In the last post, I presented P_TRIMP, a power-based version of TRIMP for durations larger than 1 hour. The fact that it is directly based on TRIMP makes it a considerable improvement over other approaches, since it is directly based on the scientific evidence on this subject.
It is ironic that some people that have never done any studies on this subject, besides being the Internet champions of the “evidence-based” methods, choose to ignore the scientific evidence in favor of their own beliefs. It seems that science only exists when it serves their interests, and that their ego-driven pseudo-science is more important than the peer-reviewed real science. If you add to this the public ad-hominem attacks, it all amounts to a classical case of intellectual dishonesty.
But all these metrics, the impulse-response models, training load quantification, what is it good for? ABSOLUTELY NOTHING!
Very often, the ones that see using the impulse-response model based training load quantification as the absolute right way of coaching, operate under the assumption that those that do not use these methods, do so because they have a difficulty in understanding them. With this series of posts, I tried to show that there are a number of coaches (because I am not only not alone, but belong to a large “silent” majority) that understand the subject to the point to know how useless it is for training and racing.
Using training load quantification tools has very little to do with coaching. It might be a curiosity for some that are not interested in performance, or maybe a way to distinguish themselves from a marketing point-of-view, but it is certainly not the way the top endurance coaches operate.
All this emphasis in meaningless “metrics” forgets the most important part of the equation: the athlete. Athletes are not simple systems that can be modeled by a few variables. This is something that good coaches have identified throughout the years as the main aspect of coaching. And while those coaches work to prepare their athletes for the biggest stages in endurance sports, the pseudo-scientists post away on Internet fora.
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8 comments:
so how do *YOU* compare training load for cycling from weeks to week ? Old school milage, when done in adequate amount can never lie. 1000Km/Week is solid training even if it is all done in 39x23.
Yes....great post. We are NOT machines and cannot be trained like one or expected to race like one.
Intellectual dishonesty is hiding behind a blog and refusing to respond to criticisms directed at one's analysis. ;-)
"Hiding behind a blog"??? Did you want to invent a new oxymoron, or did it just happen?
And here's a random smiley, just for you :-)
Thanks for reading!
Paulo
So are you done talking about TSS and what ever other abreviation you want?? Last time i check, i never heard a athlete that understand the process talking about all that sillyness....
Oh...and to FAT CYCLIST....
The best way to track a athlete training load is to observe the athlete ATTITUDE. A powermeter will never come close to give these important data.Simple but it take a experience coach to be able to read that metric.
let s get back to what matter.
to Jonnyo,
I am either "fat cyclinst", or "FAT Cyclist".Clling me "FAT CYCLIST" is like telling right in my face that I am obese, or am I ? (-_-);
Paulo and Andy Coggan, you guys should both learn korean smileys, that's what the kewl intellectuals use nowadays (^_~)
I'm shocked, paulo...
You mean to tell me that "triathlon racing is not a math problem"?
sweet, informative trimps plot here:
trimps plot
Models are good for nothing? Really?
If I'm not mistaken, your other profession relies rather heavily on them, no? Thankfully, the apparent complexity of fluids did not deter those that developed *simple* models that although not perfect were still able to capture a substantial amount of their behavior.
Athletes are indeed complex but having quantitative models to help predict and optimize their performance (i.e., coach) seems like a worthy endeavor to me.
I do agree, however, that scientific validation is paramount for progress in this area. Either that, or perhaps "science-by-internet" is somehow a valid concept? Maybe pharmaceutical companies could validate new drugs this way too?...
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