Superspike benefits decrease the faster the pace is - explains some contradictions

Running Shoes/Running Gear Shoes

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1 year ago 07/01/2023 10:34am EDT

re: Curious Too

Thank you for bumping this. I had missed the earlier posts. Kudos to zzzz for the excellent and fascinating research! It inspired me to look at higher level performers in the men's 5,000. Specifically, I looked at the 50th, 100th, 250th, and 500th best performers over the same time span. Here's what I found:

50th
2012 13:15.31 Thomas FARRELL
2013 13:15.87 Elroy GELANT
2014 13:18.10 Aweke AYALEW
2015 13:17.49 Collis BIRMINGHAM
2016 13:16.35 Davis KIPLANGAT
2017 13:17.80 Bethwell BIRGEN
2018 13:20.08 Bethwell BIRGEN
2019 13:17.23 Kirubel ERASSA
2020 13:23.99 Samwel MASAI
2021 13:15.91 Jonas RAESS
2022 13:11.73 Emmanuel Korir KIPLAGAT

100th
2012 13:24.22 Clement Kiprono LANGAT
2013 13:27.10 Dawit WELDESILASIE
2014 13:28.81 Milion YEHWALAESHET
2015 13:27.44 Daniel KIPKEMBOI
2016 13:24.13 Teklit TEWELDEBRHAN
2017 13:26.55 Thomas CURTIN
2018 13:28.54 Nicholas KIPKORIR
2019 13:27.16 Amos KURGAT
2020 13:33.83 Suguru OSAKO
2021 13:22.16 Oliver HOARE
2022 13:20.02 Nicholas Mboroto KOSIMBE

250th
2012 13:42.31 Collin JARVIS
2013 13:44.14 Abdenasir FATHI
2014 13:45.25 Girma MECHESO
2015 13:40.06 Vladimir NIKITIN
2016 13:42.06 Aaron NELSON
2017 13:43.75 Florian ORTH
2018 13:43.13 Obsa ALI
2019 13:42.06 Dejen TESFALEM
2020 13:45.80 Shuya IWAMI
2021 13:35.94 Masashi NONAKA
2022 13:33.38 Nickson Lesiyia PARIKEN

500th
2012 13:57.34 Morsi RAYYAN
2013 13:57.68 Bruno ALBUQUERQUE
2014 13:58.38 Yo YAZAWA
2015 13:54.75 Akira AIZAWA
2016 13:55.19 Tony SMORAGIEWICZ
2017 13:57.39 Mizuki SEKIGUCHI
2018 13:56.49 Milkesa MENGESHA
2019 13:55.35 Wesley DE KERPEL
2020 13:57.61 Akihiro KANEKO
2021 13:47.25 Kazuto KAWABATA
2022 13:44.65 Nathan HENDERSON

At the 50th level, the 2012-2020 average was 13:18.02 and dropped to 13:11.73 in 2022, an improvement of 6.29 seconds.

At the 100th level, the 2012-2020 average was 13:27.53 and dropped to 13:20.02, an improvement of 7.51 seconds.

At the 250th level, the 2012-2020 average was 13:43.17 and dropped to 13:33.38, an improvement of 9.79 seconds.

At the 500th level, the 2012-2020 average was 13:56.69 and dropped to 13:44.65, an improvement of 12.04 seconds.

These findings add further support to zzzz's conclusion that the super shoe effect is greater at slower paces. In this case, we see that trend within the same event.

It's also noteworthy that a 13:44.65 mark in the 5,000 would have been at or near 250th in the world from 2012-2020 but in 2022 it was "only" good enough for 500th.

1 year ago 07/01/2023 11:02am EDT

re: bad hypothesis

bad hypothesis wrote:
You make the claim that the benefits decrease the faster the pace, which insinuates that the top pros get a lesser benefit. Evidence shows the top pro runners have improved tremendously with the shoes.
It has nothing to do with pace, it has to do with DISTANCE or LENGTH of race. Of course the longer the race, the slower the pace. But on average a 2 hour marathoner and a 3 hour marathoner will both get a 4% increase in performance. 4% of 3 hours is a bigger number than 4% of 2 hours, but it's the same proportional benefit regardless of the pace.
You don't factor in that the athletes can train harder because of the shoes and still recover better. Most of the benefit of the shoes is allowing harder training, not the small % of extra energy return in the race.

zzzzz your analysis is wrong. pretty amateur mistakes.

a 13:57 5k is more than 80 seconds slower than the world record. if the shoes make a 4% difference, the 13:57 guy is going to get an additional 3.2 seconds benefit (4% of 80).

the shoes help 5k training more. it allows people to do workouts like double threshold that would cause massive injuries pre-super shoes to 99% of people.

The problem with the 800 and 1500 is that they are more speed based, meaning adding extra mileage and tempo runs aren't going to help as much. What we are seeing is more 1500m runners chose to focus on the 5k because the shoes can now help them handle the extra training.

1 year ago 07/01/2023 11:25am EDT

re: good at math

Some of the worst analysis we’ve seen.

1 year ago 07/01/2023 12:35pm EDT

re: zzzz

My intuition

SuperShoes improve economy ==> less economical runners get a bigger benefit

1 year ago 07/01/2023 5:38pm EDT

re: good at math

If the race time improvement is based on increased training through super shoes, wouldn't we see a roughly equal increase in the 1500m and 5k? The 1500m may be more speed based, but it is still very much an aerobic event. Look at Jakob's training or Steve Scott's, very heavy aerobic work even though they specialize in the 1500/mile.

Another point of clarification is that we are looking at ~0.5 to 2% improvements in track times since the introduction of super spikes, which is roughly a 2 to 3% increase in running economy. With such small improvements in race times, the data analysis can be difficult, but it does look like there is a downward trend in race time improvements as race speed increases.

7 months ago 11/22/2023 10:13am EST

re: zzzz

zzzz wrote:
zzzz wrote:
I put those four points in Excel, and the log(benefit) vs. pace is highly correlated. The best fit line gives:
55 pace: 0.3 second superspike benefit per 400
56 pace: 0.4 second
57 pace: 0.4 second
58 pace: 0.4 second
59 pace: 0.5 second
60 pace: 0.5 second
61 pace: 0.6 second
62 pace: 0.6 second
63 pace: 0.7 second
64 pace: 0.8 second (or 0.7 second in the lesser benefit/data picking cheat option in the 10,000)
65 pace: 0.8 second
66 pace: 0.9 second
67 pace: 1.0 second (or 0.9 second)
68 pace: 1.1 second (or 1.0 second)
69 pace: 1.2 second (or 1.1 second)
70 pace: 1.3 second (or 1.2 second)
71 pace: 1.5 second (or 1.3 second)
72 pace: 1.6 second (or 1.4 second)
I didn't really make it clear that the starting pace for the table above is old spikes, and that makes a difference down at slower end. So this is easier to interpret:
oldspikes/superspikes/benefit (all in seconds)
55 54.7 0.3
56 55.6 0.4
57 56.6 0.4
58 57.6 0.4
59 58.5 0.5
60 59.5 0.5
61 60.4 0.6
62 61.4 0.6
63 62.3 0.7
64 63.2 0.8
65 64.2 0.8
66 65.1 0.9
67 66.0 1.0
68 66.9 1.1
69 67.8 1.2
70 68.7 1.3 (underestimate? Vaporflys mixed into 10000 data 2016+)
71 69.5 1.5 (Vaporflys in data)
72 70.4 1.6 (Vaporflys in data)
To make it easier to interpret the impact on different track events, here are tables for the old spike/superspike conversion. I recalculated these from the regression line on the graph in post #4, so not based on the rounded benefit above. Remember that this the is collective benefit based the World Athletics male population and benefits to individuals and possibly women (will do that later) may vary.
1500m
oldspikes/superspikes/benefit
3:26.3 3:25.1 1.2
3:30.0 3:28.7 1.3
3:33.8 3:32.3 1.5
3:37.5 3:35.9 1.6
3:41.3 3:39.5 1.8
3:45.0 3:43.0 2.0
3:48.8 3:46.6 2.2
3:52.5 3:50.1 2.4
3:56.3 3:53.7 2.6
4:00.0 3:57.1 2.9
4:03.8 4:00.6 3.2
4:07.5 4:04.0 3.5
4:11.3 4:07.5 3.8
4:15.0 4:10.8 4.2
4:18.8 4:14.2 4.6
4:22.5 4:17.4 5.1
4:26.3 4:20.7 5.6
4:30.0 4:23.9 6.1
mile
oldspikes/superspikes/benefit
3:41.3 3:40.0 1.3
3:45.3 3:43.9 1.4
3:49.3 3:47.8 1.5
3:53.4 3:51.6 1.8
3:57.4 3:55.5 1.9
4:01.4 3:59.3 2.1
4:05.4 4:03.1 2.3
4:09.4 4:06.9 2.5
4:13.5 4:10.7 2.8
4:17.5 4:14.4 3.1
4:21.5 4:18.1 3.4
4:25.5 4:21.8 3.7
4:29.6 4:25.5 4.1
4:33.6 4:29.1 4.5
4:37.6 4:32.7 4.9
4:41.6 4:36.2 5.4
4:45.7 4:39.7 6.0
4:49.7 4:43.1 6.6
3000
oldspikes/superspikes/benefit
7:22.5 7:18.9 3.4
7:30.0 7:26.1 3.9
7:37.5 7:33.2 4.3
7:45.0 7:40.3 4.7
7:52.5 7:47.3 5.2
8:00.0 7:54.3 5.7
8:07.5 8:01.2 6.3
8:15.0 8:08.1 6.9
8:22.5 8:14.9 7.6
8:30.0 8:21.7 8.3
8:37.5 8:28.3 9.2
8:45.0 8:34.9 10.1
8:52.5 8:41.4 11.1
9:00.0 8:47.8 12.2
2 mile
oldspikes/superspikes/benefit
8:02.8 7:58.6 4.2
8:10.8 8:06.2 4.6
8:18.9 8:13.8 5.1
8:26.9 8:21.4 5.6
8:35.0 8:28.9 6.1
8:43.0 8:36.3 6.7
8:51.1 8:43.7 7.4
8:59.1 8:51.0 8.1
9:07.2 8:58.2 9.0
9:15.2 9:05.4 9.8
9:23.3 9:12.4 10.8
9:31.3 9:19.4 11.9
9:39.4 9:26.3 13.1
5000m
oldspikes/superspikes/benefit
12:42.5 12:35.3 7.2
12:55.0 12:47.1 7.9
13:07.5 12:58.8 8.7
13:20.0 13:10.5 9.5
13:32.5 13:22.0 10.5
13:45.0 13:33.5 11.5
13:57.5 13:44.8 12.7
14:10.0 13:56.1 13.9
14:22.5 14:07.2 15.3
14:35.0 14:18.2 16.8
14:47.5 14:29.0 18.5
15:00.0 14:39.7 20.3
10000m
oldspikes/superspikes/benefit
26:15.0 25:57.7 17.3
26:40.0 26:21.0 19.0
27:05.0 26:44.1 20.9
27:30.0 27:07.0 23.0
27:55.0 27:29.7 25.3
28:20.0 27:52.2 27.8
28:45.0 28:14.4 30.6
29:10.0 28:36.4 33.6
29:35.0 28:58.0 37.0
30:00.0 29:19.3 40.7
The most massive effects are for the slower runners and longer events, and particularly the slower runners in the longest events (down to 72 sec 400 pace at least). So, the average, non-elite high school boys in the 1600/3200, non-elite college guys in the 5000/10000, etc.

Would you be willing to do the follow-up analysis for women? Are you suggesting that elite women are getting a bigger boost than elite men?

6 months ago 12/18/2023 5:33pm EST

re: Moo G

makes sense if you remember the impact that supershoes have had on the marathon

6 months ago 12/18/2023 6:11pm EST

re: Riskit2win

Zzzz are you out there?

2 months ago 05/08/2024 10:29pm EDT

re: zzzz

For fun I extrapolated out (very roughly) from the data provided for much slower runners - just looked at the mile because that is what I run. Looks like this :

orig data

4:37.6 4:32.7 4.9
4:41.6 4:36.2 5.4
4:45.7 4:39.7 6.0
4:49.7 4:43.1 6.6

roughly every 4s in mile times is an additional 0.5s / 0.6s advantage
extrapolating & use 5s & 0.5s to be conservative:
4:55 7
5:00 7.5
5:05 8
5:10 8.5
5:15 9
5:20 9.5
5:25 10
5:30 10.5

So this is what it might look like for slower runners.

This post was edited 10 minutes after it was posted.

2 months ago 05/09/2024 1:48am EDT

re: zzzz

What about better training (copying Marius bakken/ingebrigtsen)? And the Maurten products?

it is quite obvious that times improved a lot once other countries started training after the norwegian method.

(if you listen to the interviews with Marius, he says that he came up with the method by studying (male) kenyans and how the best runners naturally would run at threshold, without even knowing it. So they wouldnt be showing the same improvement by new training).

And if you listen to the Norwegians/almgren they definitely claim that they get a boost (time wise) from maurtens biocarb/hydrogels.

It seems that there is an underlying assumption that faster times is only attributed to superspikes/shoes.

2 months ago 05/09/2024 3:56am EDT

re: Riskit2win

Riskit2win wrote:
zzzz wrote:
I didn't really make it clear that the starting pace for the table above is old spikes, and that makes a difference down at slower end. So this is easier to interpret:
oldspikes/superspikes/benefit (all in seconds)
55 54.7 0.3
56 55.6 0.4
57 56.6 0.4
58 57.6 0.4
59 58.5 0.5
60 59.5 0.5
61 60.4 0.6
62 61.4 0.6
63 62.3 0.7
64 63.2 0.8
65 64.2 0.8
66 65.1 0.9
67 66.0 1.0
68 66.9 1.1
69 67.8 1.2
70 68.7 1.3 (underestimate? Vaporflys mixed into 10000 data 2016+)
71 69.5 1.5 (Vaporflys in data)
72 70.4 1.6 (Vaporflys in data)
To make it easier to interpret the impact on different track events, here are tables for the old spike/superspike conversion. I recalculated these from the regression line on the graph in post #4, so not based on the rounded benefit above. Remember that this the is collective benefit based the World Athletics male population and benefits to individuals and possibly women (will do that later) may vary.
1500m
oldspikes/superspikes/benefit
3:26.3 3:25.1 1.2
3:30.0 3:28.7 1.3
3:33.8 3:32.3 1.5
3:37.5 3:35.9 1.6
3:41.3 3:39.5 1.8
3:45.0 3:43.0 2.0
3:48.8 3:46.6 2.2
3:52.5 3:50.1 2.4
3:56.3 3:53.7 2.6
4:00.0 3:57.1 2.9
4:03.8 4:00.6 3.2
4:07.5 4:04.0 3.5
4:11.3 4:07.5 3.8
4:15.0 4:10.8 4.2
4:18.8 4:14.2 4.6
4:22.5 4:17.4 5.1
4:26.3 4:20.7 5.6
4:30.0 4:23.9 6.1
mile
oldspikes/superspikes/benefit
3:41.3 3:40.0 1.3
3:45.3 3:43.9 1.4
3:49.3 3:47.8 1.5
3:53.4 3:51.6 1.8
3:57.4 3:55.5 1.9
4:01.4 3:59.3 2.1
4:05.4 4:03.1 2.3
4:09.4 4:06.9 2.5
4:13.5 4:10.7 2.8
4:17.5 4:14.4 3.1
4:21.5 4:18.1 3.4
4:25.5 4:21.8 3.7
4:29.6 4:25.5 4.1
4:33.6 4:29.1 4.5
4:37.6 4:32.7 4.9
4:41.6 4:36.2 5.4
4:45.7 4:39.7 6.0
4:49.7 4:43.1 6.6
3000
oldspikes/superspikes/benefit
7:22.5 7:18.9 3.4
7:30.0 7:26.1 3.9
7:37.5 7:33.2 4.3
7:45.0 7:40.3 4.7
7:52.5 7:47.3 5.2
8:00.0 7:54.3 5.7
8:07.5 8:01.2 6.3
8:15.0 8:08.1 6.9
8:22.5 8:14.9 7.6
8:30.0 8:21.7 8.3
8:37.5 8:28.3 9.2
8:45.0 8:34.9 10.1
8:52.5 8:41.4 11.1
9:00.0 8:47.8 12.2
2 mile
oldspikes/superspikes/benefit
8:02.8 7:58.6 4.2
8:10.8 8:06.2 4.6
8:18.9 8:13.8 5.1
8:26.9 8:21.4 5.6
8:35.0 8:28.9 6.1
8:43.0 8:36.3 6.7
8:51.1 8:43.7 7.4
8:59.1 8:51.0 8.1
9:07.2 8:58.2 9.0
9:15.2 9:05.4 9.8
9:23.3 9:12.4 10.8
9:31.3 9:19.4 11.9
9:39.4 9:26.3 13.1
5000m
oldspikes/superspikes/benefit
12:42.5 12:35.3 7.2
12:55.0 12:47.1 7.9
13:07.5 12:58.8 8.7
13:20.0 13:10.5 9.5
13:32.5 13:22.0 10.5
13:45.0 13:33.5 11.5
13:57.5 13:44.8 12.7
14:10.0 13:56.1 13.9
14:22.5 14:07.2 15.3
14:35.0 14:18.2 16.8
14:47.5 14:29.0 18.5
15:00.0 14:39.7 20.3
10000m
oldspikes/superspikes/benefit
26:15.0 25:57.7 17.3
26:40.0 26:21.0 19.0
27:05.0 26:44.1 20.9
27:30.0 27:07.0 23.0
27:55.0 27:29.7 25.3
28:20.0 27:52.2 27.8
28:45.0 28:14.4 30.6
29:10.0 28:36.4 33.6
29:35.0 28:58.0 37.0
30:00.0 29:19.3 40.7
The most massive effects are for the slower runners and longer events, and particularly the slower runners in the longest events (down to 72 sec 400 pace at least). So, the average, non-elite high school boys in the 1600/3200, non-elite college guys in the 5000/10000, etc.
Would you be willing to do the follow-up analysis for women? Are you suggesting that elite women are getting a bigger boost than elite men?

It’s not worth doing a follow up for women because the analysis isn’t good.

Any analysis of super shoes, must be first held to a backwards compatibility standard.

For example, the top 1500m times are all pre-super shoe era. If we adjust those times about 0.3 per lap, we are looking at 1.5 seconds per 1500m.

So, if El G is a 3:24.5 guy Lagat 3:24.7.

That’s a colossal difference between the 3:28s the best are running today.

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