Your Lower Back May Be Limiting More Than You Think

The lower back tends to enter the conversation only when it hurts. Until then, most people treat it as something that should stay out of the way while the “real” muscles do the work. You train your legs with squats, your hamstrings with Romanian deadlifts, your back with rows, and your shoulders with presses. The lower back is simply assumed to be along for the ride.

But that’s not really how those movements work.

Your lower back is one of the structures responsible for maintaining the position that allows force to move through the rest of the body. In a squat, your legs and hips may be producing most of the force needed to move the weight, but that force still has to pass through a trunk capable of holding its position. In an RDL, your hamstrings and glutes may be strong enough to keep working, but if your spinal erectors can no longer maintain the position of your torso, the set is effectively finished. The same thing can happen in a bent-over row, a loaded carry, a deadlift, or even a standing press.

This is where the lower back becomes more than a support structure. It becomes part of the system that determines how much of your strength you can actually use.

A person can have strong legs and still struggle to express that strength in a squat because the torso loses position under load. The hamstrings can have more to give during an RDL while the lower back is already exhausted. The upper back can still be capable of producing force in a row, but that capacity becomes irrelevant once you can no longer hold the position required to perform the exercise well. In each case, the muscle you’re trying to train may not be the thing ending the set.

That distinction is important because it changes how you think about weak links. A weak link isn’t necessarily the muscle producing the least force. Sometimes it’s the structure that can no longer maintain the conditions that allow everyone else to keep producing force.

Working Hard Doesn’t Mean It’s Fully Trained

This is also where the usual response of “I already squat and deadlift, so my lower back gets plenty of work” becomes a little too simple.

Of course it gets work. The spinal erectors are involved heavily in those exercises. But involvement and complete development aren’t the same thing.

A muscle can accumulate a lot of fatigue while performing a supporting role without necessarily being trained in the way that would best improve its strength or endurance. During a squat, for example, the lower back may be working very hard to resist the tendency of the torso to collapse forward. That can make it tired. It doesn’t automatically mean the squat is the best exercise for improving every quality of lower-back strength.

This is one of the reasons direct training can make sense even for someone who already performs plenty of compound lifts. It allows you to isolate the problem more deliberately. You can choose the position you want to strengthen, control the amount of load and fatigue, and build capacity without relying on the lower back to improve as a side effect of something else.

That doesn’t mean everyone needs to start hammering heavy good mornings three times a week. The lower back already participates in a lot of training, so direct work has to account for everything else you’re asking it to do. If you’re squatting, deadlifting, doing unsupported rows, and performing heavy hinges during the same week, piling more heavy hinge work on top may create more fatigue than progress. The point of direct training is to address a limitation, not to create another one.

Different Exercises Challenge the Lower Back Differently

It also helps to stop thinking of every posterior-chain exercise as interchangeable. An RDL, good morning, 45-degree back extension, horizontal back extension, and reverse hyper can all involve the lower back, but the mechanical demands aren’t identical.

One useful way to organize them is by looking at where in the movement they tend to become most demanding.

Romanian deadlifts and good mornings place a large demand on the lower portion of the hinge. As the torso travels farther away from upright, the weight gains more leverage against you. The muscles responsible for extending the hips and maintaining the position of the spine have to produce more force to resist that leverage. This makes RDLs, standing good mornings, and seated good mornings useful when you want to become stronger while the torso is farther forward.

A 45-degree back extension changes that profile. It generally moves the challenge more toward the middle and upper portion of the movement, although this is where it becomes important not to pretend these categories are exact. Bench angle, range of motion, body proportions, technique, and where you hold the weight can all change the exercise considerably. Holding a plate against your chest creates a different resistance profile than holding it at arm’s length. Increasing the range changes the problem again. The 45-degree extension is better thought of as a flexible tool that can be manipulated than as an exercise that belongs permanently to one exact point on a strength curve.

Horizontal back extensions and reverse hypers shift the problem again. As the torso approaches horizontal during a back extension, or as the legs rise toward horizontal during a reverse hyper, the external demand increases. These movements can therefore be useful for strengthening the upper portion of the range.

The reverse hyper also deserves to be considered separately from a conventional back extension because the body is organized differently. In a back extension, the lower body is relatively fixed while the torso moves. In a reverse hyper, the torso is supported while the legs move. Both involve the posterior chain, but they ask the system to solve the movement in different ways.

None of this means the lower back is neatly divided into three separate sections that can be isolated with the correct exercise. That would be taking the strength-curve idea too literally. These classifications are useful because they help us think about where an exercise tends to create the greatest external demand, but the exact profile will always depend on how the exercise is performed.

What This Means for Your Training

You don’t need every lower-back exercise in the same program. In most cases, that would simply be redundant.

A better approach is to look at what your current training already provides and where you appear to lose capacity. If you already perform heavy squats and RDLs, you’re getting a considerable amount of work while the torso is inclined forward. Adding another heavy good morning may give you more of the same thing when what you actually need is additional work with a different resistance profile. A 45-degree back extension or reverse hyper might make more sense.

If your position consistently breaks down near the bottom of an RDL or good morning, strengthening that deeper hinge position may deserve more attention. If you’re strong there but lose control or force as you approach extension, another exercise may be more useful. The exercise should solve a problem you actually have.

The same principle applies to strength and endurance. The lower back needs enough strength to tolerate heavy loads, but it also needs enough endurance to maintain position as a set continues and fatigue builds. A person can be reasonably strong for a few heavy repetitions and still have the lower back become the limiting factor during longer sets. On the other end, someone can perform endless light back extensions and still lack the strength required to hold position when the loading becomes meaningful.

Good programming develops both qualities while accounting for the work the lower back is already doing elsewhere.

That’s really the larger point. The lower back shouldn’t be treated as a fragile area that you hope survives your training, nor should it be buried under endless extra work simply because “a strong back is important.” It’s trainable musculature with a specific role in the system. When it can maintain position under greater loads and for longer periods of time, the muscles above and below it have a more stable structure through which to express their strength.

Sometimes the reason a lift stops improving isn’t that the obvious muscle needs to become stronger. Sometimes the rest of the system is already capable of more, and the structure connecting it all together simply hasn’t caught up.

Can an Algorithm Really Tell You How Much Weight to Lift?

There is an understandable appeal to removing the guesswork from training. If an app knows what you lifted last week, how many reps you completed, and what the program asks you to do next, it seems reasonable that it should be able to tell you exactly what weight to put on the bar. That is especially attractive when progressive overload is commonly explained as some version of doing more over time.

One common way to turn that idea into a program is to increase the training weight by a fixed percentage from week to week. The 2.5% figure is not a law of human performance. It is simply a practical example of a small, manageable progression: if you lifted 100 lbs this week, the program might prescribe 102.5 lbs next week, then approximately 105 lbs after that, continuing upward as long as the increases remain achievable. The number is small enough to seem realistic, while still creating a clear progression rule.

There is nothing inherently wrong with using a progression rule like that. For some people, particularly newer lifters, it may work quite well for a while. The problem begins when a simple progression rule is presented as though it is an individualized prediction of what someone should be capable of lifting. Increasing everyone's training weight by 2.5% because another week has passed isn't really an individualized model. It is a predetermined progression applied to individuals.

The difficulty is that strength doesn't progress at a uniform rate, either between people or within the same person over time. Someone who has been lifting for three months can improve incredibly quickly, while someone who has trained seriously for ten years may spend months trying to add a few pounds to the same exercise. Even that distinction is incomplete because training age doesn't tell us how experienced someone is with a particular movement. An experienced lifter who has never performed a front squat may improve rapidly for several weeks simply because they are learning how to perform the exercise better.

That improvement is still real in the sense that they can now lift more weight, but it doesn't necessarily mean that their muscles became 15% stronger over that period. Early improvements can come from better intermuscular and intramuscular coordination, improved technique, greater comfort under load, better understanding of effort, and a growing willingness to actually push the set. A beginner may add 10 or 20% to an exercise over a relatively short period without experiencing anything close to a corresponding increase in muscle mass.

This immediately creates a problem for any algorithm that assumes everyone's performance should move according to the same percentage.

It becomes even more complicated when we consider how differently people approach training. Two people can have the same training age, perform the same exercise, and possess roughly similar strength, yet require very different loading decisions. One person may consistently underestimate what they are capable of and stop sets with 4 or 5 reps left in the tank. Another may routinely push every set to the edge of failure regardless of what was prescribed. Someone else may be technically excellent and unusually consistent from session to session, while another person's execution changes enough that the same number on the bar doesn't always represent the same exercise.

A coach learns these things about an athlete over time. You learn that someone's reported 2 reps in reserve is usually closer to 4. You learn that another athlete will attempt almost anything you put in front of them, so the coaching problem is occasionally holding them back rather than convincing them to work harder. You learn who responds well to larger jumps in weight, who needs smaller increases, whose performance varies considerably from day to day, and who is remarkably predictable.

None of this means that algorithms are useless. It means that predicting training load is a much more complicated problem than multiplying last week's weight by 1.025.

What Are We Actually Trying to Predict?

The first thing to clarify is the algorithm's prediction target.

A useful question is:

Given what this person has demonstrated recently on this exercise, what load gives them the best chance of completing today's prescribed reps at approximately the intended level of effort?

A person's 1RM can still provide useful context, but the practical training decision concerns the load most likely to produce the intended reps and effort in today's session.

If the workout calls for three sets of 8 reps with approximately 2 RIR, the algorithm is trying to predict the weight most likely to create that outcome.

That is a different problem from simply estimating maximal strength.

A person's recent training already provides useful information. If they performed 185 lbs for 8 reps with approximately 2 reps remaining, that set tells us something about their current capacity. If they later perform 190 for the same reps and effort, or perform more reps with 185, we have evidence that their usable strength on that exercise has improved.

This gives us a better foundation than predetermined weekly increases because the progression now follows demonstrated performance.

A simple estimate could begin with:

Estimated reps to failure = completed reps + reps in reserve

If someone performs 8 reps with 2 left, we can treat that set approximately like a ten-rep maximum for estimation purposes.

An established rep-max equation can then turn that performance into an estimated current strength value. The exact equation will never be perfect, but that is acceptable because the purpose is not to discover someone's true physiological maximum to the pound. We are trying to create a useful estimate that can be updated continually as more information becomes available.

The important change is that every training session becomes another observation.

The Athlete's History Should Become More Important Over Time

Initially, an algorithm knows almost nothing about someone. It has to rely on general assumptions about relationships between reps, load, effort, and strength. That is unavoidable.

But the longer someone uses the system, the less those generic assumptions should matter.

Imagine a general equation predicts that someone should be able to perform 8 reps with 2 RIR at roughly 80% of their estimated max. After several months, however, the athlete's training history consistently shows that they perform that prescription better around 75%.

At that point, continuing to insist that they should use 80% because a generalized equation says so would defeat the purpose of having individual data.

The model should gradually learn the athlete's own relationship between load, reps, and effort for each exercise.

This distinction also needs to exist between exercises. Someone's bench press, back squat, split squat, and dumbbell row do not necessarily follow the same rep-to-load relationship. The more data an athlete accumulates on an exercise, the more that exercise should develop its own prediction model.

Training history can therefore become increasingly specific:

This person, on this exercise, in this rep range, at approximately this effort, has historically performed best around this percentage of their estimated capacity.

That is much closer to meaningful individualization.

Training Age Plays a Role

Training age should probably influence the model. Not by assigning everyone with the same experience the same progression rate but in determining how stable we expect someone's performance to be.

A new lifter can change dramatically from week to week because they are learning how to lift. Their technique and coordination improves, their perception of effort changes, and they become more comfortable producing force. Their predicted load may therefore need to move rapidly.

An experienced athlete is usually different. If someone with ten years of serious training suddenly appears to have increased their strength by 15% in a week, that observation deserves more skepticism. It could be real, but it could also reflect an unusually good session, a different technique, inaccurate RIR reporting, changes in equipment, simple measurement noise, or anabolics.

So rather than using training age to determine the predicted weight itself, I would use it initially to influence the amount of expected variation around the prediction.

An athlete with less than a year of experience might reasonably have a much wider prediction range than an advanced athlete whose performance has been stable for years.

For example, if the model predicts a working weight of 200 lbs, the novice prediction might initially be something like:

200 lbs, with substantial expected variation

while the experienced athlete might receive:

200 lbs, with a much narrower expected range.

Those ranges should eventually stop being determined primarily by training age. Once enough individual data exist, the athlete's actual historical variability should take over.

Someone with eight years of training may still have highly variable performance. Someone with two years may be incredibly consistent. Their own history is better evidence than the category we initially placed them in.

Effort Creates Another Layer of Uncertainty

The model also has to account for the fact that reps in reserve are subjective.

If a workout calls for 8 reps with 2 remaining, the system may assume that someone reporting 2 RIR could have completed approximately 10 reps. But that assumption only works if their perception of effort is reasonably accurate.

Some people become quite good at estimating proximity to failure. Others are consistently wrong.

This is another area where a coach develops context that is difficult to capture from a single number. Over time, however, an algorithm could begin learning from the discrepancy between predicted and demonstrated capacity.

If someone routinely reports 2 RIR but occasionally performs a calibrated set close to failure and demonstrates that they actually had 5 reps available, the model now has evidence that this person's RIR reporting is systematically conservative.

It could begin adjusting future predictions accordingly.

Again, the system becomes more useful when it learns from behavior rather than assuming that every person's "2 RIR" means the same thing.

Then Life Happens

Even after accounting for training age, exercise history, effort, and previous performance, we still have the problem that human performance fluctuates.

Sleep, nutrition, carbohydrate availability, stress, soreness, recent training, illness, travel, and motivation can all influence performance, but none of these variables behaves like a simple switch.

Sleeping poorly doesn't mean that you will necessarily perform poorly the following day. Someone can sleep 4 hours and still hit a PR. Another athlete can sleep 9 hours, eat perfectly, report low stress, and have a terrible session because his mind is somewhere else.

That makes lifestyle information useful, but dangerous if it's interpreted too literally.

An algorithm that says:

"You slept poorly, therefore reduce today's load by 7%."

may be just as misleading as one that completely ignores sleep.

A better interpretation is that lifestyle variables change the uncertainty of today's prediction.

If someone's recent training suggests that 200 lbs is appropriate, poor sleep and unusually high stress might make us less confident that 200 will behave normally today. They don't necessarily tell us whether the correct weight is 190, 200, or 205.

The prediction could therefore remain centered around 200 while the expected range becomes wider.

This is probably one of the most important distinctions in the entire model.

Lifestyle data should generally influence confidence in the prediction before the athlete trains, while actual performance during the session should quickly become the stronger source of information.

If the athlete slept badly but their warm-ups are fast, their technique looks good, and their first working set is considerably easier than expected, the system should respond to what is happening in front of it.

A coach naturally does this.

They may walk into the session expecting the athlete to be flat because they know the athlete was up all night. But if the athlete starts moving extremely well, the coach changes the plan.

The sleep data provided context rather than becoming a command.

The First Working Set May Be the Best Readiness Test

This leads to what may ultimately be the most useful feature of the entire model.

Instead of expecting an algorithm to perfectly predict someone's capacity before they touch a weight, we can use the algorithm to choose the best starting load and then use the athlete's actual performance to update the session.

Suppose the model recommends:

200 lbs for 8 reps at 2 RIR.

The athlete performs the first set and reports:

200 × 8 @ 4 RIR.

We now have better information than we had 10 minutes earlier. Whatever the sleep score, stress score, calorie intake, or training-age model predicted, today's actual performance suggests that the initial recommendation was conservative.

If instead the athlete performs:

200 × 7 @ 0 RIR,

we also learned something. Today's capacity is probably lower than expected.

At that point, the actual set should outweigh most of the softer readiness information.

This creates a hierarchy that resembles how good coaching already works:

What is the athlete demonstrating right now?

Then:

What have they demonstrated recently?

Then:

What does their longer-term history tell us?

Then:

What contextual factors might affect today's performance?

And finally:

What do population-level assumptions suggest when individual information is unavailable?

The farther down that hierarchy we go, the less confidently the information should dictate the training decision.

What the Algorithm Could Actually Look Like

If we were going to build a more defensible training-load algorithm, I would structure it around a series of estimates rather than one progression percentage.

The first layer would estimate current exercise-specific capacity from recent working sets:

Load + Reps + Reps in reserve

Each set provides an estimated strength value.

Several recent observations are then combined, with more recent and more informative sets receiving greater weight. A hard set performed last week should probably tell us more about current capacity than an easy set performed three months ago.

That produces an estimate of current capacity for the specific exercise.

The program's target is then introduced:

Target reps + target RIR

If the workout asks for 8 reps with 2 remaining, the system estimates what percentage of the athlete's current capacity has historically produced approximately that outcome.

Initially, it may use generalized rep equations. As individual data accumulate, the person's own exercise-specific history should increasingly replace those general assumptions.

Training age can initially influence the expected variance around the prediction, with less experienced athletes receiving wider ranges because their performance is likely to change more quickly.

Exercise experience should modify that further. A ten-year lifter performing an unfamiliar exercise should be treated differently from a ten-year lifter performing a movement they have trained weekly for five years.

The individual's actual history of prediction errors should eventually replace much of this categorical information. If the model repeatedly predicts 200 lbs and the athlete reliably performs best at 190, the algorithm should learn that rather than repeatedly making the same mistake.

Lifestyle information can then widen or narrow the confidence range around the day's recommendation without automatically forcing the load upward or downward.

Finally, the first meaningful working set becomes the strongest available information and allows the system to adjust subsequent sets if necessary.

The result is less like:

Last week's weight + 2.5% = today's weight

and more like:

Based on your recent performance, exercise history, training experience, individual response to previous load increases, reported effort, and today's uncertainty, this is the most likely useful starting load. We will update that estimate based on what you actually do.

That is a fundamentally different way of thinking about the problem.

Where the Coach Still Has an Advantage

Even this more sophisticated model remains imperfect because it is attempting to turn a large number of interacting human variables into measurable inputs.

A coach does something similar, but not entirely through arithmetic.

They see how the athlete walks into the room. They see whether the warm-up looks normal. They notice if someone is protecting a knee without realizing it. They know that an athlete is usually conservative when choosing weight, or that another one becomes reckless when they feel good. They can tell when technique changed enough that last week's numbers are no longer directly comparable to today's. They can recognize when someone needs to be pushed and when the same person needs to be restrained.

More importantly, a coach can ask why, while an algorithm only sees that performance dropped 8%.

A coach might learn that the athlete broke up with their girlfriend, slept three hours, skipped breakfast, has an exam in two hours, and still wants to train hard because training is the only part of their day that currently feels normal.

None of those things automatically determines what weight should go on the bar, but together they change how the coach interprets what happens next.

That doesn't mean coaches are infallible. Coaches guess too. Good coaching isn't the absence of uncertainty. Much of coaching is making increasingly educated decisions from incomplete information and then adjusting as reality gives you better information.

An algorithm can do some of that as well, particularly when it has enough longitudinal data. However, the difference is that we should be careful about presenting mathematical precision as physiological certainty.

A recommendation of 187.5 lbs looks incredibly precise. The human being lifting it may not be.

A Better Approach?

There is value in using technology to remove unnecessary guesswork from training. Most people don't have an experienced coach watching every set, and an adaptive recommendation based on actual training history could be considerably more useful than asking someone to randomly choose a weight.

The system should make the best available prediction, communicate how uncertain that prediction is, and improve its recommendations as the athlete provides more information.

It should become less dependent on generic rules the longer someone uses it. Training age may help establish an initial expectation. Exercise history makes that expectation more specific. Previous performance begins shaping the athlete's individual model. Effort calibration tells us how much confidence to place in subjective feedback. Lifestyle factors modify our uncertainty about the day. Actual performance then tells us whether the prediction survived contact with reality.

That is a much harder problem than adding 2.5% every week. It's also much closer to what coaching actually is.

What the Algorithm Could Actually Look Like

All of this still leaves us needing to put a number on the bar. If I were trying to build the algorithm, I would use everything above to solve three separate problems: estimate the athlete's current capacity, calculate the load most likely to satisfy today's prescription, and then establish how much uncertainty should exist around that prediction.

The starting point would be recent exercise-specific performance. If we know the load, completed reps, and estimated reps in reserve, we can approximate how many total reps the athlete was capable of:

Estimated reps to failure = completed reps + RIR

A set of 185 lbs for 8 reps at approximately 2 RIR therefore behaves roughly like a 10RM. That performance can then be converted into an estimated current capacity using an established rep-max equation. Using the O'Connor equation as an example:

Estimated 1RM = Load × [1 + 0.025 × estimated reps to failure]

For 185 lbs and approximately 10 available reps:

185 × [1 + (0.025 × 10)] = approximately 231 lbs

I wouldn't use that one set as the answer. The model could combine the most recent several exposures, giving greater weight to more recent performances. As an initial structure, the last five useful exposures might receive weights of 40%, 25%, 15%, 12%, and 8%. That produces a rolling estimate of the athlete's current capacity on that specific exercise.

Suppose that estimate is 250 lbs and today's program calls for 8 reps at 2 RIR. We again treat the prescription as approximately 10 available reps and reverse the equation:

Predicted load = estimated current capacity ÷ [1 + 0.025 × target available reps]

In this case:

250 ÷ 1.25 = 200 lbs

So 200 lbs becomes the initial prediction.

The next step is where the model begins becoming genuinely individual. If the equation repeatedly predicts 200 lbs, but this athlete consistently achieves the intended reps and RIR closer to 190, the system should learn from that discrepancy. We could calculate an individual correction factor:

Individual correction = actual successful load ÷ predicted load

If 190 consistently works when 200 was predicted:

190 ÷ 200 = 0.95

Future comparable predictions would then be multiplied by approximately 0.95. This correction should be specific to the exercise and, where enough data exist, the rep range. The more the athlete trains, the less the model depends on generalized rep equations and the more it depends on that athlete's own history.

The final pre-workout calculation would therefore look roughly like:

Personalized predicted load = estimated load from current capacity × individual correction factor

That gives us the number we think the athlete should use. It doesn’t tell us how certain we should be.

Initially, training age and exercise familiarity can establish the expected range around that prediction. A reasonable starting assumption might look something like:

Less than 1 year training: ±10%

1-2 years: ±7.5%

2-4 years: ±5%

4+ years: ±3%

These numbers are starting assumptions rather than established physiological thresholds. An unfamiliar exercise would widen the range further because performance can change rapidly while the athlete learns the movement. Once enough individual data exist, however, the system should stop relying primarily on training age and instead calculate how far this particular person's actual loads normally deviate from its predictions.

Lifestyle information can then widen that range when today's performance is unusually difficult to predict. Poor sleep, unusually high stress, low calorie intake, soreness, travel, or illness would not automatically subtract weight from the recommendation; they would reduce our confidence that the athlete will perform exactly as expected.

The output might therefore look like:

Predicted starting load: 200 lbs

Expected range today: 190-210 lbs

Then the athlete actually lifts.

At that point, today's performance should begin replacing prediction with observation. If the prescription was 200 × 8 at 2 RIR and the athlete performs 200 × 8 at 4 RIR, the starting prediction was conservative. If they perform 200 × 8 at 0 RIR, it was aggressive. If they only complete 6 of the prescribed 8 reps, that objective failure should carry even more weight than the subjective RIR estimate.

A simple first version could adjust the next set approximately 2% for each RIR the prediction missed by, while missed reps could carry a larger adjustment, perhaps around 3% per rep. Those coefficients would need to be tested against real training data rather than treated as established truths.

The complete system therefore becomes fairly simple despite the number of variables behind it:

1. Use recent performance to estimate current exercise-specific capacity.

2. Use today's reps and RIR target to calculate the most likely starting load.

3. Correct that prediction using what the system has learned about the individual.

4. Attach a range reflecting how uncertain the prediction is.

5. Let today's actual performance update the recommendation once the athlete begins training.

The precise coefficients would almost certainly change as real data accumulated. That is partly the point. A genuinely individualized algorithm should become less dependent on the assumptions it was built with as it learns how the individual actually performs.

Understanding the Stretch-Shortening Cycle: Muscle, Tendon, Stiffness, and Explosive Movement

The stretch-shortening cycle is easy to recognize but much harder to explain clearly. We see it whenever an eccentric action immediately precedes a concentric one, such as dipping before a vertical jump, absorbing force before pushing off during a sprint, or rapidly loading the arm before a throw. The eccentric phase improves the performance of the concentric phase that follows, allowing greater force, impulse, velocity, or movement than would be possible from a concentric-only action. The difficulty comes from explaining why this happens, because the stretch-shortening cycle is not driven by one mechanism. The stretch reflex contributes, but so do pre-activation, residual force enhancement, tendon elastic energy storage, and the interaction between muscle and tendon. In fast athletic movements such as sprinting and jumping, the behavior of the muscle-tendon system becomes especially important.

To understand that interaction, it helps to separate the muscle fibers from the tendon even though they function as one connected system. Muscle fibers actively generate force, while the tendon transmits that force to the skeleton and can temporarily store and return mechanical energy. Because the muscle and tendon sit in series, a change in the total length of the muscle-tendon unit does not require the muscle fibers and tendon to change length by the same amount. Depending on the task, more of the movement can occur through the muscle fibers, more can occur through the tendon, or the contribution can be shared between the two. This is where much of the confusion around the stretch-shortening cycle begins, because joint movement is often assumed to reflect what the muscle fibers themselves are doing.

During high-speed movement, the tendon can undergo substantial length change while the muscle fibers remain relatively stable. In sprinting, for example, the plantar-flexor muscles are already active before the foot contacts the ground. Ground contact then creates an external force that drives the ankle toward dorsiflexion and lengthens the calf–Achilles muscle-tendon unit. At the same time, the calf musculature is contracting strongly and resisting that lengthening. The force created between the external loading of the limb and the active muscle causes the Achilles tendon to elongate while the muscle fibers themselves may change length relatively little. When the direction of movement reverses into push-off, the tendon recoils and contributes to rapid plantar flexion while the muscle remains active.

This is the context in which the term quasi-isometric becomes useful. The muscle fibers are not perfectly motionless, but their change in length can be small compared with the speed and range of movement occurring at the joint. That allows the muscle to continue producing substantial force without having to shorten at the same extreme velocity as the limb. The force-velocity relationship tells us that muscle fibers are capable of producing more force when they shorten slowly than when they shorten very quickly. Tendon deformation therefore provides a mechanical advantage: the body can move rapidly while the muscle fibers remain in a slower, more favorable force-producing condition. The tendon is not creating force independently. The muscle remains the active source of force, while the tendon changes how that force is transmitted and how the movement is distributed across the muscle-tendon system.

This is also where tendon stiffness becomes easy to misunderstand. Tendon stiffness describes how resistant a tendon is to elongation under load. A stiffer tendon changes length less for a given increase in force, while a more compliant tendon changes length more. That property can be useful in different ways depending on the movement. In slow, high-force actions such as heavy strength training, large amounts of tendon deformation are not especially useful because the task does not rely heavily on rapid stretch and recoil. A relatively stiff tendon can transmit force efficiently while allowing the muscle fibers to operate under favorable conditions during a slow, force-dominant movement. This helps explain why tendon stiffness can be beneficial in strength-oriented contexts.

Fast stretch-shortening actions create a different mechanical problem because tendon deformation itself becomes useful. When the tendon elongates during the eccentric phase, it can temporarily store elastic energy and return some of that energy as it recoils during the concentric phase. The same tendon movement also allows the muscle fibers to avoid having to perform all of the rapid length change themselves. Both effects can improve the performance of the movement. The tendon therefore needs to deform enough to participate meaningfully in the stretch-recoil process, which is why simply trying to maximize tendon stiffness can become counterproductive when the goal is high-velocity stretch-shortening performance.

The elastic-energy equation helps explain why this becomes more complicated than saying that a stiffer tendon is automatically a better spring. Elastic energy is often represented as E = ½kx², where k represents stiffness and x represents displacement. If two tendons were stretched the same distance, the stiffer tendon could store more energy. The problem is that greater stiffness also makes the tendon harder to stretch that distance in the first place. Because displacement is squared in the equation, the amount the tendon actually moves becomes extremely important. A tendon with a high stiffness value that barely deforms during the brief loading phase of a sprint or jump may not provide the same functional benefit as a tendon that undergoes greater useful displacement under the forces produced by the athlete.

This is why the relationship between eccentric muscle strength and tendon stiffness is more useful than thinking about stiffness by itself. The muscle has to produce enough eccentric force during the loading phase to deform the tendon. Two athletes could have tendons with similar mechanical stiffness but behave very differently if one athlete can generate substantially more eccentric force. The stronger athlete may be able to stretch the tendon farther during the same brief loading period, allowing more energy storage and a greater contribution from tendon recoil. In that situation, the tendon behaves more compliantly during the movement even though its underlying structural stiffness has not necessarily decreased.

This distinction also explains an apparent contradiction in plyometric training. Tendon stiffness can increase during successful plyometric training while stretch-shortening performance improves at the same time. The reason is that eccentric muscle strength may increase faster than tendon stiffness. The tendon becomes slightly stiffer, but the muscle becomes even more capable of deforming it. The athlete therefore produces a more useful stretch-recoil interaction even though the tendon itself has not become structurally softer. The meaningful change occurs in the relationship between the force-producing capacity of the muscle and the resistance of the tendon to deformation.

The misunderstanding in training often comes from reducing the entire system to one tissue property. Tendons are commonly described as springs, which leads to the assumption that a stiffer spring must create a more explosive athlete. That idea overlooks the fact that a spring only stores meaningful energy if it can actually be loaded and deformed. It also ignores the role of the muscle in creating the force that loads the tendon and the role of the tendon in allowing the muscle fibers to remain relatively slow while the joint moves rapidly. As a result, training methods that increase tendon stiffness can be mistakenly treated as direct stretch-shortening-cycle training even when they are primarily improving a different quality.

Isometric training is a good example. Heavy or long-duration isometrics can increase tendon stiffness and may be useful for improving force production or other strength-related adaptations. Brief maximal isometrics can also improve motor-unit recruitment. Those adaptations may still be valuable for an athlete, but they should not automatically be treated as equivalent to improving the high-velocity stretch-shortening cycle. If the goal is to improve the rapid eccentric-to-concentric interaction that occurs during sprinting, jumping, or throwing, the athlete still needs exposure to fast stretch-shortening actions in which the muscle and tendon are forced to coordinate under those conditions.

Plyometric training is useful because it exposes the athlete directly to that interaction. The muscle must produce eccentric force rapidly, the tendon must accept and store part of the resulting load, and the entire system must transition immediately into a high-velocity concentric action. The value of plyometrics therefore goes beyond making a tendon stiffer or more compliant. They train the relationship between eccentric force production, tendon deformation, elastic energy storage and return, muscle-fiber behavior, coordination, and subsequent movement velocity. That is why low-volume, high-quality plyometric work can be highly effective even when the total number of repetitions is small.

It is also useful to separate the stretch-shortening-cycle adaptation from the speed benefit that can occur during the concentric phase of a plyometric. If the specific goal is to improve eccentric muscle strength relative to tendon stiffness, the concentric phase is not what creates that adaptation. The concentric phase can still provide an additional benefit because the preceding stretch-shortening action can allow the athlete to reach a higher movement velocity than they would from a concentric-only start. That higher velocity may then provide a separate speed-related stimulus. In practice, the same exercise can therefore train more than one quality, but those benefits should not be treated as though they come from the same physiological mechanism.

This also helps explain why plyometric volume does not need to be especially high. The desired adaptation is not dependent on accumulating fatigue or completing a large number of contacts. A small number of maximal-quality repetitions can provide the required exposure, while continuing to add repetitions after movement quality begins to fall may increase tissue loading without adding the same quality of stimulus. The goal is therefore to preserve the speed, eccentric force, and coordination of the movement rather than chase volume for its own sake.

The tendon’s contribution to movement also becomes easier to understand once energy storage is separated from energy generation. The tendon is not an active motor. It does not create energy on its own. Energy enters the system through muscular force and external loading, and some of that energy can temporarily be stored as elastic strain within the tendon. When the tendon recoils, that stored energy is returned quickly and contributes to the movement. The muscle is still producing force throughout the process, but the tendon allows some of the mechanical work to be stored and released in a way that would be difficult for muscle fibers to reproduce through rapid shortening alone.

A useful mental model is to think of the muscle as an active motor connected to an elastic spring, with the spring connected to a lever. In a slow strength movement, the motor performs most of the active work and the spring behaves primarily as a force-transmitting structure. During a fast stretch-shortening action, an external force rapidly loads the lever while the motor is already active. The active motor resists the imposed movement, which loads the spring. The spring stretches, the direction of movement reverses, and the spring recoils while the motor continues producing force. Because the spring changes length rapidly, the motor does not have to shorten at the same velocity as the lever. This allows the system to combine high external movement speed with relatively favorable force production from the muscle fibers.

The change in stretch-shortening-cycle performance can also be monitored without sophisticated equipment. One practical option is to compare a concentric-only jump, such as a squat jump, with a jump that includes a rapid eccentric loading phase, such as a drop jump performed for maximum height. The difference between those performances provides a rough indication of the additional contribution being gained from the stretch-shortening cycle. Both jump heights may improve at the same time, but if the drop jump improves faster than the squat jump, the stretch-shortening-cycle contribution has increased. If the relationship stops improving and begins to plateau, that may indicate that the current emphasis has already produced most of the available change.

The practical goal of stretch-shortening-cycle training is therefore to improve the ability of the entire muscle-tendon system to accept rapid eccentric loading and convert it efficiently into subsequent concentric performance. That requires enough eccentric muscle force to load the tendon effectively, tendon properties that allow useful deformation and recoil, favorable muscle-fiber behavior, and coordination that preserves the rapid transition between loading and propulsion. Heavy strength work, isometrics, eccentric training, and plyometrics can all contribute useful adaptations, but they contribute in different ways and should be programmed according to the specific adaptation they are intended to develop.

That does not necessarily mean stretch-shortening-cycle performance should be maximized throughout the entire training year. Greater tendon movement may support high-velocity performance, but a tendon that repeatedly undergoes larger excursions is also being exposed to greater mechanical loading. This creates a programming trade-off between maximizing performance and managing tissue stress. During periods farther from competition, greater emphasis on strength and carefully dosed isometric work can allow the athlete to develop force-producing and recruitment adaptations without continually maximizing tendon deformation. Plyometric emphasis can then increase when high-velocity stretch-shortening performance becomes more important. The basic exercises do not necessarily need to change completely between off-season and in-season training; the amount, frequency, and emphasis can change according to the demands placed on the athlete.

The most useful way to think about tendon stiffness is therefore in relation to the task rather than as an isolated quality to maximize. In slow, high-force actions, relatively greater stiffness can support efficient force transmission. In fast stretch-shortening actions, tendon deformation becomes valuable because it allows elastic energy storage and helps the muscle fibers operate at slower shortening velocities while the body moves rapidly. High-velocity stretch-shortening performance therefore depends less on whether a tendon can simply be described as “stiff” or “compliant” and more on whether the muscle can generate enough eccentric force relative to that stiffness to deform the tendon effectively. Plyometric training can improve this relationship by increasing eccentric muscle strength faster than tendon stiffness, allowing greater useful tendon deformation, elastic energy storage and return, and slower muscle-fiber shortening while the joint itself moves rapidly. The training objective is to develop a muscle-tendon system whose force-producing capacity and tendon behavior are appropriately matched to the velocity demands of the movement, rather than attempting to maximize either stiffness or compliance in isolation.

Proximal-to-Distal Sequencing: How Force and Velocity Should Shape Athletic Training

Proximal-to-distal sequencing describes the way many athletic movements transfer kinetic energy through the body. In actions such as jumping, sprinting, throwing, kicking, or striking, the joints closer to the center of the body begin accelerating and reach peak angular velocity before the joints farther away. In the lower body, the hip reaches its peak angular velocity before the knee, and the knee before the ankle. As energy is transferred down the chain, the more distal segments reach progressively higher movement velocities.

This has an important implication for training because the different joints are operating in different parts of the force–velocity spectrum. The proximal segments move more slowly and are therefore more force-dominant, while the distal segments move progressively faster and become more velocity-dominant. In practical terms, this means the hip can often benefit substantially from heavy strength training because it plays a major role in generating the kinetic energy that is later transferred through the rest of the movement. The knee may require a more balanced mixture of force- and velocity-oriented work, while the ankle and calf, particularly in very fast movements, may benefit more from high-velocity and plyometric work than from large amounts of heavy strength training.

This also changes how exercise selection should be approached. Rather than treating an athlete as globally “force-dominant” or “velocity-dominant,” it can be more useful to examine where each joint sits within the movement sequence. An athlete may already produce plenty of force overall and still benefit from additional heavy hip training because the hip remains the slower, more force-oriented part of the sequence. Conversely, adding excessive strength work or muscle mass farther down the limb may offer little benefit when those distal segments are already operating at very high velocities. In cyclical movements such as sprinting, additional distal mass can also increase the cost of accelerating and decelerating the limb.

The same framework applies to the upper body. Throwing, punching, and striking rely heavily on force and energy generated through the hips and torso before that energy is passed into the arm. When an implement such as a bat, racket, or golf club is added, it effectively extends the sequence and makes more of the body relatively proximal, which can increase the usefulness of heavy strength training in areas that would otherwise sit closer to the velocity end of the chain. This is also why rotational strength deserves more attention: the torso is a highly proximal segment in many sporting actions, yet rotational training is often performed only with light, fast movements rather than stable, high-force exercises.

Fatigue is another important consideration because it can disrupt the sequence itself. When an athlete becomes fatigued, the normal timing between proximal and distal joints can deteriorate, with the joints beginning to accelerate and decelerate more simultaneously rather than passing energy efficiently from one segment to the next. This reduces performance and also changes the coordination pattern being practiced. If the goal is to improve the skill or speed of a movement, accumulating repetitions after the sequence has begun to deteriorate may no longer provide the same training effect.

A practical way to program around this for an athlete trying to improve jump performance would be to organize the session from the speed end back toward the force end while preserving the role each joint plays in the sequence. The athlete might begin with a small number of maximal countermovement jumps or calf hops while completely fresh, using the distal end of the chain at high velocity. That could be followed by a loaded jump variation to provide a more balanced knee-dominant stimulus, then heavier hip-dominant strength work such as hip thrusts and stiff-leg deadlifts to develop force production through the glutes and hamstrings. If the sport also requires resisting force through the torso, as in grappling, a good morning could be added to train hip extension while maintaining torso rigidity. The overall session would stay relatively low in volume so that the fast work remains fast and the heavier work does not create unnecessary fatigue that compromises later exposures.

The most useful takeaway is that athletic exercise selection should reflect where a joint sits in the kinetic sequence. Train the more proximal segments primarily for force, allow the middle of the chain to use a blend of force and velocity work, and bias the most distal segments toward high-velocity training when the sport demands very fast movement. At the same time, keep the athlete fresh enough during skill and speed work to preserve the proximal-to-distal sequence you are actually trying to improve.

Speed: What Actually Makes Someone Faster

Speed is produced by several underlying adaptations, and each requires a different type of stimulus.

Coordination is one of the most specific. To improve the coordination of a fast movement, that movement has to be practiced quickly. Slow practice does not fully develop the motor program required at maximal velocity. This is why sprinting, jumping, throwing, or other high-speed tasks need to be performed at or near the speed the athlete is trying to improve.

The quality of each repetition matters. Once fatigue causes the athlete to slow down, the training stimulus changes. High-velocity work therefore does not need to follow traditional high-volume set-and-rep structures. A small number of maximal-quality repetitions may be more useful than continuing after velocity has fallen.

Motor-unit recruitment is different because it is primarily muscle-specific rather than movement-specific. Heavy strength training and brief maximal isometrics can improve recruitment and contribute to faster performance even though those exercises are not performed at maximal velocity.

Other adaptations, however, require actual fast movement. Increased motor-unit firing rates and increased muscle-fiber shortening velocity do not appear to develop from heavy strength training alone. They require genuinely high movement speeds. Maximal intent with a slowly moving heavy load is therefore not a substitute for fast training.

Strength work can also interfere with speed when poorly managed. Excessive fatigue can promote shifts toward slower muscle-fiber characteristics, and unnecessary hypertrophy can add mass that the athlete then has to accelerate and decelerate. This is especially relevant when muscle is added distally rather than around the hips.

Actionable takeaway: Train speed through three main targets: practice the actual fast movement for coordination, use heavy work or brief isometrics to improve transferable recruitment, and include genuinely maximal-velocity work to improve firing rate and shortening velocity. Stop the work when speed begins to fall.

Maximum Strength: What Actually Makes Someone Stronger

Maximum strength can improve through several different adaptations, and those adaptations do not all transfer equally to other movements.

Coordination is one contributor. Repeated practice can make an exercise more efficient, allowing someone to lift more weight without necessarily increasing the force-producing capacity of the muscle itself. This type of improvement is highly specific to the movement and even to the load being used. Becoming more skilled at a heavy squat is therefore very useful for a powerlifter, but that coordination does not automatically transfer to sprinting or jumping.

Motor-unit recruitment is much more transferable. Improving the nervous system’s ability to activate more of a muscle increases the force that muscle can produce across different movements that use it. High recruitment can be trained through heavy lifting, brief maximal isometrics, and other high-effort contractions, provided fatigue and discomfort are kept low enough to allow high central motor command.

Hypertrophy contributes by increasing the amount of contractile tissue available to produce force. For athletes, however, the location of that muscle matters. Adding muscle where it contributes to the demands of the sport can be useful, while adding mass indiscriminately may not be.

Other adaptations can also contribute. Reduced antagonist co-activation can increase net force around a joint. Lateral force transmission can improve how efficiently muscle-fiber force reaches the tendon. Increased tendon stiffness can improve force production in slow, high-force actions by changing how quickly the muscle fibers themselves have to shorten.

The important point is that the same improvement in a strength test can come from very different mechanisms.

Actionable takeaway: Do not treat “getting stronger” as one process. Identify whether the goal is movement skill, greater muscle activation, more muscle, or another force-producing adaptation, and program specifically for that need.

Strength, Speed, and Power: Understanding the Relationship

Strength, speed, and power are performance outcomes. They are things we measure, not single adaptations that the body develops directly.

An increase in strength might come from greater muscle size, improved motor-unit recruitment, better coordination, changes in tendon behavior, or other adaptations. Speed works the same way. A faster sprint, jump, or throw tells us performance improved, but the useful question is what changed underneath that performance.

Power sits one level higher because it is the product of force and velocity. That means transferable power is improved by increasing force capability, velocity capability, or both. This is important because the load that produces the highest measured power in a gym exercise is not necessarily the load that best develops either quality. Heavy loading is better suited to high-force adaptations, while very light or unloaded movements performed at genuinely high speeds are better suited to high-velocity adaptations.

Strength also does not automatically become speed. The force a muscle can produce decreases as shortening velocity increases, so an athlete can be very strong at slow speeds while still lacking the ability to produce force at high velocities. Likewise, intending to move a heavy load quickly is not the same as actually moving quickly. Speed-specific adaptations require exposure to genuinely fast movement.

Fatigue therefore matters because it can reduce the athlete’s ability to reach the velocities required for speed development. High-velocity work should be performed while the athlete is fresh enough to actually express speed.

Actionable takeaway: Decide whether you, or the athlete, primarily needs more force, more speed, or both. Train those qualities directly and protect the quality of the stimulus by limiting unnecessary fatigue.