidence supports the general direction—teams seeking richer understanding of blocking and rushing interactions—but not team-specific rotational playbooks, which no supplied source documents.
The Future of Defensive Line Evaluation
The decision facing NFL teams is clear: they are increasingly moving toward a multi-faceted analytical approach, integrating continuous-time player tracking, detailed move charting, and context-adjusted pressure rates to gain a more complete understanding of pass rusher performance and optimize defensive strategies. The measurable indicator to watch is continued public discussion from analytics providers and sports media about the application of these advanced metrics in NFL team evaluations and strategic insights—each new published model, charting deep dive, or adjusted-rate leaderboard marks the next milestone in that shift.
