What a fast shoe is worth

Every published measurement of the same effect, and why they do not agree.

A plated racing shoe is supposed to be worth about 4%. That number came from one study, of one prototype, mass-matched against a racing flat1, on a treadmill, at 16 km per hour — and it was measured as metabolic cost, not as time. Every one of those qualifiers moves it. Collect the other published measurements of the same thing and they run from 1.1% to 4.2%. Look inside the studies that report individual runners rather than group means and the range is 22.7 percentage points wide, crossing zero in 3 of the 4 studies that publish it.

This page is that collection. It is a table of 21 published rows, every one with a named study and a link to it, and some arithmetic over them: what the spread is, how much of it is between studies and how much inside one, and what happens when the economy figures are converted into race time. It does not tell you which shoe to buy, and the reason it cannot is the finding.

What it finds

13 controlled laboratory comparisons of an advanced-footwear shoe against a control shoe have been published with a percentage attached. They range from 1.1% to 4.2%, a factor of 3.8, with a median of 2.8%.

Thirteen rows, one per published comparison, each a dot at the measured change in the energy cost of running, ordered from largest to smallest. Six rows carry a horizontal bar for the standard deviation the paper published; the other seven are a dot alone.

Every comparison in the table, ordered by effect size. A bar is the standard deviation the paper published; 7 of the 13 published no dispersion at all and are drawn as a dot alone, because an interval nobody reported is not one this page will draw.

Reading down that figure sorts the comparisons roughly by how artificial the test was. Prototypes on treadmills at elite pace sit at the top; the only measurement made outdoors, an advanced trail shoe at2 11.5 km per hour on real ground, sits at the bottom at 1.1% with a standard deviation equal to its mean. That ordering is suggestive and not demonstrated: shoe generation, control shoe, speed, terrain and laboratory all vary together across these rows and nothing here isolates one of them. It is a pattern across studies, not a trend, and it is not drawn as one.

The more useful comparison is between the spread across studies and the spread inside one. Where a study reports what happened to each runner instead of the group mean, the ranges are wider than the entire between-study spread, and most of them include runners the shoe made slower.

One axis of per cent change in running economy. Above a dividing line, the thirteen study means sit as a tight cluster of dots between one and four per cent, all right of zero. Below it, four horizontal bars show the individual runner ranges published by two studies; three of the four cross zero and the widest spans from minus eleven to plus eleven per cent.

The 13 study means above the rule, the 4 published individual ranges below it, on one axis. Colour names the study: the two Knopp rows are one paper and the two Barnes and Kilding rows another3, and Barnes and Kilding's own means carry the same colour above, so one study can be followed from its tight mean to its wide individuals. The widest range, 22.7 points, is in world-class runners4 — the group the shoes were built for.

The means huddle. The individuals sprawl. Nothing in the literature predicts which runner falls where in those ranges, and that is the honest answer to what a fast shoe is worth to any particular person: the population mean is 2.8% and the individual outcome is not the population mean.

What that is worth in a race

Almost every number above is a change in the metabolic or energy cost of running, and no runner is timed in joules. The exchange rate between the two is the subject of Economy is not time, which works it out and reconciles it against the cost-of-running curve: about 0.70, so roughly seven tenths of a metabolic saving reaches the clock. That page owns the derivation and the reason the rate is not one; this one only spends it.

Applying that rate to anything other than added shoe mass is an assumed step and is marked as one wherever it appears. It has never been measured for advanced footwear, and the two interventions need not transfer alike. The rate also has an interval of its own, 0.39 to 1.18, which the figure below does not draw. With those caveats, the laboratory range of 1.1% to 4.2% becomes 0.8% to 3.0% of race time.

Three horizontal intervals on an axis of per cent faster in race time. The top one, drawn as a dashed outline and labelled assumed, is the laboratory range converted through the transfer coefficient. Below it two solid bars show the intervals observed for men and for women in actual marathons. All three overlap.

The dashed interval is derived through the transfer coefficient and is drawn differently for that reason alone. The two solid ones are what was observed in 22 marathons by reading shoes off public race photographs5. They overlap, which is consistency and not proof: the race estimates are observational, runners chose their own shoes, and the fastest adopted them first.

This is the one place the page can show agreement rather than disagreement, and it is worth being precise about how weak the agreement is. Three intervals overlapping does not confirm the coefficient; it fails to contradict it.

The mass rule, and the trial that contradicts it

The oldest number in this field6 is that a shoe costs about 1% of metabolic rate per 100 g per shoe, and it is the number the 0.70 ratio above depends on. It was measured at7 1.11%, with a 95% confidence interval of 0.88 to 1.35 — the only confidence interval published anywhere in this table.

A later trial performed the same manipulation8 and measured 7.40% and 10.21%.

Three rows on a linear axis of per cent worsening in the energy cost of running per hundred grams added per shoe. The first row sits near one per cent with a narrow confidence interval; the other two, from a later study, sit at seven and ten per cent with no interval published.

The founding constant and the trial that contradicts it, on a linear axis. A log axis would compress a gap of 7 to 9 times into something that looks like ordinary disagreement, and the size of the gap is why the figure exists. Both are peer-reviewed and neither is retracted.

No one has reconciled them. That matters beyond the mass question, because the 1% figure is load-bearing: it is the basis of the transfer coefficient, and it is the reason the industry treats shoe weight as a design constraint9 at all.

What the mechanism is not

The popular explanation is the carbon plate. The evidence does not support that as a standalone claim. A meta-analysis of 48 studies covering 878 runners found that neither longitudinal bending stiffness alone nor midsole energy return alone significantly affected oxygen consumption — only their interaction did10. A separate meta-analysis found that a curved plate improved economy while a flat plate did not11, and a narrative review puts the bending-stiffness literature at about 3% deterioration to about 3% improvement12.

Two independent meta-analyses, one over 17 trials and one over 48, both arrive at a standardised mean difference13 of -0.44 for oxygen consumption. That agreement is real. It is also not a percentage, which is why no meta-analysis appears as a row in the figures above: a standardised mean difference and a per cent change do not share an axis, and putting them on one would be the conflation this page argues against.

Method

The laboratory comparisons come from Hoogkamer and colleagues in 2018, Hunter and colleagues in 2019, Barnes and Kilding in 2019, Whiting and colleagues in 2021, Joubert and colleagues in 2024, and Joubert and Sanders in 2026. The added-mass rows come from Hoogkamer and colleagues in 2016 and from Rodrigo-Carranza and colleagues in 2020, the individual ranges from Knopp and colleagues in 2023, and the race estimates from Guinness and colleagues in 2020.

The data is a declared table of 21 rows in data/studies.py, each read out of the paper it comes from and carrying the sample, the test speed, the setting, a note, and a DOI. There is no download step, because no public dataset reports these numbers. The inclusion rule for the laboratory comparisons is every controlled comparison of an advanced-footwear shoe against a control shoe reporting running economy or metabolic cost as a percentage that the Stage 1 literature search found. That is a collected table and not a systematic review, and where a study reported two arms both are in — dropping the smaller of a study's two spikes is how a collection starts flattering itself.

Everything else is arithmetic over that table: the extremes, the median, the ratio between them, the width of each individual range and whether it crosses zero, the counts of what was and was not published, and the transfer coefficient, which is one published row divided by another and is recomputed here rather than read from the sibling project's payload, so neither page can break the other. Nothing is fitted, smoothed or regressed. A standard deviation and a confidence interval are held in separate fields and neither is ever derived from the other.

The sign convention is that a positive number means the energy cost of running fell. Advanced-footwear rows are therefore positive and added-mass rows negative, and the build refuses to run if any row breaks that.

Limits

Nothing here predicts an individual. The published ranges say the individual response varies by more than the mean effect; no study offers a variable that predicts where a given runner lands, and this page does not offer one either.

The samples are narrow. Of 21 rows, 6 include women at all, and 1 was measured outside a laboratory. 3 rows measure time; the rest measure oxygen or energy.

There is no shoe database. No free, comprehensive source gives shoe mass, stack height, foam type or plate geometry by model. World Athletics publishes a list of approved shoes14, and it is an eligibility list — brand, model, and which events a shoe may be worn in, with no specifications. So this page cannot compare shoes, only studies.

The table is a collection, not a review. It was assembled by one search rather than by a registered protocol, and a systematic review would apply quality weighting this does not.

The code

Three Python files and the table. data/studies.py declares every published row with its source and DOI; build_shoes.py computes the spreads, the counts and the transfer coefficient and writes the payload; fig_shoes.py draws the four figures from it. The page's numbers are written from the payload by update_page.py, and test_shoes.py checks the failure modes this project is exposed to — chiefly that no interval is drawn that nobody published.

Download source · 55 KB All code

Sources

Numbered markers in the text above point here. Emission factors, cost ranges and lifespan figures are representative values from these sources, not measurements made for this project.

  1. Hoogkamer W, Kipp S, Frank JH, Farina EM, Luo G, Kram R (2018). A Comparison of the Energetic Cost of Running in Marathon Racing Shoes. Sports Med 48(4):1009-1019 (correction, 48(6):1521-1522). doi:10.1007/s40279-017-0811-2The 4% figure: a prototype against two established racing shoes, mass matched, at three elite paces.
  2. Joubert DP, Sanders J (2026). Effects of Advanced Footwear Technology in Trail Running Shoes on Running Economy. J Strength Cond Res, ahead of print. doi:10.1519/JSC.0000000000005553The only comparison measured outdoors, and the smallest.
  3. Barnes KR, Kilding AE (2019). A Randomized Crossover Study Investigating the Running Economy of Highly-Trained Male and Female Distance Runners in Marathon Racing Shoes versus Track Spikes. Sports Med 49(2):331-342. doi:10.1007/s40279-018-1012-3Per-athlete ranges in 24 highly trained runners of both sexes, including a runner the shoe made less economical.
  4. Knopp M, Muñiz-Pardos B, Wackerhage H, Schönfelder M, Guppy F, Pitsiladis Y, Ruiz D (2023). Variability in Running Economy of Kenyan World-Class and European Amateur Male Runners with Advanced Footwear Running Technology. Sports Med 53(6):1255-1271. doi:10.1007/s40279-023-01816-1Individual running-economy responses in world-class and amateur runners, and the range they span.
  5. Guinness J, Bhattacharya D, Chen J, Chen M, Loh A (2020). An Observational Study of the Effect of Nike Vaporfly Shoes on Marathon Performance. arXiv:2002.06105. doi:10.48550/arXiv.2002.06105Observed marathon-time effects for men and for women, from public race results and photographs.
  6. Frederick EC, Daniels JT, Hayes JW, “The effect of shoe weight on the aerobic demands of running”, in Current Topics in Sports Medicine, Urban & Schwarzenberg, 1984, 616-625.The origin of the roughly 1% per 100 g rule, measured directly by Hoogkamer and colleagues in 2016.
  7. Hoogkamer W, Kipp S, Spiering BA, Kram R (2016). Altered Running Economy Directly Translates to Altered Distance-Running Performance. Med Sci Sports Exerc 48(11):2175-2180. doi:10.1249/MSS.0000000000001012The per-100 g mass effect on metabolic rate, with the only confidence interval in the table, and on 3000 m time.
  8. Rodrigo-Carranza V, González-Mohíno F, Santos-Concejero J, González-Ravé JM (2020). Influence of Shoe Mass on Performance and Running Economy in Trained Runners. Front Physiol 11:573660. doi:10.3389/fphys.2020.573660The added-mass trial that disagrees with the per-100 g rule by six to nine times.
  9. Fuller JT, Bellenger CR, Thewlis D, Tsiros MD, Buckley JD (2015). The effect of footwear on running performance and running economy in distance runners. Sports Med 45(3):411-422. doi:10.1007/s40279-014-0283-6The association between shoe mass and metabolic cost across the earlier literature.
  10. Stephen CHN, Kelly LA, Schuster RW, Diamond LE (2025). The effects of running shoe longitudinal bending stiffness and midsole energy return on oxygen consumption and ankle mechanics and energetics. J Sport Health Sci 14:101069. doi:10.1016/j.jshs.2025.101069Bending stiffness alone and midsole energy return alone are each non-significant; the interaction is not.
  11. Rodrigo-Carranza V, González-Mohíno F, Santos-Concejero J, González-Ravé JM (2022). The effects of footwear midsole longitudinal bending stiffness on running economy and ground contact biomechanics. Eur J Sport Sci 22(10):1508-1521. doi:10.1080/17461391.2021.1955014Plate geometry, rather than the presence of carbon, is what the data separates.
  12. Ortega JA, Healey LA, Swinnen W, Hoogkamer W (2021). Energetics and Biomechanics of Running Footwear with Increased Longitudinal Bending Stiffness: A Narrative Review. Sports Med 51(5):873-894. doi:10.1007/s40279-020-01406-5The range of published bending-stiffness effects, in both directions.
  13. Xiao Y, Hu X, Tian D, Qiu A (2025). Effects of Advanced Footwear Technology on Running Economy and Endurance Performance: A Meta-Analysis. Int J Sports Med 47(2):81-94. doi:10.1055/a-2637-7283The second of the two meta-analyses agreeing on the pooled effect.
  14. World Athletics, Book C - C2.1A Athletic Shoe Regulations, approved 2 December 2025, effective 1 January 2026; and the World Athletics approved shoe list.The stack-height and single-plate rules, and what the approved list does and does not contain.