SCM. For some reason, the script horked on the 70+ age groups. I may or may not be able to get back to that soon.
Update 1:20 PM ... fixed.
:applaud: Great job as always. THANKS!
It was easy to change the script to output a .csv file. Sneak peak! Here are the 2016 SCY Motivational Times in a spreadsheet.
Thanks for this, Swimosaur! Although it's a bit disheartening to see that I am not even back on the chart for most events (I was a AAA in my best event before my injuries, and in the A's and BB for just about everything else), I am just very thankful today (and everyday) to be back in the pool! :agree:
Happy Thanksgiving!
:turkey:
Judd - Thank you for posting these times! Do you have the information available in a spreadsheet?
It was easy to change the script to output a .csv file. Sneak peak! Here are the 2016 SCY Motivational Times in a spreadsheet.
It was easy to change the script to output a .csv file ...
But it was hard to resist the urge to play with it. Recall, in the Motivational Times, Column X is the average of the 10th place time over the previous three years; all other columns are just scaled copies of it. So we might as well just play with Column X.
The first graph is a (somwhat edited) version of what you get if you just graph Column X. I threw out the longer events and the IM's, so the horizontal axis represents 50s, 100s, and 200s of each stroke. The vertical axis is time in seconds. If you look closely, you can see the variations due to age and sex. The thing that impressed me most about this graph was how similar the shape of the curve is, over age groups, regardless of distance, stroke, or sex.
forums.usms.org/attachment.php
So in the second graph, I first divided the times for the 100s by 2, and the 200s by 4, to get the average time per 50, regardless of distance. Then I summed the times for each age group, regardless of stroke or distance, giving a total time for men & women by age group. The curves are almost parallel, but not quite.
forums.usms.org/attachment.php
For the third graph, I combined men & women, and normalized the overall time so that AG 18-24 = 100; all higher AGs can be interpreted as some percentage slower. What I find remarkable is how slowly performance deteriorates -- 10th place times even in the 50-54 age group are within 10% of the times in 18-24! Another impressive feature is how lawful the curve looks (yes, there's a lot of averaging going on).
forums.usms.org/attachment.php
So I invented a little holiday game for myself, called, "Are you ahead of the game?" The way this game is played, you pick some time in some event you did at a younger age, and then multiply by the appropriate factors to predict your time at your current age. If you're faster than predicted, you're ahead of the game! What fun!
For example, I did a 1:48 in the 200 free as an 18 year old senior in high school in 1976. 1:48 = 108 seconds. I did a 2:03 at Senior Games Nationals as a 55 year old in 2013. 2:03 = 123 seconds. Am I ahead of the game, or not?
Younger Time in sec * ( Older Factor / Younger Factor ) = Predicted Older Time in sec
108 * ( 114.81 / 100.00 ) = 123.99 >> 2:03.99 ... I am ever so slightly ahead of the game!
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
... if more swimmers participated in masters in that age group, I bet the drop off percentages would be steeper.
If more fast swimmers participated in every age group, all the times would be faster. I know guys from my HS swim team, now in their 50s, who could post a Top Ten time every splash. But they don't compete. One HS guy, now in his 40s, swam SCY nats in 2012 & won all the fly events. Then back to the shadows. I think there are a lot of fast people out there, in every age group, who don't compete.
But I don't think it matters. This method arbitrarily picks the 10th place time nationally among swimmers who do compete. These are pretty healthy people. But you could pick 15th or 30th, or almost any other number, and probably get almost the same curves. (Numbers #1 & #2 probably wouldn't work, because the freak-of-nature factor would put them off the curve.)
That said, there are plenty of problems. Two are, (1) We're comparing times across many decades, and the younger age groups have presumably been exposed to more modern training methods and presumably better coaching. So is this really an apples-to-apples comparison? (2) The curves for men & women diverge in higher age groups, so they really shouldn't be combined. Hey, it's a game! :)
This is very cool! I'm way behind the curve but with so many swim-less years, that doesn't suprise me.
I do wonder, however, if the sample size is too small in the 18-24 age group to adequately predict the drop off if you were an age group or college swimmer. Just generally browsing the chart, it looks like my actual 18-24 times would put me typically in the X or AAAA group. In the world of USA swimming, though, I know my times at that time, were mostly AA, a couple AAA and two consistent AAAA. Meaning, if more swimmers participated in masters in that age group, I bet the drop off percentages would be steeper. By the time the 30s-40s come around more ex-swimmers get back in the groove and there are more swimmers in general so masters times start looking faster again. Especially in the less contested events like the 400 IM.
For example, at 35, my fastest event is currently at 16% decline after 8 months. I expect to get back into the 5.5% range within a year or two. In the first month I jumped back in the pool, we were talking 58% decline over 18yo times.
My less favorable events are actually closer to a 21% decline and I expect to get back to the 12% range in a year or two.
I, however, have generally not stayed in good active shape. I had a spell of swimming in my late 20's but I was not consistent enough to really make a fitness dent. A guy on my team has stayed in great shape with other sports through the years and within 3 months back in the pool seems to be hitting within close to (within 1-3 seconds) his 18 year old times. He's in the 45-50 age group.
That graph is wonderful.It makes me feel a little better about my recent times, but at 66,nearly 67 I can tell you it is frustrating to be in the steeper part of the curve.
