The Performance Manager Chart in WKO and TrainingPeaks

In the Fall of 2004, I attended USA Cycling's Coaches Summit where I had the pleasure of seeing Dr. Andy Coggan, Ph.D present on training with power. I had seen Andy present before but during his talk he began to present on a impulse-response model he was working on.

At the end of Andy's talk, I asked how I could use the model and a few hours later a complicated spreadsheet showed up in my inbox. I scratched my head and poured over it. A week later I joined a group of 12 or so athletes, coaches and sports scientists as beta testers for this 3rd generation power-based impulse-response model.

At the end of Andy's talk, I asked how I could use the model and a few hours later a complicated spreadsheet showed up in my inbox. I scratched my head and poured over it. A week later I joined a group of 12 or so athletes, coaches and sports scientists as beta testers for this 3rd generation power-based impulse-response model.

As a beta tester, I figured the best way to understand the complexities of the model was to use my own data. Everyday I returned home with great enthusiasm to download my SRM power data and analyze how each day's TSS affected the model, my power output & performance, plus how it felt.

It wasn't long before the beta testers all got together online and began discussing every aspect of their training and their subsequent all time best performances. Soon I began planning out my training using a TSS for each day of training and using the model to peak for the Colorado State Time Trial. The end result was a performance of a lifetime (a true peak), which I am very proud of to this day.

The following month in July I gave PEZCyclingNews the first look at the model in an article entitled "Finding Form: A Power-Based Performance Model". At the time, the model was not available to the public and rather than try to tiptoe around what it was and how it worked, I decided to hype the model a tad while describing the broader purpose - how to use it to manage your training load (CTL or chronic training load) to achieve a peak performance (TSB or training stress balance) - and how it led to my successful performance at the CO State Time Trials that year.

Also during this time, I was reading Daniel Coyle's "Lance Armstrong’s War". Since I was a time trial enthusiast, I liked the way Armstrong called his F One equipment "The Shit that Will Kill Them".

And so when I was writing about the model for my monthly PEZ training article it came to me, this model is The Shit that Will Kill Them. It truly is our secret weapon that we (mostly Andy) have/has developed which we are using to our competitive advantage. In the aftermath of the article someone, somewhere in one of the online power based web forums began using the acronym TSTWKT. And for the short period of time the name stuck I quietly enjoyed how it caught on. To this day TSTWKT is printed on the back of the collar of the FasCat Jerseys.

These days the model is called the Performance Manager Chart or PMC.  I am proud of the work I did as a beta tester. What I learned from the model (and continue to do so) forms the foundation of my philosophy as a coach. Not all of it, but a large, large portion. If you are serious about training and going big for a particular event start using the PMC.

If you would like to learn more about the model, below is a good start. The best way to learn how to use the model in my opinion is with your own training data or perhaps your athlete's training data.  There are three aspects to understand while you are using it:

1. CTL/ATL/ and your "Form" aka TSB

2. Fatigue and its relationship to power output and that relationship to CTL/ATL/TSB

3. How you or your athlete 'feels', also in relation to # 's 1 & 2 above.

Below is what started out as a FAQ and then turned into a Glossary which as of now doesn't even cover everything. I don't know if it ever will. For further reading I suggest starting with Tim Taha's graduate thesis review linked below on Systems Modelling of the Relationship between Training and Performance. Enjoy!

Philosophy
The relationship between an athlete's training load (or CTL, see below) and his or her athletic performance is one of the most basic principles of training. Without enough training, the athlete will under perform. However, after too much training, the athlete will also under perform. I like to compare this to an anesthesiologist and their job in the surgical room. I was originally turned on to this relationship way back in the day by a graduate student by the name of Allen Lim. You may have seen his microwave popcorn slide. I understood the principle but until the PMC came along, I never grasped how to put the principle into practice.

Peak athletic performance is a slippery slope and occurs with the optimal amount of training load. Prescribing just the right "dose" of training, like Goldilocks, is the key to peak athletic performance and the holy grail for athletes, coaches and sports scientists.

TSTWKT helps the user figure out exactly what that Goldilocks dose of training is. Furthermore, the model helps plan for peak performances using CTL - ATL = TSB

TRIMPS
Acronym for TRaining IMPulseS originally described by Dr. Eric Banister in his 1975 publication titled "A systems model of training for athletic performance". Banister's heart rate based model was popularized by multisport athletes for years adding further evidence to the robust-ness of the model's prediction of performance.

TRIMPS = exercise duration x average heart rate

Banister's model describes the use of TRIMPS to quantify an athlete's training load and measure the impulse:

Thierry Busso et. al
In 1990, the French physiologist Thierry Busso began publishing his work on a system model of training responses. Seven years later, Busso published data validating the systems model with time varying parameters "for describing the responses of physical performance to training".

Of particular interest is the way in which Busso and his colleagues quantified the training load or impulse used in their study:

Number of intervals performed x weighted intensity effort (power output / P lim 5' x 100) For example, four 5 minute intervals performed at 85% of P lim 5' was calculated by 4 x 85 = 340 training units.

Compared to TSS, you'll notice that Busso's method for quantifying the training load is rather rudimentary.

Using TSS (Training Stress Score, see below) rather than heart rate data or training units as Busso did, Dr. Andy Coggan and the Training Manager beta testers developed a third generation power based impulse response model.

TSTWKT/PMC users will now be able to model their training and track their performance by using their daily TSS as the "impulse" to quantify their overall training load. The training manager and model takes the impulse and uses the algorithms previously described in the literature to predict performance in terms of the metric TSB (see below) or the response to the training.

It is important to recognize that the Training Manager is a mathematical model which does not account for specificity of training adaptations. Just like meteorologists use models to predict the path of hurricanes we are using this model to predict performance. But with all models there is a certain "art" to go along with the science.

Part of the so called "art" lies in how to interpret and apply the model to the data, the athlete and race results being produced. It is up to the athlete, coach, or sports scientist to correlate that prediction of performance, TSB, with actual race performance along with various length peak power outputs.

Ultimately the model may be used to control the athlete's periodization. Or more simply to plan and guide the user to 'peak' on or around a specific date or event.

Chronic Training Load (CTL)
How much an athlete has been training historically. Also known as an athlete's "training load". CTL represents the positive gain "ascribed to training adaptations". Since CTL is the stating point of the IR model it is often compared to an athlete's fitness. For example, an athlete who has achieved a CTL of 110 will be able to achieve greater TSB.

In terms of power output, "fitness" and race performance, the larger an athlete's CTL (** see CTL range below), the better poised the athlete will be to achieve a greater TSB. "Poised" being the key word because there's a slew of disclaimer's.

The CTL constant to set your Performance Manager Chart to in TrainingPeaks is 42.  42 is supported in the literature and is the 1/2 life of training. In other words if your CTL is 100 and you didn't train for 42 days your CTL would be 50.

Acute Training Load (ATL)
How much an athlete has been training recently. ATL represents the negative gain in the systems model that is associated with exercise fatigue.

The ATL constants to set your Performance Manager Chart to in TrainingPeaks is between 3 - 7.  Lower for younger athletes that recover faster and higher for older athletes that need more time to recover.

Training Stress Balance (TSB)
Synonym to the popularized term "form". TSB is calculated by subtracting ATL from CTL.. TSB is the "response" from the impulse-response model. Athletes may correlate race performance and specific length power outputs to their TSB.

Training Stress Score (TSS)
= exercise duration x normalized power x Intensity Factor^2

TSS is the "impulse" in the I-R model.

A superior measure of overall training load. Compare TSS to heart rate data and its known limitations; then compare TSS to Busso's method of quantifying training load.

Time Constant
42 days.  In other words the half life of training is 42 days.  If you have a CTL of 100 and don't train at all, in 42 days your CTL will be 50.

**Optimal CTL Range
An athlete's optimal CTL range is going to be highly dependent on the athlete plus the amount of time he or she has to train!

Masters Athletes need to achieve/maintain CTL's between 75 - 100*

Professional Athletes need to achieve/maintain CTL's between 90 - 150**

There is such a thing as too much training and therefore too much CTL. To high a CTL deadens the legs and takes away from peak power outputs that win races.

*Over the past 15 years of using the model, in my experience from coaching athletes and my own data and performances, I have come to understand that athletes over 40 years old want to maintain CTLs under 100 even if they have the time to go higher.

** 150 is extremely high, grand tour riders will achieve 150-170's coming out of the mountainous stages of a grand tour. See Christian van Velde's 2006 Tour de France data modeled out in TSTWKT

TSB Event Specificity
This is a developing art like the rest of the model. The current thinking is that shorter events like criteriums and track events may warrant higher TSB whereas longer events such as road races or even ultra endurance events may favor a lower TSB in exchange of "retaining" CTL.

"Sweet Spot"
Originally described by Frank Overton in the Pez Cycling News training tip, sweet spot training is an effective training method to raise an athlete's CTL.

Adjusting & customizing time constants for athletes relative to their total training load

This area of the model is also a developing art. However we are implementing varying time constants based on the athlete's total training load as defined by CTL. We are suggesting that your ATL time constant may be shorter for lower CTL's and longer for greater CTL's. Individuals will vary but a good starting point is a 5d or 7d time constant. The athlete's age matters too: masters athletes have a greater ATL time constant than a 25 year old because it takes the older athlete longer to recover.

CTL Reload or "Reload"
After an athlete has managed his training to peak, he or she will have given up CTL. In order to build for a second peak in the second half of the season, that athlete will need to "reload" his CTL. That period, build, or phase is known as a CTL reload.

CTL Composition
It is important to recognize that the fundamentals of endurance training have not changed. A CTL of 120 composed of entirely level 2 rides will not result in the same performance as a CTL of 120 obtained with a well thought out scientifically designed training plan consisting of various levels of intensity.

CTL Maintenance
When the goal of an athlete's training is to increase their CTL (in a build, for example) there are days when you too fatigued to train hard but not fatigue enough to warrant laying on the couch. A prime example of a "CTL maintenance" ride is going out for a couple of hours in Zones 1 & 2. The end results is a CTL that neither drops nor increases but is poised to continue the upward build when the athlete is recovered the following day.

The Shit that will Kill Them (TSTWKT)*
Lance Armstrong's description of his one of a kind high tech equipment developed by the F-One project. When it comes to training with power, the "Training Manager" is the Shit that will Kill Them".

"Coming up for air"
A term related to an athlete's TSB becoming positive after a prolonged period of training and consequently negative values. An athlete will "come up for air" by taking the appropriate amount of rest & recovery following an hard training block. As the model predicts the athlete will experience good legs and similarly higher power outputs that validate his or her TSB.

References & Recommended Reading:
Avalos M, Hellard P, Chatard JC. Modeling the training-performance relationship using a mixed model in elite swimmers. Med Sci Sports Exerc 2003; 35: 838-846.

Banister, E.W.; Calvert, T.W.; Savage, M.V.; and Bach, T.M. A systems model of training for athletic performance. Aust. J. Sports Med 7:57-61, 1975

Banister EW, Calvert TW. Planning for future performance: implications for long term training. Can J Appl Sport Sci 1980; 5: 170-176.

Banister EW, Hamilton CL. Variations in iron status with fatigue modeled 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.; Benoit, H.; Bonnefoy, R.; Feasson, L.; and Lacour, J.R. Effects of training frequency on the dynamics of performance response to a single training bout. J Appl Physiol 92: 572-580, 2002

Busso, T.; Denis D.; Bonnefoy, R.; Geyssant, A.; and Lacour, J.R. Modeling of adaptations to physical training by using a recursive least squares algorithm. J Appl Physiol 82: 1685-1693, 1997

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.

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.

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 runners. 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 competitive 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 modeling 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.

* from Daniel Coyle's "Lance Armstrong’s War", Harper Collins, 2005

Copyright © 2021 FasCat Coaching - all rights reserved.

Join our *FREE* Athlete Forum to nerd out with FasCat coaches and athletes about your FTP, race data, power based training, or anything related to going fast on the bike!