I'm no statistic and never said I was but I'm an engineer and I understand math. You can bash me how much you want but that won't change the fact that there really isn't any significant improvement in results when you look at plain numbers from pre to post VF introduction. If VF were so much better as some claim here you'd get difference in number of best results because for the past two years most of the faster runners have switched to them. All races I did this year almost entire elite section had VF either 4% or Next%
Claim is they improve performance by 2.7% (4% less oxygen consumption). If that would be the case we would have to see almost exponential increase in number of best sub-x:xx times per year as 2.7% increase in results translates to 2-3 min marathon improvement. Yet numbers are very much consistent in all groups just look at the table. The increase in the past year or two is small and on par with 2011-2012 results.
For example, top 1000th best time per year in the last decade by athlete. Difference between slowest year (2010, 2:20:54) and fastest year (2012, 2:19:04) is 1:50 or 1.3%. And that is before VF era.
Difference between just pre and post VF years is only about a minute or 0.7% which is less increase than what happened in 2011-2012.
Number of athletes that broke sub-2:35 per year? Difference between lowest (2010, 3000) and highest (2011, 3386) year is 11%. That is an astounding increase and yet again it happened from 2010 to 2011. Difference between pre and post VF era is much more normally distributed with low (2014, 3115) and high (2018, 3318) being only 6% difference.
If you look at sub-2:25 there is the same trend. Low year (2010, 1412) and high year (2012, 1678) is 16% apart. Crazy jump versus just pre and post VF era relatively modest increase of 7% from 1478 (2015) to 1596 (2019). Even less spectacular if take decades average time as reference.
The only increase you see is in very fast marathon times still on par with year 2012 though. But these are performances on the far left side of the Gaussian (normal) distribution curve and would be discarded by scientific study.
Column 1 | Year
Column 2 | Number of sub-2:05 performances (best by athlete)
Column 3 | Number of sub-2:15 performances (best by athlete)
Column 4 | Number of sub-2:25 performances (best by athlete)
Column 5 | Number of sub-2:35 performances (best by athlete)
Column 6 | 10th fastest marathon time (best by athlete)
Column 7 | 100th fastest marathon time (best by athlete)
Column 8 | 1000th fastest marathon time (best by athlete)
YEAR | 2:05 | 2:15 | 2:25 | 2:35 | 10TH | 100TH | 1000TH
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2019 | 12 | 566 | 1596 | 3311 | 2:04:24 | 2:08:12 | 2:19:09
2018 | 14 | 515 | 1568 | 3318 | 2:04:40 | 2:08:46 | 2:20:03
2017 | 4 | 492 | 1439 | 3106 | 2:05:39 | 2:09:11 | 2:20:20
2016 | 7 | 459 | 1471 | 3094 | 2:05:21 | 2:09:28 | 2:20:19
2015 | 2 | 486 | 1478 | 3165 | 2:06:00 | 2:09:14 | 2:20:10
2014 | 8 | 478 | 1492 | 3115 | 2:05:13 | 2:09:00 | 2:20:15
2013 | 9 | 478 | 1513 | 3174 | 2:05:16 | 2:09:08 | 2:20:09
2012 | 11 | 566 | 1678 | 3368 | 2:04:54 | 2:08:33 | 2:19:04
2011 | 3 | 495 | 1610 | 3386 | 2:05:45 | 2:09:25 | 2:19:51
2010 | 3 | 448 | 1412 | 3000 | 2:06:09 | 2:09:41 | 2:20:54