MEASURING IS KNOWING
Is it?
We live in an age of measurement.
We measure almost everything.
Performance.
Health.
Risk.
Behavior.
Movement.
And the more precisely we can measure something, the more confident we tend to become that we understand it.
It seems almost self-evident:
Measuring is knowing.
But is it?
That question has been on my mind for some time.
And it became even more interesting after something I wrote recently about baseball and the assumption that the feet should always be the starting point for movement.
The question was simple:
Who said the feet have to be the anchor?
The deeper question turned out to be much bigger.
Because before we can decide where to start, perhaps we first need to understand what we are actually looking at.
And that brings us to a problem that extends far beyond baseball.
When Measurement Becomes Knowledge
John Ioannidis has spent much of his career examining the reliability of scientific research.
His work has challenged a comforting assumption:
That if something has been scientifically studied, measured, and published, we can automatically treat the finding as reliable knowledge.
But research findings exist within a system.
Sample size matters.
Study design matters.
Statistical power matters.
Bias matters.
Multiple comparisons matter.
Replication matters.
And interpretation matters.
A finding can therefore be real as a statistical finding without necessarily representing the broader truth we think it represents.
This distinction is easy to overlook.
Because numbers feel different from opinions.
A number looks objective.
A graph looks objective.
A statistically significant result looks objective.
And the more precise the number becomes, the stronger that feeling can become.
But there is an important difference between:
measuring something
and
understanding what the measurement means.
Then It Gets Even Deeper
Mattias Desmet approaches the problem from a different direction.
His argument is not simply that scientific studies can be poorly designed or incorrectly interpreted.
It is that we can develop an excessive confidence in what quantification itself represents.
We take something complex and living.
We reduce part of it to measurable variables.
And then, gradually, the variables begin to stand in for the thing itself.
The representation becomes more important than the reality it represents.
This is where his concept of pseudo-objectivity becomes interesting.
The number may be perfectly accurate.
The measurement may be perfectly reproducible.
But the phenomenon being measured may still be far more complex than the number suggests.
The problem is therefore not necessarily bad measurement.
It can be good measurement being asked to answer a question it was never capable of answering.
And that distinction is enormous.
Baseball Is a Perfect Example
Baseball has embraced measurement like few other sports.
And rightly so.
The modern game can tell us things about a pitch that previous generations could never have known.
Velocity.
Spin rate.
Movement.
Release height.
Extension.
Approach angle.
Arm slot.
Kinematic sequencing.
Joint angles.
Angular velocities.
Forces.
Torques.
The same is true for hitting.
Bat speed.
Exit velocity.
Launch angle.
Attack angle.
Time to contact.
And much more.
This is remarkable progress.
It would be foolish to argue otherwise.
Data has transformed baseball.
It has helped organizations evaluate players.
It has helped coaches identify patterns.
It has helped players improve.
It has created entirely new areas of expertise.
The answer is not to measure less.
But perhaps we should become more careful about what we believe our measurements tell us.
Because baseball may be approaching a point where the next challenge is no longer collecting more information.
It is understanding the information we already have.
A Number Can Be Correct
Consider a pitcher.
We measure his release point.
The measurement is accurate.
We measure his arm slot.
Accurate.
We measure his kinematic sequence.
Accurate.
We measure the forces involved.
Accurate.
We measure the resulting pitch.
Accurate.
So now we know.
Right?
Not necessarily.
We know what happened.
But do we know why it happened?
And that distinction becomes critical when the subject is a human being.
Because the same measurable movement can emerge from very different organizational solutions.
Two pitchers can produce similar outputs while organizing their bodies differently.
Two players can receive exactly the same instruction and respond completely differently.
Two athletes can eventually arrive at the same position through completely different paths.
The measurement captures the outcome.
It does not automatically explain the organization that produced it.
What If the Numbers Are Right?
This may be the most important question.
What if the numbers are accurate — but our interpretation is wrong?
That is very different from saying:
“The data is wrong.”
The data may be excellent.
The technology may be excellent.
The measurement may be excellent.
And yet the conclusion can still be incomplete.
Because there is a step between measurement and understanding.
Measurement → Interpretation → Understanding
The first gives us information.
The second gives that information meaning.
The third requires context.
And when we are dealing with human movement, that context includes the individual producing the movement.
Seeing Movement Is Not the Same as Understanding Movement
This is where the question of the feet becomes relevant again.
Suppose a coach begins with the feet.
There may be very good reasons to do that.
The player reacts.
The coach observes the reaction.
The coach adjusts.
Then another player receives the same starting point and responds differently.
The coach adjusts again.
This is good coaching.
It is adaptive.
It is observational.
It recognizes that athletes are individuals.
But there is another question we can ask:
Could we understand something about the athlete’s natural organization before prescribing the movement?
That is a different starting point.
Not:
What movement should we teach?
But:
How is this individual naturally organized to learn and produce movement?
That distinction may seem subtle.
It is not.
The Human Being Comes Before the Metric
There is a temptation in modern performance environments to begin with the measurable output.
The pitch.
The swing.
The force.
The position.
The number.
Then we work backward to determine how to change it.
But perhaps we sometimes reverse the order.
Perhaps the better sequence is:
Human → Organization → Movement → Outcome
rather than:
Outcome → Movement → Human
The first approach begins with the individual.
The second can easily turn the individual into the explanation for a number.
That does not make one scientific and the other unscientific.
It simply recognizes that different layers of information answer different questions.
Biomechanics can describe movement extraordinarily well.
Analytics can describe outcomes extraordinarily well.
Medical science can describe physiological conditions extraordinarily well.
But none of those measurements, by themselves, necessarily tells us how a particular individual naturally organizes movement.
That is another layer.
And This Matters More Than Performance
Because performance is not the only thing at stake.
By the beginning of September 2026, approximately $1.2 billion in MLB salaries had already been paid this year to players on the injured list.
That is 2026 alone.
And Major League Baseball represents only the highest and most visible level of professional baseball.
What about the Minor Leagues?
How many players are going through surgery there?
How many development trajectories are interrupted?
How many pitchers never reach the Major Leagues because their development is derailed first?
The number is difficult to ignore.
But the point of raising it here is not to suggest that measurement causes injury.
It doesn’t.
Nor is it to suggest that biomechanics, workload management, medical intervention or performance science are the problem.
They are not.
The question is more uncomfortable:
If our ability to measure human performance has never been greater, are we equally good at understanding what those measurements mean for the individual athlete?
More Information Can Create an Illusion of Understanding
This is perhaps the paradox.
The more information we collect, the more knowledgeable we can feel.
And sometimes that is justified.
But sometimes information simply increases our confidence without increasing our understanding by the same amount.
A pitcher has a measurable arm slot.
That does not mean the arm slot is the cause of his performance.
A pitcher has a measurable release point.
That does not mean the release point is something that should be deliberately manipulated.
A player produces a particular kinematic sequence.
That does not mean the sequence should be imposed on another player.
A metric correlates with performance.
That does not automatically make the metric the mechanism responsible for that performance.
And a successful intervention does not automatically tell us why it worked.
This is where the distinction between description and explanation becomes so important.
Data Tells Us What
This does not diminish data.
It gives data its proper place.
Data can tell us:
What happened.
It can tell us:
How much.
It can tell us:
How often.
It can reveal patterns that the human eye cannot reliably detect.
That is extraordinarily valuable.
But the next question is:
Why?
And that question requires a different kind of understanding.
In human movement, one possible organizing layer is the individual’s Motor Signature — the way that person naturally organizes, perceives and learns movement.
That does not replace the measurement.
It changes how we interpret it.
The question is no longer simply:
“What does this pitcher look like?”
It becomes:
“What is this pitcher organizing — and why?”
This Is Not Data Versus the Human
That would be the wrong conclusion.
The future should not be:
Analytics versus intuition.
Biomechanics versus coaching.
Science versus the athlete.
That is a false choice.
The opportunity is to bring the layers together.
To use measurement for what it does best.
To use science for what it does best.
To use observation for what it does best.
And to keep the individual human being at the center of the interpretation.
Because the more powerful our measurement systems become, the more important it becomes to understand their boundaries.
We Don’t Need Less Measurement
We need better questions.
Instead of asking only:
What does the data say?
we should also ask:
What does the data actually represent?
Instead of:
What movement should we change?
perhaps:
What is organizing the movement we see?
Instead of:
Why doesn’t this athlete move like the model?
perhaps:
Why should this athlete move like the model in the first place?
And instead of assuming that precision gives us certainty:
What are we still not seeing?
The Next Frontier May Not Be Another Metric
Baseball has become extraordinarily good at collecting information.
The next competitive advantage may come from something more difficult:
knowing how to interpret it.
Not every number is an explanation.
Not every correlation is a cause.
Not every measurable position is an instruction.
Not every successful movement solution is transferable.
And not every difference is a problem that needs to be corrected.
The challenge is not to move away from science.
It is to become more sophisticated about what science, technology and measurement can actually tell us.
Because there is a fundamental difference between knowing the measurement and knowing the person behind the measurement.
Measuring Is Knowing
Maybe the phrase was never completely wrong.
Maybe it was simply incomplete.
Measuring gives us information.
Information gives us possibilities.
Understanding gives us context.
And when the subject is a human being, context matters.
So perhaps the better question is not:
“Can we measure it?”
Of course we can.
The better question is:
“What does the measurement allow us to know — and what does it not?”
That distinction may become increasingly important as baseball continues to collect more and more data about the people who play it.
Because the future of performance may not belong to the organization with the most measurements.
It may belong to the organization that understands what those measurements actually mean.
And perhaps that is the real lesson:
Measuring is knowing.
Is it?
