Movement Analysis • March 15, 2026
Your fastest student might be your worst runner.
Teacher talk • May 19, 2026
By Timothy Starkey
A few years ago, I had the pleasure of teaching a very athletic Year 6 cohort. There were multiple A tier sprinters (male & female) who had the potential to pursue athletics as a career post-schooling. I watched them grow over the years & it was exciting to witness their motor skills & confidence develop. It was one of those cohorts you don't forget. But there was 1 student in particular that intrigued me. Particularly, her sprinting ability. She was clearly the fastest sprinter in the school, winning many accolades and dominating at interschool competitions. But her running technique was… peculiar. Her arms would flail above her head, she had a very upright posture (almost as if she was leaning backwards) and her knee lift was unusually high. She was very tall, granted, with long legs which was what contributed to her long stride length.
I found myself genuinely torn. If I were to assess her running technique against biomechanical criteria, I'd have a hard time giving her anything better than a C. Her mechanics were, by any measurable standards, inefficient. And yet she was crossing the finish line miles ahead of children who looked textbook by comparison. It unravelled something in my thinking that changed my approach to assessment forever.
Are we assessing the outcome?
Or are we assessing the quality of movement?
When she and the other students found out her running assessment score was a C, they were shocked to say the least.
The same tension exists across every skill domain we teach. A student might launch a football with a drop punt that carries 40 metres, but their grip was wrong, guide and release point inconsistent, their hip rotation was absent, and the only reason it went that far was raw explosive force and a powerful tail wind. Another student demonstrates a technically beautiful punt that travels 25 metres. The same could be said for an overarm throw, cricket bowl, badminton smash, long jump.. you name it. But who gets the better mark? And here's the harder question: should the outcome even factor into the grade at all?
It’s easy for us PE teachers to fall into the trap of assessing outcomes. Outcomes are tangible, quick to record, and satisfying in a way that ticking a rubric box rarely is. There's no ambiguity in a stopwatch. But a stopwatch only tells you what happened. It says nothing about the how, and in a school context, the how is almost everything.
What sprint times don’t tell you.
Consider two hypothetical students. Let's call them Mia and Bella. They both run a 50-metre sprint in roughly the same time. From the sideline, watching them cross the finish line simultaneously, you'd probably assess them the same way. Fast kids, moving well, tick tick.
But here's what a biomechanical analysis of their running actually reveals.
Mia has an average forward torso lean of 7 degrees — right in the range that research consistently associates with efficient energy transfer and hip drive. Her elbow angle during the swing phase sits close to 90 degrees, her ground contact time is brief, and her stride symmetry is high. Her mechanics are clean. She is, in the truest sense, an efficient runner.
Bella runs with her trunk pitched forward at more than 12 degrees, leaning from the waist rather than driving from the ankle. Her arm swing crosses the midline of her body with each stride. Her left and right stride patterns show measurable asymmetry. She is working considerably harder than Mia to produce the same output — and she is building compensatory movement patterns that will only become more entrenched as she gets bigger and faster.
This distinction is invisible to a stopwatch but unmissable to biomechanical analysis. This is the problem I kept running into as a PE teacher. And it's the problem that eventually inspired the idea behind Huddl.
The research literature makes this distinction clearly. It separates what are called process-oriented assessments — which evaluate the quality of movement patterns from product-oriented assessments, which measure the outcome or result of a movement (Logan et al., 2017; Hulteen et al., 2020).
These two approaches don't always agree with each other.
Logan and colleagues (2017) studied children across three age groups (4–5, 7–8, and 10–11 years) and found that correlations between process and product assessments of the same fundamental movement skills ranged widely, from weak to strong — depending on the specific skill and age of the child. A child can achieve a competitive product score (a fast sprint time, a long jump distance) while their underlying movement pattern is still immature or inefficient.
That's Student B. Running fast despite their technique, not because of it.
What the biomechanics actually show
Let me get specific, because this is where it gets genuinely interesting.
When researchers at Loughborough University studied 97 endurance runners across a range of competitive standards, they found that running technique (assessed through three-dimensional full-body kinematics) was a significant independent predictor of both running economy and performance (Folland et al., 2017). Not just fitness or leg strength. The technique itself.
The kinematics they measured included posture, vertical oscillation, braking forces, stride parameters, and lower limb angles. When these were combined, they explained a meaningful and statistically significant proportion of the variance in running economy — the energy cost of running at a given speed.
What does that mean in plain language? Two runners at the same speed can be burning very different amounts of energy, depending on how they're running. The one with better mechanics is doing less work. They have more left in the tank. And over time, they have a higher ceiling.
At Huddl, the biomechanical metrics we assess during running are directly grounded in this kind of research. We look at:
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Stride length ratio — stride length normalised to body height
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Cadence — step rate over time
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Torso lean — the degree of forward trunk inclination
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Elbow angle — the position of the arm during the swing phase
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Ground contact time — how long the foot is in contact with the ground per stride
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Flight time — time spent airborne between contacts
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Stride symmetry — the consistency between left and right stride patterns
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Torso lean: a case study in invisible errors
Let me use torso lean to illustrate why this matters, because it's one of the most common errors I see in school-aged runners — and one of the least corrected.
A small amount of forward lean during running is both natural and beneficial. Research by Williams and Cavanagh found that the most economical runners used a mean trunk flexion angle of approximately 5.9 degrees. But there's a crucial distinction in where that lean comes from. Leaning from the ankle — a whole-body forward tilt — is biomechanically efficient. Leaning from the waist (trunk flexion) is not.
A 2024 study published in PLOS ONE tested this directly, having 16 runners run at the same speed across three different postural lean conditions. Large forward trunk lean significantly impaired running economy — the body had to work harder, with increased muscle activation, to maintain the same output (PLOS ONE, 2024).
![Side-view skeleton overlay of a student running, showing trunk angle measurement with degree annotation]
In a class of 25 kids doing a sprint assessment, how many are running with excessive trunk flexion? In my experience, quite a few. And because they're getting across the line in a reasonable time, it doesn't register as a problem.
But it is a problem. It's a compensatory pattern that limits propulsion efficiency, increases injury risk, and if left unaddressed, becomes more ingrained as a student gets older and faster.
Why development makes this even more important
Here's something that surprised me when I first started digging into the research: children's running mechanics are not a scaled-down version of adult mechanics. They're genuinely different, and they develop along predictable trajectories.
Biomechanical studies have shown that gait parameters like stride length, cadence, and movement symmetry continue to be refined well into adolescence, with a more adult-like pattern generally emerging around ages 7–8 for the most fundamental features (Scientific Reports, 2022). But refinement doesn't happen automatically. It happens in response to feedback, instruction, and practice.
Research on motor development in early childhood has found that gait parameters such as stride length, cadence, and gait symmetry are progressively refined through childhood, with a mature gait pattern typically established by around age 7. What this means in practice is that the movement errors we see in a Year 3 student are, in many cases, developmentally predictable. They are expected deviations from mature movement — and that's exactly when intervention is most effective.
If we're only looking at outcomes, we're missing the window.
The process vs. product gap in schools
Biomechanics is one argument, but the assessment design is potentially more important.
Research published in Scientific Reports (2025) notes that process-oriented assessments are particularly valuable for providing qualitative insights into children's motor competence that can be used to design and plan targeted interventions.
In other words: if you want to actually help a kid move better, you need to know how they're moving, not just how far or fast they went.
Product-oriented measures focus on the outcomes of skill execution such as throw velocity or jump distance, whereas process-oriented measures evaluate the quality of movement patterns during skill execution, including specific biomechanical components of a skill. Current research indicates that correlations between process and product assessments vary from weak to strong, depending on the specific skill and the age of children being assessed.
That variance is the key point. A strong outcome score does not guarantee a mature, efficient movement pattern. And a developing movement pattern doesn't always produce a competitive outcome score… particularly in younger children.
A child who jumps 1.2 metres in the standing long jump might be doing so with immature arm mechanics and a poorly timed knee drive. Another child might jump 1.0 metres with technically superior form that, with growth and development, will produce far greater distances in future years. Which child is a better mover? Which child has a higher ceiling? The product score doesn't tell you.
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What we should be grading in PE
I'm not arguing that outcomes are irrelevant. Speed matters in athletics. Distance matters in field events. But in a PE context (particularly with primary and lower secondary students), our job isn't to rank athletic performance. It's to develop capable, confident movers.
That means the question we're asking at assessment time matters enormously.
"How far did they jump?" is a different question from "Are they using the movement components that lead to a greater jumping ability?
"How fast did they run?" is a different question from "Are they building the biomechanical foundations that will allow them to run efficiently for the rest of their life?"
The stopwatch answers the first question. Biomechanical analysis answers the second.
This is the philosophy at the heart of Huddl. When a student runs and we analyse the footage, we're not producing a time. We're producing a grade based on the quality of the movement — assessed against age-appropriate biomechanical benchmarks, because what we expect from a Foundation student is different from what we expect from a Year 9.
Same finish line. Different story.
Back to Mia and Jordan.
If I'm assessing them on time, I give them both a similar mark. I move on.
But if I've got a biomechanical breakdown of their running, I know that Mia is moving efficiently and building a strong foundation. I know that Jordan is compensating — and I know specifically how. I can intervene. I can teach. I can do the thing I'm actually here to do.
That's the shift I think PE needs to make — not away from outcomes entirely, but toward a richer picture of what movement quality actually looks like, grounded in the same kinematic data that elite coaches and sports scientists have been using for years.
Our students deserve the same lens.
Huddl.
Huddl uses biomechanical analysis to grade student movement quality in PE — giving teachers clear, evidence-based feedback on how their students are moving, not just what they're producing. If you're interested in trialling Huddl at your school, visit huddlapp.com.au.
References
Folland, J. P., Allen, S. J., Black, M. I., Handsaker, J. C., & Forrester, S. E. (2017). Running technique is an important component of running economy and performance. Medicine & Science in Sports & Exercise, 49(7), 1412–1423.
Hulteen, R. M., True, L., & Pfeiffer, K. A. (2020). Differences in associations of product- and process-oriented motor competence assessments with physical activity in children. Journal of Sports Sciences, 38(4), 375–382.
Li, M. (2024). Research on the application of biomechanics analysis in optimizing physical education movement techniques. Molecular & Cellular Biomechanics, 21(3), 496.
Logan, S. W., Barnett, L. M., Goodway, J. D., & Stodden, D. F. (2017). Comparison of performance on process- and product-oriented assessments of fundamental motor skills across childhood. Journal of Sports Sciences, 35(7), 634–641.
Tomlinson, R., Tully, M., & Corlett, E. (2024). The effect of forward postural lean on running economy, kinematics, and muscle activation. PLOS ONE, 19(5), e0302249.
Scientific Reports (2022). Three-dimensional gait analysis of lower extremity gait parameters in Japanese children aged 6 to 12 years. Nature/Scientific Reports.
Study on the motor development and biomechanical characteristics of children aged 3–5 years. PMC, 2025.