Sequencing Workouts/Intensity
- Thread starter LT Intolerant
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Ok, OG....I'll show you how it works. I'm an old guy too (62). My FTP is 265w. Here's a 40k tt I recently did. 
See how easy that was? /img/vbsmilies/smilies/biggrin.gif
See how easy that was? /img/vbsmilies/smilies/biggrin.gif
After doing some research to confirm my recollection ...
It appears Dr. Banister's model was intended to make predictions. You design a training schedule. See what it predicts. Let some people train according to the schedule. Compare the results to the predictions.
It appears that the model makes some very poor predictions. Those predictions are clearly not dependant on the specific training schedule, but are defects in the model.So the model has been shown to be less than valid. Some authors have suggested changes to the model.
You say outlandish. I say the fact I can do both the intervals and rides based on the thought experiments is proof that TSS is not what it is claimed to be.
I would suggest that those who cannot do the intervals or rides based on the thought experiments are not training properly. But it is always a pleasure to show up at events where people are training poorly.
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I like the phrase "generally accurate." Pure religion.
1. But you have not provided any evidence that you can, in fact, complete the session you claim to have completed.
"In God we trust - everyone else must bring data." - W. Edward Demmings
2. Ain't it, though? /img/vbsmilies/smilies/biggrin.gif (He types while looking up at the stars-and-stripes jersey hanging on the wall.)
Better head back to the library: not only can the impulse-response model often accurately predict even day-to-day variations in performance (as well as day-to-day changes in physiological markers, e.g., changes in plasma hormone levels), such predictions are entirely dependent on the specific training schedule. (Indeed, the impulse-response model really only tells you when to train, not how/how much to train.)
The only problems with this approach are 1) the difficulty in collecting, outside of a laboratory setting, enough data in a short enough period of time to accurately solve the model, and 2) the fact that bar is set very, very, VERY high by experience/knowledge gained via other means. The practical utility of Banister's approach is therefore rather limited "in the real world",
OG,
All you are doing is showing that you "say you can", but, you haven't yet shown with evidence what you "actually can" do
I have read through the whole thread......zero evidence showing what you ACTUALLY can do, but plenty of evidence showing what your THINK you can do.....
It's pretty simple OG, start by posting your FTP plus data to support your claims (beyond saying I can do ***), that will help the doubters, until that occurs, which probably never will happen....AC and Co are all over this argument.....which has been a fact up to this point.
Paul
All you are doing is showing that you "say you can", but, you haven't yet shown with evidence what you "actually can" do
I have read through the whole thread......zero evidence showing what you ACTUALLY can do, but plenty of evidence showing what your THINK you can do.....
It's pretty simple OG, start by posting your FTP plus data to support your claims (beyond saying I can do ***), that will help the doubters, until that occurs, which probably never will happen....AC and Co are all over this argument.....which has been a fact up to this point.
Paul
In fairness to him, it is impossible for him to prove his argument. If he posts a file showing he can do it, people will just say his FTP is set too low. If he posts a file from an FTP test, he can't prove it was a maximal effort rather than a sub-maximal effort. There is nothing he can post that will prove he can do what he says he can do.
So I post my FTP. You ask for **** that it is actually my FTP. I have no way to prove that.
So I post the files from my rides. You say that I must have my FTP too low.
Then Mr. Coggan chimes in with "Not that a single exception - or even multiple exceptions - can disprove the conclusion that the normalized power algorithm is generally accurate to w/in ~5%."
As long as TSS remains a religion there is no value in producing what you ask for.
Forget your FTP, normalized power, TSS, etc.: just post a file proving you've done 2-3 h of short intervals at any intensity.
"often accurately predicts" has nothing to do with "makes some very poor predictions."
Your response whould have been: Which very poor predictions?
You seem to not know the peer reviewed papers very well. Yet, I am able to find papers that find problems. You appear to not understand Dr. Banister's work well enough to know that there are people who know how to collect the data. Yet, I can find papers where the authors have no problems in collecting the data..
You need to find a new religion.
I suppose that depends upon your definition of "proof", but for starters he could provide a file demonstrating that he (or at least someone) has, in fact, done 2-3 h of short, high intensity intervals (at a work:rest ratio of 1:1) as he has claimed. That's a very tall order regardless of how you look at it.
Yeah, right. /img/vbsmilies/smilies/rolleyes.gif
Bibliography
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Banister EW, Hamilton CL. Variations in iron status with fatigue modelled from training in female distance runners. Eur J Appl Physiol 1985; 54: 16-23.
Banister EW. Modeling elite athletic performance. In: MacDougall JD, Wenger HA, Green HJ, eds. Physiological Testing of the high-performance athlete, 2nd ed. Champaign, IL: Human Kinetics, 1991; 403-424.
Banister EW, Morton RH, Fitz-Clarke J. Dose-response effects of exercise modeled from training: physical and biochemical measures. Ann Physiol Anthropol 1992; 11: 345-356.
Banister EW, Carter JB, Zarkadas PC. Training theory and taper: validation in triathlon athletes. Eur J Appl Physiol 1999; 79: 182-191.
Busso T, Hakkinen K, Pakarinen A, et al. A systems model of training responses and its relationship to hormonal responses in elite weight-lifters. Eur J Appl Physiol 1990; 61: 48-54.
Busso T, Carasso C, Lacour JR. Adequacy of a systems structure in the modeling of training effects on performance. J Appl Physiol 1991; 71: 2044-2049.
Busso T, Hakkinen K, Pakarinen A, et al. Hormonal adaptations and modelled responses in elite weightlifters during 6 weeks of training. Eur J Appl Physiol 1992; 64: 381-386.
Busso T, Candau R, Lacour JR. Fatigue and fitness modelled from the effects of training on performance. Eur J Appl Physiol 1994; 69: 50-54.
Busso T, Denis C, Bonnefoy R, et al. Modeling the adaptations to physical training by using a recursive least squares algorithm. J Appl Physiol 1997; 82: 1685-1693.
Busso T, Benoit H, Bonnefoy R, et al. Effects of training frequency on the dynamics of performance response to a single training bout. J Appl Physiol 2002; 92: 572-580.
Busso T. Variable dose-response relationship between exercise training and performance. Med Sci Sports Exerc 2003; 35: 1188-1195.
Calvert TW, Banister EW, Savage MV, et al. A systems model of the effects of training on physical performance. IEEE Trans Syst Man Cybern 1976; 6: 94-102.
Chatard, J.C., & Mujika, I.T. (1999). Training load and performance in swimming. In K.L. Keskinen, P.V. Komi, & A.P. Hollander (Eds.), Biomechanics and Medicine in Swimming VIII (pp. 429-434). Jyväskylä: University Press (Gummerus Printing).
Fitz-Clarke JR, Morton RH, Banister EW. Optimizing athletic performance by influence curves. J Appl Physiol 1991; 71: 1151-1158.
Hellard P, Avalos M, Millet G, et al. Modeling the residual effects and threshold saturation of training: a case study of Olympic swimmers. J Strength Cond Res 2005; 19: 67-75.
Hellard P, Avalos M, Lacoste L, et al. Assessing the limitations of the Banister model in monitoring training. J Sport Sci 2006; 24: 509-520.
Hooper, S.L.; Mackinnon, L.T. (1999). Monitoring regeneration in elite swimmers. In M. Lehmann, C. Foster, U. Gastmann, H. Kaizer, & J.M. Steinacker (Eds.), Overload, Performance, Incompetence and Regeneration in Sport (pp. 139-148). New York: Kluwer Academic/Plenum Publishers.
le Bris S, Ledermann B, Topin N, et al. A systems model of training for patients in phase 2 cardiac rehabilitation. Int J Cardiol 2005l 19: [Epub ahead of print]
Millet GP, Groslambert A, Barbier B, et al. Modelling the relationships between training, anxiety, and fatigue in elite athletes. Int J Sports Med 2005; 26: 492-498.
Millet GP, Candau RB, Barbier B, et al. Modelling the transfers of training effects on performance in elite triathletes. Int J Sports Med 2002; 23: 55-63.
Morton RH, Fitz-Clarke JR, Banister EW. Modeling human performance in running. J Appl Physiol 1990; 69: 1171-1177.
Morton RH. Modeling training and overtraining. J Sport Sci 1997; 15: 335-340.
Mujika I, Busso T, Lacoste L, et al. Modeled responses to training and taper in competive swimmers. Med Sci Sports Exerc 1996; 28: 251-258.
Mujika, I. T.; Busso, T.; Geyssant, A.; Chatard, J. C.; Lacoste, L. and Barale, F. (1996). Modeling the effects of training in competitive swimming. In: J.P. Troup, A.P. Hollander, D. Strasse, S.W. Trappe, J.M. Cappaert, & T.A. Trappe (Eds.), Biomechanics and Medicine in Swimming VII (pp. 221-228). London: E&F Spon.
Taha T, Thomas SG. Systems modelling of the relationship between training and performance. Sports Med 2003; 33: 1061-1073.
Zarkadas PC, Carter JB, Banister EW. Modelling the effects of taper on performance, maximal oxygen uptake, and the anaerobic threshold in endurance triathletes. Adv Exp Med Biol. 1995; 393:179-186.
Quote:
Originally Posted by An old Guy .
I am able to find papers that find problems. You appear to not understand Dr. Banister's work well enough
I've discussed the limitations to the impulse-response model in some detail here:
http://home.trainingpeaks.com/articles/cycling/the-science-of-the-performance-manager.aspx
Quote:
Originally Posted by An old Guy .
You need to find a new religion.
Science is my religion.
BTW, those are only (and I believe essentially all of, at least at the time I wrote that article) the studies that have used the impulse-response model, or some variation thereof. There is a smattering of other papers out there that have used other approaches (e.g., neural networking), but I didn't cite them since they weren't directly relevant.
On a semi-related note (for those who might wish to dig into things a bit more): Here is a list of peer-reviewed papers that have cited my applied "research" (in quotes, since I consider it a hobby), even though I've never published any of it in a scientific journal:
Abiss CR, Quod MJ, Martin, Netto KJ, Nosaka K, Lee H, Suriano R, Bishop D, Laursen PB. Dynamic pacing strategies during the cycle phase of an Ironman triathlon. Med Sci Sports Exerc 2006; 38:726-734.
McGregor SJ, Weese RK, Ratz IK. Performance modeling in an Olympic 1500-m finalist: a practical approach. J Strength Conf Res 2009; 23:2515-2523.
Gregory CM, Doherty AR, Smeaton AF, Warrington GD. Correlating multimodal physical sensor information biological analysis in ultra endurance cycling. Sensors 2010; 10:7216-7235.
Garvican LA, Martin DR, McDonald W, Gore CJ. Seasonal variation of haemoglobin mass in internationally competitive female road cyclists. Eur J Appl Physiol 2010; 109:221-231.
Francis JT Jr, Quinn TJ, Amann M, Laroche DP. Defining intensity domains from the end power of a 3-min all-out cycling test. Med Sci Sports Exerc 2010; 42:1769-1775.
Robinson ME, Plasschaert J, Kisaalita NR. Effects of high intensity training by heart rate or power in recreational cyclists. J Sports Sci Med 2011; 10:498-501.
Garvican LA, Pottgiesser T, Martin DT, Schumacher YO, Barras M, Gore CJ. The contribution of haemoglobin mass to increases in cycling performance stimulated by LHTL. Eur J Appl Physiol 2011; 111:1089-1101.
Cowell JF, Cronin JB, McGuigan MR. Time motion analysis of Supercross BMX racing. J Sports Sci Med 2011; 10:420-421.
Cowell JF, McGuigan MR, Cronin JB. Movement and skill analysis of Supercross BMX. J Strength Cond Res Publish Ahead of Print 2012 (DOI: 10.1519/JSC.0b013e318234eb22)
Abiss CR, Quod MJ, Martin, Netto KJ, Nosaka K, Lee H, Suriano R, Bishop D, Laursen PB. Dynamic pacing strategies during the cycle phase of an Ironman triathlon. Med Sci Sports Exerc 2006; 38:726-734.
McGregor SJ, Weese RK, Ratz IK. Performance modeling in an Olympic 1500-m finalist: a practical approach. J Strength Conf Res 2009; 23:2515-2523.
Gregory CM, Doherty AR, Smeaton AF, Warrington GD. Correlating multimodal physical sensor information biological analysis in ultra endurance cycling. Sensors 2010; 10:7216-7235.
Garvican LA, Martin DR, McDonald W, Gore CJ. Seasonal variation of haemoglobin mass in internationally competitive female road cyclists. Eur J Appl Physiol 2010; 109:221-231.
Francis JT Jr, Quinn TJ, Amann M, Laroche DP. Defining intensity domains from the end power of a 3-min all-out cycling test. Med Sci Sports Exerc 2010; 42:1769-1775.
Robinson ME, Plasschaert J, Kisaalita NR. Effects of high intensity training by heart rate or power in recreational cyclists. J Sports Sci Med 2011; 10:498-501.
Garvican LA, Pottgiesser T, Martin DT, Schumacher YO, Barras M, Gore CJ. The contribution of haemoglobin mass to increases in cycling performance stimulated by LHTL. Eur J Appl Physiol 2011; 111:1089-1101.
Cowell JF, Cronin JB, McGuigan MR. Time motion analysis of Supercross BMX racing. J Sports Sci Med 2011; 10:420-421.
Cowell JF, McGuigan MR, Cronin JB. Movement and skill analysis of Supercross BMX. J Strength Cond Res Publish Ahead of Print 2012 (DOI: 10.1519/JSC.0b013e318234eb22)
And just to drive those points home (y'all might want to bookmark this post for future reference! /img/vbsmilies/smilies/wink.gif):
1) While the impulse-response model can be used to accurately describe changes in performance over time, it has not been possible to link the structure of the model to specific, training-induced physiological events relevant to fatigue and adaptation, e.g., glycogen resynthesis, mitochondrial biogenesis. In that regard the model must be considered purely descriptive in nature, i.e., largely a “black box†into which it is not possible to see. Although this by no means invalidates the approach, being able to relate the model parameters (in particular, the time constants τa and τf (or τ1 and τ2)) to known physiological mechanisms would allow the model to be applied with greater confidence and precision.
2) The impulse-response model essentially assumes that there is no upper limit or upper bound to performance, i.e., a greater amount of training always leads to a higher level of performance, at least once the fatigue due to recent training has dissipated. In reality, of course, there will always be some point at which further training will not result in a further increase in performance, i.e., a plateau will occur. This is true even if the athlete can avoid illness, injury, overtraining, or just mental “burn outâ€. While Busso et al. have proposed a modification to the original model that explicitly recognizes this fact and which results in a slightly improved fit to actual data, this modification further increases the mathematical complexity and requires an even larger amount of data be available to solve the model (see below).
3) To obtain a statistically valid fit of the model parameters to the actual data, it is necessary to have multiple, direct, quantitative measurements of performance. The exact number depends in part on the particular situation in question, but from a purely statistical perspective somewhere between 5 and 50 measurements per adjustable parameter would generally be required. Since the model has four adjustable parameters, i.e., τa and τf (or τ1 andτ2) and ka and kf (or k1 and k2), this would mean that performance would have to be directly measured between 20 and 200 times in total. Moreover, since the model parameters themselves can change over time/with training (see more below), these measurements should all be obtained in a fairly short period of time. Indeed, Banister himself has suggested revisting the fit of the model to the data every 60-90 d, which in turn would mean directly measuring an athlete’s maximal performance ability at least every 4th day, if not several times per day. Obviously, this is an unrealistic requirement, at least outside of the setting of a laboratory research study.
4) Even when an adequate number of performance measurements are available, the fit of the model to the data is not always accurate enough for the results to be helpful in projecting future performance (which is obviously necessary to be able to use the impulse-response model to plan a training program). In other words, even though an adequate R2 might be obtained with a particular combination of parameter estimates, the parameter estimates themselves may not always be sufficiently stable, or certain, to be enable highly reliable prediction of future performance. This seems to be particularly true in cases where the overall training load is relatively low, in which case the addition of the second, negative term to the model often does not result in a statistically significant improvement in the fit to the data, i.e., the model can be said to be overparameterized. In other studies in the literature, the parameter estimates that provide the best mapping of training (i.e., the input function to the model) to performance (i.e., the output of the model) fall precisely on the constraints imposed when fitting the model, i.e., the model has essentially been forced to fit the data. Again, while this is not necessarily an invalid approach, it suggests that either the model structure is inadequate (even if it the best available choice) to truly describe the data, or that the data themselves are too variable or “noisy†to be readily fit by the model.
5) As illustrated by the data shown in Table 1, the values reported in the literature for τa (or τ1) are quite consistent across studies, at least when one considers a) the wide variety of sports that have been studied (and hence the wide variety of training programs that have been employed), and b) that the model is relatively insensitive to changes in τa (or τ1 ) (i.e., increasing or decreasing τa (or τ1) by 10% changes the output of the model by <5%). Moreover, the interindividual variation in τa (or τ1) is relatively small, as indicated by the magnitude of the standard deviation compared to the mean value in each case. On the other hand, the values obtained for τf (or τ2) do vary significantly across studies, and, to a somewhat lesser extent, also within a particular study (i.e., between individuals). However, these variations in τf (or τ2) appear to be due, in large part, to differences in the overall training load. This effect is especially evident in the study of Busso (2003), in which increasing the training load (by increasing the frequency of training from 3 to 5 d/wk while holding all other aspects constant) resulted in ~33% increase in τf (or τ2). In addition, the degree to which the training might be expected to result in significant muscle damage also appears to play a role. For example, the value for τf (or τ2) obtained in the study of Morton et al. (1990), which involved running, is comparable to that found in the study of swimmers by Iñigo et al. (1996), despite the much smaller total training load in the former study. Indeed, the highest value for τf (or τ2) reported in the literature appears to be 22±4 d in a study of elite weight-lifters, with this extreme value presumably reflecting both the nature and magnitude of the training load of such athletes. Thus, although τf (or τ2) is more variable thanτa (or τ1), this variability appears explainable. In contrast, it is harder to explain the variability obtained in different studies for the gain terms of the impulse-response model, i.e., ka and kf (or k1 and k2). In part, this is because these values serve not only to “balance†the two integrals in Eq. 1, but also to quantitatively relate the training load to performance in an absolute sense. In other words, for the same set of data/for the same individual, the values for ka and kf (or k1 and k2) would be different if performance were, for example, defined as the power that could be maintained for 1 min versus 60 s, or if training were quantified in kilojoules of work accomplished instead of TRIMP. It is clear, however, that this is not the only explanation for the variation in ka and kf (or k1 and k2) between studies, as even their ratio varies significantly, with this variation seemingly unrelated to factors such as the overall training load. For example, the ratio of kf to ka (or k2 to k1) in the study of Busso et al. (1997) is comparable to that found by Hellard et al. (2005), despite the large difference type and amount of training. Moreover, as indicated by the standard deviations listed in the last three columns of Table 1, the variability in kaand kf (or k1 and k2) between individuals in a given study is as large, or even larger, than the variation across studies. Because of this variability, it is difficult, if not impossible, to rely on generic values for ka and kf (or k1and k2) from the literature to overcome the limitations discussed under points 3 and 4 above. This is especially true given the fact that the impulse-response model is more sensitive to variations in these gain factors than it is to variation in the time constants, especially τa (or τ1).
Table 1. Representative studies from the literature that have used the impulse-response model.
Note: The time constants Ï„a and Ï„f (or Ï„1 and Ï„2) are measured in days, whereas the units of the gain factors ka and kf (ork1 and k2) vary from study to study depending on how training and performance were quantified.
You really have to stay on point here. I made no comments on the idea of impulse-response. I made comments on Dr. Banister's model of impulse-response. His model is defective. The fact that you can list a series of papers is not remarkable. What is remarkable is that you cannot find a paper that points out that Dr. Banister's model make outrageous predictions. That is an important paper. As I said before one cannot prove a theory; one can only disprove it.
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I find impulse-response to be a reasonable idea. Dr. Banister's model has problems. Your model of it, TSS, has serious problems.
Let me recap what you have said here and in the past.
You have said: TSS is best thought of as a indicator of glycogen depletion. You have also said: not so. But you have not indicated any other "better thought."
The basic idea of TSS is an implementable model of impulse-response. You have no data to show it models anything.
You say that TSS does not predict performance. My understanding is that Dr. Banister's model is good enough to compare proposed training schedules and determine if one is superior to another. True or false does not matter here. The importance here is that one cannot use TSS in that manner.
You say that no amount of proof a single person could amass would disprove TSS (actually you switched words and used NP.) But you keep asking for my numbers. As though you would accept my numbers as a valid disproof.
I have provided you with workouts that shoudl cause you to doubt TSS, but you dismiss them.
You claim TSS is better than any other easily implemented method but you provide no proof. It should be easy to compare TSS averages (CTL; ATL) to something like boxcar averages of daily work or even riding time and show that TSS is better. But since TSS is not predictive, what doe better mean?
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I just don't know how to deal with a religious zealot like you.
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stevel ----
I missed your post. So I said the same thing on the next page.
It is nice to see someone who understands some of this.
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I find impulse-response to be a reasonable idea. Dr. Banister's model has problems. Your model of it, TSS, has serious problems.
Let me recap what you have said here and in the past.
You have said: TSS is best thought of as a indicator of glycogen depletion. You have also said: not so. But you have not indicated any other "better thought."
The basic idea of TSS is an implementable model of impulse-response. You have no data to show it models anything.
You say that TSS does not predict performance. My understanding is that Dr. Banister's model is good enough to compare proposed training schedules and determine if one is superior to another. True or false does not matter here. The importance here is that one cannot use TSS in that manner.
You say that no amount of proof a single person could amass would disprove TSS (actually you switched words and used NP.) But you keep asking for my numbers. As though you would accept my numbers as a valid disproof.
I have provided you with workouts that shoudl cause you to doubt TSS, but you dismiss them.
You claim TSS is better than any other easily implemented method but you provide no proof. It should be easy to compare TSS averages (CTL; ATL) to something like boxcar averages of daily work or even riding time and show that TSS is better. But since TSS is not predictive, what doe better mean?
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I just don't know how to deal with a religious zealot like you.
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stevel ----
I missed your post. So I said the same thing on the next page.
It is nice to see someone who understands some of this.
Actually it isn't. He made a couple of claims: That a couple of hours consisting of intervals that are equal length work/rest at an excessive power output (around 150% FTP) is easy. The proof - make the data from such a session available. He's made statements in the past referring to power levels so arriving at a guesstimated ballpark from classics like "I was climbing a hill easily at 280watts and a rider passed me. I sipped on some water and caught him at the top. He stopped and I rode to the store for a quart of chocolate milk." At least an hour of 450 watt intervals... and there's enough number crunchers on here to check for "editing"SteveI said:
That TSS is flawed. I'm sure there's a way to prove this somehow - but do we care? The claim made above is much more interesting. If he has WKO+ a suitable graph could be obtained.Similar threads
