Resistance Is Not Load: A Programming Distinction That Changes Everything
In programming, resistance and load get used interchangeably. They shouldn’t. They mean different things, and once you understand the distinction, your ability to program with specificity goes to another level.
This episode is a brainstorming discussion between us on how to more accurately define these terms within the Absolute strategy—with historical roots and real-world application.
What We Cover in This Episode
The Soviet grading system for resistance. The Soviets were the first to grade resistance based on how many repetitions a given weight permits. One rep = maximal. Two to three = submaximal. Four to seven = heavy. All the way up to 25+ = very light. This grading system was foundational to their programming of Olympic weightlifting—and it’s still foundational to how we program today.
Why the Soviets needed this system. They weren’t just training—they were practicing the lifts. They needed to constrain resistance so they could get the neural output they wanted: optimal bar path, bar speed, and technical mastery. That’s Perlipin’s chart. That’s the logic that eventually got them beat by the Bulgarians, who cared less about technical mastery and more about scaling up the nervous system.
Louie Simmons blended both approaches. Louie combined Soviet and Bulgarian logic. When programming speed strength, he’d use submaximal resistance (two to three reps) with the intent of highest attainable velocity. The resistance was submaximal, but the intent was maximal. That distinction matters.
Resistance is external. Load is the total output. Resistance is what you put on the bar — straight weight, band tension, chains, or any combination. It constrains the lift. Load is what the neural network actually outputs: resistance multiplied by the total volume of work. Three sets of six at 50 pounds = 900 pounds of load. That’s the cumulative demand on the tissue and the nervous system.
Accommodating resistance changes the game. Once you introduce bands or chains, the resistance is no longer just plate weight. You might have 50% straight weight and 20% band tension for a total of 70% resistance — but the composition of that resistance changes the output. This is where programming gets creative, and where avoiding neurological stagnation becomes possible.
Resistance as strain rate. Here’s the key reframe: resistance determines the strain rate the lift imposes on the athlete.
High resistance = high strain = skewed toward the neural network.
Lower resistance = more repetitions = skewed toward the biological network.
This gives programmers a direct lever for biasing training effects.
The NFL lineman example. An offensive lineman blocking a 400-pound defensive tackle 60–70 times per game—the resistance is the opponent’s mass and force output. The load is 60–70 reps times that resistance over four hours. In offseason programming, you need to build the neural network’s capacity to output that load while managing the biological strain so the tissue doesn’t break down. That’s where the neurological-to-biological asymmetry shows up—the nervous system can keep outputting, but the biology can’t keep up.
Programming on the conjugate edge. The goal is to ride the line between neurological and biological demand. Higher resistance biases top-down neural output. Lower resistance with specific intent (like punch-and-dampen drills at 19–25 reps) biases bottom-up tissue adaptation. The programmer’s job is to fluctuate along that edge—what we call the conjugate edge—so the neural network and biology are synergizing, not stagnating.
Practice vs. training through resistance. When a lineman does punch drills into a bag for 19–25 reps with damping intent, the resistance is very light by Soviet standards. But the intent — pressing isometric into the bag, tuning connective tissue — creates a unique load that’s biologically skewed. That’s technically practice, not training, and it falls on a recovery or deload day. But it’s building the bottom-up tissue architecture that makes the top-down output more efficient over time.
Key Concepts
