Testing Shawne Merriman's claim: Does Donald at 80% capacity still perform as a Top 3 NFL interior defensive lineman?
RUSH PARAMETERS
80%
50% (Decline)80% (Merriman)100% (Peak AD)
AD99 mass: 120kg | Torque: 680Nm
Tip: Drag DT or adjust vector during snap
0.00s
PASS RUSH WIN RATE
21.8%
TIME-TO-PRESSURE
2.24s
SACK / HIT PROB.
18.5%
SIMULATED DT RANK
#3
馃張
Merriman's Claim Verdict:At 80% physical capacity, Donald's elite leverage yields a 21.8% pressure rate, placing him strictly #3 among interior defenders.
NFL INTERIOR DEFENSIVE LINEMEN BENCHMARKSimulated vs 2024 Active All-Pros
Rank
Player / Capacity
Win Rate (%)
Avg Time to Pressure
QB Hit Prob.
Is 80 percent still top three? Testing the claim with the simulator's own math
A one-minute painted explainer of how this simulator turns physical capacity, technique and protection scheme into a win rate and a ranking, and what its defaults actually say about the 80 percent claim.
Read the narration
Shawne Merriman's claim, as this page frames it: even at eighty percent, Aaron Donald is still a top three interior lineman. This simulator puts a number on it. Capacity doesn't scale one for one. The model raises it to the one point two power, so eighty percent keeps about three-quarters of his peak twenty-six percent win rate: nineteen point nine against a double team. The tool's leaderboard has Chris Jones at twenty-three point five, Dexter Lawrence at twenty-two point one, and Quinnen Williams at twenty point four. So Donald lands fourth, just off the podium. Technique can flip that. The club-swim adds eight percent, lifting him to twenty-one point five and third place. A single guard puts him first; a triple team drops him to twelve point four. Time to pressure is two point oh five seconds divided by the same factors: two point five six at the default. So at the defaults the claim falls just short, even though the verdict text says it holds. Try the technique buttons and watch the ranking move.
What the model assumes
The tool's win-rate formula
Win rate = 26% 脳 capacity^1.2 脳 scheme factor 脳 technique factor, kept between 8% and 32%. Scheme factors relative to a double team: single guard 脳1.39, triple team 脳0.625. Technique: Club-Swim 脳1.08, Spin 脳1.04, Rip 脳1.02, Bull Rush 脳1.00.
What the defaults give
At 80% capacity, Bull Rush, Double Team: 19.9% win rate, 2.56 s time to pressure and a 16.9% sack/hit probability (85% of the win rate). That ranks #4 behind the tool's benchmarks of 23.5%, 22.1% and 20.4%.
A verdict-text bug
At exactly 80% the page always prints "Merriman's claim holds", even when its own rank is #4. At default settings the model says the claim falls just short; Club-Swim or Spin Move, or 82% capacity, lifts Donald to #3.
Benchmarks are the tool's figures
The leaderboard numbers for Chris Jones, Dexter Lawrence, Quinnen Williams and Justin Madubuike are the tool's built-in benchmarks, not official league statistics, and the physics canvas is an illustration. Donald himself retired in March 2024 after ten seasons, 111 sacks and three Defensive Player of the Year awards.
Try it in the tool
With Physical Capacity at 80%, switch Pass-Rush Technique from Bull Rush to Club-Swim and watch Simulated DT Rank move from #4 to #3.
Change Offensive Protection Scheme to 1-on-1 Guard, then Triple Team, and compare Pass Rush Win Rate and Time-To-Pressure.
Drag Physical Capacity up from 80% and find where Donald first ranks #3 with Bull Rush (82%).
Official tracking data on pass rush and time to pressure.
Numbers in the video come from this simulator's built-in formula and benchmark table; they illustrate the model, not real player performance.
Pass-Rush Physics and Win Rate Modeling
How does this simulator model pass-rush win rate and collision dynamics across different capacity levels?
This interactive tool pairs an empirical formula for pass-rush win rate with a 2D Matter.js physics engine to test hypothetical pass-rush scenarios. Rather than running a validated physiological model, the script calculates pass-rush win rate using a power curve scaled to physical capacity, scheme resistance, and move technique, while 2D rigid-body simulation renders contact, impulses, and pocket approach on the canvas.
The simulator relies on hardcoded formula weights and preset All-Pro benchmarks rather than live tracking telemetry or empirical kinesiology data. The 2D canvas treats blockers and rushers as simple geometric shapes driven by 2D forces, omitting 3D hand fighting, pad height, offensive line slide calls, and snap cadence.
Try a worked example
With capacity at 80% and the protection scheme set to Double Team, clicking the 'Club-Swim' button updates the rush technique. The interface recalculates Donald's pass-rush win rate from 19.9% to 21.5%, shortens estimated time-to-pressure to 2.37s, increases torque telemetry to 734Nm, and moves his simulated interior lineman rank from #4 up to #3, surpassing Quinnen Williams on the benchmark table.
Mathematical Heuristic vs. Rigid-Body Dynamics
The telemetry values in the dashboard are evaluated analytically: the script sets pass-rush win rate to 26.0 multiplied by (capacity/100)^1.2, modified by scheme multipliers (0.72 for double team, 0.45 for triple team, 1.0 for single) and technique bonuses (1.08 for club-swim, 1.04 for spin, 1.02 for rip).
Separately, the visual pocket breakdown runs inside a 2D Matter.js simulation. During a snap, the script applies initial linear impulses and directional forces using Matter.Body.applyImpulse and Matter.Body.applyForce, driving the defensive tackle circle toward the quarterback target while offensive line rectangles impart opposing impulses. Matter.Body Module - Matter.js Physics Engine API Docs
Real-World PRWR Context
In official NFL analytics, pass rush win rate assesses whether a defender defeats their block within 2.5 seconds of the snap. In contrast, this page's time-to-pressure formula calculates a synthetic estimate capped between 1.85s and 3.80s, illustrating the general trade-off between rusher burst and blocking resistance. Matter.js Physics Engine API Docs