In threads where training philosophy comes up, discussions of TRIMPS and TSS and other training models occasionally intrude. These models are not very well known, and even more poorly understood, so probably SolarEnergy, qbrain and I are just talking to each other and killing threads in those conversations. In any case, I figured I would present a brief overview of what it is that we're talking about when this terminology starts showing up.
Best case, this will introduce these models to the subset of swimmers (or coaches) who would be interested enough to use them, but didn't previously know enough to do so.
Plus, even if you're not the type to be interested in quantifying your training, it can be useful to think about workouts in this general framework.
And, at the very least, this might serve as a place to discuss some of the details without worrying about driving those other threads too far off-topic.
It's done through a long process of trial and error involving reconciliation between the peaks that the graphs generated with the actual performance that was delivered.
That said though, and with all due respect for the quality of the research you've been doing so far, it is also strongly recommended to use weighted averaged scoring data to these models for better accuracy, as opposed to rely solely on distance sort of inputs.
Moreover, since there's a very strong sprinting over shorter distances component to most swimmers, I would certainly prioritize using weighted avg scoring data over adjusting time constant for better accuracy.
As for the long process of fitting, it takes the excel solver roughly 2 seconds to find the time constants and K factors, it's really not that bad. As I mentioned, getting the performance measures seems to be the limiter for most people.
Selecting the performances is also important regarding what you mentioned. In one athlete for whom I had data at 100, 200, and 500 yards, the constants were different for each distance tested. So we have to pick intelligently a test performance distance that is relevant to the athlete. That's how I have handled it before.
As for yardage vs swimscore or sharp score, I am pragmatic about it. I use what is available, if only yaradage that is fine, we get good data fits with yardage as input. If I have yaradage and intensity that's fine too, I also get good fits using that as input.
I'll be doing a comparative study with my masters squad this fall, if everything goes right.
It's done through a long process of trial and error involving reconciliation between the peaks that the graphs generated with the actual performance that was delivered.
That said though, and with all due respect for the quality of the research you've been doing so far, it is also strongly recommended to use weighted averaged scoring data to these models for better accuracy, as opposed to rely solely on distance sort of inputs.
Moreover, since there's a very strong sprinting over shorter distances component to most swimmers, I would certainly prioritize using weighted avg scoring data over adjusting time constant for better accuracy.
As for the long process of fitting, it takes the excel solver roughly 2 seconds to find the time constants and K factors, it's really not that bad. As I mentioned, getting the performance measures seems to be the limiter for most people.
Selecting the performances is also important regarding what you mentioned. In one athlete for whom I had data at 100, 200, and 500 yards, the constants were different for each distance tested. So we have to pick intelligently a test performance distance that is relevant to the athlete. That's how I have handled it before.
As for yardage vs swimscore or sharp score, I am pragmatic about it. I use what is available, if only yaradage that is fine, we get good data fits with yardage as input. If I have yaradage and intensity that's fine too, I also get good fits using that as input.
I'll be doing a comparative study with my masters squad this fall, if everything goes right.
