Here are the 2017 SCY motivational times. LCM & SCM will be posted later!
Motivational times are presented in three formats: human-readable PDF, and two CSV spreadsheets. In the first spreadsheet (mm:ss), times are rendered in the usual minute:second notation. In the second (sec), times are converted to seconds, which facilitates stuff like this. Have fun!
PDF
mm:ss
sec
SCY
2017-SCY-Masters-Motivational-Times.pdf
mmss.csv
sec.csv
LCM
SCM
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Motivational times (MTs) are calculated from the base time given in Column X. The algorithm for calculating the base time is similar, but not identical to, the method USMS uses to calculate national qualifying times (NQTs) for the annual SCY national championships.
Most of the time, Column X is(A) the average of the previous three year’s 10th place times.
However, if there are fewer than three 10th place times over the previous three years, we use, in order,(B) average of two 10th place times over the previous three years. If there are fewer than two 10th place times,
(C) average of three 5th place times + 4.45%. If there are fewer than three,
(D) average of two 5th place times + 4.45%. If there are fewer than two,
(E) No Time (NT).
If one of the alternatives B-D is used, it’s indicated by a superscript.
The rest of the columns are proportional to Column X as follows,
AAAA = X + 5%
A = X + 20%
AAA = X + 10%
BB = X + 30%
AA = X + 15%
B = X + 40%
For MTs, the same algorithm is used for all three courses, SCY, LCM, and SCM.
Relationship to NQTs. For SCY, as long as Column X is calculated using method A, B, or E, Columns AA and AAA should be, but are not guaranteed to be, exactly the NQTs for sprints and 200+ events respectively. However, there will be some small differences for methods C and D. For LCM, the MTs should be different from the NQTs in all cases. USMS does not publish NQTs for SCM or for age groups 85+.
Column X. I like to think of Column X as “the moral equivalent of a Top Ten time”. Of course, in any given year, the 10th place time will be faster or slower by some amount than the average of the three previous years, so of course, Column X is not an actual Top Ten time. Too bad, huh? You can also think of it as “the time I need to hit to have about a 50/50 chance.”
Any chance, with your spreadsheet expertise, that you could come up with a formula to update and improve this "age grading" calculation from 2006?
www.usms.org/.../articledisplay.php
I did something very similar in this post. The main difference is, I used more aggressive averaging,
Nordstrom used 5th place times from 2004; I used the average of 10th place times from 2013-2015.
Nordstrom calculated age-grading ratios for both men and women, for all events. I wanted a smaller number of numbers in the final table, so I averaged over events, and combined men & women.
In addition,
Nordstrom used data for all events; I threw out IMs and all events longer than 200 yards (simply because it made my job easier. I wasn't trying to be so thorough and meticulous).
Nordstrom's ratios are less than one, mine are the reciprocals (greater than one).
Given that, I would not expect there to be too much difference between my factors and (the reciprocal of) Nordstrom's factors, for the bottom row of his table, which is the average over all events. See graph below.
The relevant equations are (for my factors),
If you're trying to convert a time from a younger age group to an older age group (from my 2015 post),Younger Time in sec * ( Older Factor / Younger Factor ) = Predicted Older Time in sec
And if you're trying to convert a time from an older age group to a younger age group,Older Time in sec * ( Younger Factor / Older Factor ) = Predicted Younger Time in sec
The factors are,
AG
Factor
18-24
100.00
25-29
100.81
30-34
102.88
35-39
104.38
40-44
105.49
45-49
107.05
50-54
109.49
55-59
114.81
60-64
123.22
65-69
135.87
70-74
156.47
75-79
190.36
80-84
214.17
For comparison, here's a graph of my factors vs. Nordstrom's averages,
forums.usms.org/attachment.php
To my eye,
The factors Nordstrom vs. Swimosaur are not too different for age groups through 45-49 for both men and women. For men, they are nearly identical through 65-69.
For age groups 50+, Nordstrom's factors are higher for women than men. I also noticed this in the 2015 post ("The curves are almost parallel, but not quite."), but I averaged men & women anyway.
Personally, if I were going to do this, I would use my factors and formulas. They are simpler. You don't have to wade through big tables of numbers. After all, this is an inexact science, close enough is good enough, and your time would probably be better spent doing a killer kick set :)
For another, probably superior method, see Chris Stevenson's calculator.
Any chance, with your spreadsheet expertise, that you could come up with a formula to update and improve this "age grading" calculation from 2006?
www.usms.org/.../articledisplay.php
I did something very similar in this post. The main difference is, I used more aggressive averaging,
Nordstrom used 5th place times from 2004; I used the average of 10th place times from 2013-2015.
Nordstrom calculated age-grading ratios for both men and women, for all events. I wanted a smaller number of numbers in the final table, so I averaged over events, and combined men & women.
In addition,
Nordstrom used data for all events; I threw out IMs and all events longer than 200 yards (simply because it made my job easier. I wasn't trying to be so thorough and meticulous).
Nordstrom's ratios are less than one, mine are the reciprocals (greater than one).
Given that, I would not expect there to be too much difference between my factors and (the reciprocal of) Nordstrom's factors, for the bottom row of his table, which is the average over all events. See graph below.
The relevant equations are (for my factors),
If you're trying to convert a time from a younger age group to an older age group (from my 2015 post),Younger Time in sec * ( Older Factor / Younger Factor ) = Predicted Older Time in sec
And if you're trying to convert a time from an older age group to a younger age group,Older Time in sec * ( Younger Factor / Older Factor ) = Predicted Younger Time in sec
The factors are,
AG
Factor
18-24
100.00
25-29
100.81
30-34
102.88
35-39
104.38
40-44
105.49
45-49
107.05
50-54
109.49
55-59
114.81
60-64
123.22
65-69
135.87
70-74
156.47
75-79
190.36
80-84
214.17
For comparison, here's a graph of my factors vs. Nordstrom's averages,
forums.usms.org/attachment.php
To my eye,
The factors Nordstrom vs. Swimosaur are not too different for age groups through 45-49 for both men and women. For men, they are nearly identical through 65-69.
For age groups 50+, Nordstrom's factors are higher for women than men. I also noticed this in the 2015 post ("The curves are almost parallel, but not quite."), but I averaged men & women anyway.
Personally, if I were going to do this, I would use my factors and formulas. They are simpler. You don't have to wade through big tables of numbers. After all, this is an inexact science, close enough is good enough, and your time would probably be better spent doing a killer kick set :)
For another, probably superior method, see Chris Stevenson's calculator.
