Introduction
Knowledge of the relationship between the volume
and intensity of training on the one hand and the resulting improvements in
performance on the other is obviously critical when attempting to design the
optimal training program, i.e., one that maximizes an individual’s performance
ability at the time of key events while also avoiding illness, injury, or
overtraining. To understand this relationship, most coaches and athletes rely
upon some combination of tradition (i.e., knowing what has worked previously
for others), empiricism (i.e., trial-and-error experimentation), and the
application of basic training principles (e.g., the overload principle). In a
number of scientific studies, however, this relationship has been investigated
in a more direct, quantitative manner (see Bibliography). These studies
have used a wide variety of mathematical approaches, ranging from simple linear
regression to complex neural networking. By far the most common approach,
however, has been to use what is typically referred to as the impulse-response
model. First proposed by Banister et al. in 1975, this model, and/or variations
thereof, has been repeatedly shown to accurately predict training-induced
changes in performance (both positive and negative) in a wide variety of
endurance and non-endurance sports (see below). The impulse-response model has
therefore been successfully used to design theoretically-ideal training
programs, optimize tapering regimens, evaluate the effects (or lack thereof) of
cross-training in triathletes, etc. As will be discussed, however, this model
also has a number of inherent limitations, which tend to limit its usefulness
outside of a laboratory setting. The purpose of the present article is
therefore to describe a somewhat simpler approach, termed the Performance
Manager, that was developed by the present author, and in particular to explain
the etiology of this idea in the context of the impulse-response model. While
some information about how to best use this analytical tool is included,
readers should also see this article by Hunter Allen for more information on this aspect of the topic. For
information about how to actually create the Performance Manager chart in WKO+,
please follow this link http://www.cyclingpeakssoftware.com/power411/howtoperformancemanager.asp
Banister’s impulse-response model: theory, applications,
and limitations
In the impulse-response approach, the
quantitative relationship between training and performance is modeled as a
transfer function, the input to which is the daily “dose” of training (i.e.,
combination of volume and intensity) and the output of which is the
individual’s actual performance. The transfer function describing the behavior
of the system (i.e., the athlete) is composed of two first order filters, one
representing the more long lasting (or chronic) positive adaptations to
training, which result in an increase in performance ability, and the other
representing the more short term (or acute) negative effects of the last
exercise bout(s), i.e., fatigue, which result in a decrease in performance
ability. The time course of changes in performance in response to repeated
bouts of training is therefore described by equation 1 below:
Equation 1. The impulse-response model of Banister et al.
where pt is the performance at any time t,
p0 is the initial performance, ka and kf
(or k1 and k2) are gain terms relating the
magnitudes of the positive adaptive and negative fatigue effects (and also serving
to convert the units used to quantify training to the units used to quantify
performance), τa and τf (or τ1
and τ2) are time constants describing the rate of decay
of the positive adaptive and negative fatigue effects, and ws
is the daily dose of training. The model therefore has four adjustable
parameters, i.e., ka and kf (or k1
and k2) and τa and τf
(or τ1 and τ2), which are
constrained such that ka<kf (or k1<k2)
and τa>τf (or τ1>τ2).
The best-fit solution to the model is determined iteratively, i.e., by
repeatedly measuring both the daily dose of training and the resulting
performance, then adjusting the values of these parameters to result in the
closest correspondence between the model-predicted and actual performances. Figure
1 below graphically illustrates the effects of a single bout of training on
performance resulting in a TSS of 100 as predicted by this model. As can be
seen in this figure, performance (i.e., the difference between the two terms of
the equation above) is initially predicted to be diminished or degraded, due to
the acute, negative influence of training. As this effect wanes, however, the
positive adaptations to training begin to dominate, such that performance is
eventually improved.
Figure 1. Effect of a single bout of training (TSS = 100) on
fitness, fatigue, and performance as predicted by the impulse-response model. τa
and τf (or τ1 and τ2)
were assumed to be 42 and 7 d, respectively, whereas ka and kf
(or k1 and k2) were assumed to equal 1 and
2, respectively.
The impact of repeated bouts of training on
performance is then the summation of such individual impulses, with the
ultimate effect (i.e., when, or even whether, training results in an increase
or decrease in performance, and the extent to which this is true) depending on
the magnitude and timing of each “dose” of training. This is illustrated in
Figure 2, which depicts the response to a sustained increase in daily training
to 100 TSS/d:
Figure 2. Changes in fitness, fatigue, and performance as
predicted using the impulse-response model. The daily training load was assumed
to increase on January 1 from 0 to 100 TSS/d. Model parameters as in Fig. 1
As indicated previously, the above-described
model has been successfully applied to a number of different sports, e.g.,
weight lifting, hammer throwing, running, swimming, cycling, triathlon, and has
been shown to account for >70%, and often >90%, of the day-to-day
variation in performance (i.e., the R2 between the predictions of
the model and the actual data is typically >0.7 and often >0.9). Moreover,
the model has also been shown to accurately predict changes in
performance-related parameters considered indicative of training (over)load
and/or adaptation, such as serum hormone (e.g., testosterone) or enzyme (e.g.,
creatine kinase) levels, or psychological measures of anxiety or perceived
fatigue. The impulse-response model has therefore been used to optimize training/tapering
regimens, evaluate the impact of training in one sport (e.g., running) on
performance in another (e.g., cycling), etc. In most of these studies, the
metric used to track training load has been Banister’s heart rate based
“training impulse” (TRIMP) score, but other ways of quantifying training have
also been used (especially in studies of non-endurance sports, but also, e.g.,
for swimming), and, roughly speaking, the model appears to work equally well
regardless of precisely how training has been quantified.
Given the rather robust behavior described
above, the impulse-response model would appear to be a highly useful tool for
coaches and athletes wishing to maximize their probability of success in
competition, and some prominent national team programs in cycling have
attempted to exploit this approach. There are, however, a number of limitations
to the impulse-response model, some of which may be only academic in nature,
but others of which clearly tend to limit its usefulness in a practical sense:
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 out”. 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 and τ2) 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 than τa (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 ka and 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 k1 and 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 τa and τf
(or τ1 and τ2) are measured in
days, whereas the units of the gain factors ka and kf
(or k1 and k2) vary from study to study
depending on how training and performance were quantified.
The Performance Manager concept: part science, part art
Given the relative complexity of the
impulse-response model (at least from the perspective of most non-scientists)
and the limitations discussed above, many are tempted to simply dismiss the
approach out-of-hand as unbelievable “black magic”, or at the very least, as too
unpractical for routine use. To do so, however, is to willfully ignore
potentially valuable knowledge that has been gained via such scientific
research into the quantitative relationship between training and performance. Specifically,
even though the impulse-response model may be purely descriptive in nature,
studies using this approach have provided important insight into the effective
time course of recovery from, and adaptation to, exercise training. Failure to
apply this information when planning and evaluating training programs, tapering
schemes, etc., means failure to take maximum advantage of available knowledge. Or,
to put it another way: if, as Dave Harris has put it, the body responds to
training “like a Swiss watch”, then logically this information could, and
should, be put to use by coaches and athletes wishing to maximize performance. The
problem, however, is how to do so in a manner that is consistent with the
results of this previous scientific research, yet is still simple enough to be
used and applied in a real-world setting.
The above considerations led the present author
to search for a practical way of applying the lessons learned via research
using the impulse-response model to data obtained using a powermeter. The
starting point for this search was recognition of the simple fact that
performance is typically greatest when training is first progressively
increased to a very high level to build fitness, after which the athlete
reduces their training load, i.e., tapers, to eliminate residual fatigue. Or,
to put it more simply, “form equals fitness plus freshness”. With this
perspective in mind, it was recognized that eliminating the gain factors ka
and kf (or k1 and k2)
from the impulse-response model would solve two problems simultaneously: 1) it would
remove any uncertainty regarding the precise values to use (with the price
being that interpreting the results of the calculations becomes as much a
matter of art as it is of science – see more below), and 2) it would allow
substitution of simpler exponentially-weighted moving averages for the more
complex integral terms in the original equation (because at least qualitatively
they behave the same way). Based on this logic, the components of the Performance
Manager were defined, i.e.,:
1)
Chronic training load, or CTL, provides a measure of how much an athlete
has been training (taking into consideration both volume and intensity)
historically, or chronically. It is calculated as an exponentially-weighted
moving average of daily TSS (or TRIMP, etc.) values, with the default time
constant set to 42 d. CTL can therefore be viewed as analogous to the positive
effect of training on performance in the impulse-response model, i.e., the
first integral term in Eq. 1, with the caveat that CTL is a relative
indicator of changes in performance ability due to changes in fitness, not an
absolute predictor (since the gain factor, ka (or k1), has been
eliminated).
2)
Acute training load, or ATL, provides a measure of how much an athlete
has been training (again, taking into consideration both volume and
intensity) recently, or acutely. It is calculated as an exponentially-weighted
moving average of daily TSS (or TRIMP, etc.) values, with the default time
constant set to 7 d. ATL can therefore be viewed as analogous to the negative
effect of training on performance in the impulse-response model, i.e., the
second integral term in Eq. 1, with the caveat that ATL is a relative
indicator of changes in performance ability due to fatigue, not an absolute
predictor (since the gain factor, kf (or k2),
has been eliminated).
3)
Training stress balance, or TSB, is, as the name suggests, the
difference between CTL and ATL, i.e., TSB = CTL – ATL. TSB provides a measure
of how much an athlete has been training recently, or acutely, compared to how
much they have been training historically, or chronically. While it is tempting
to consider TSB as analogous to the output of the impulse-response model, i.e.,
as a predictor of actual performance ability, the elimination of the gain
factors ka and kf (or k1
and k2) means that it is really better viewed as an indicator
of how fully-adapted an individual is to their recent training load, i.e., how
“fresh” they are likely to be.
Thus,
within the logical constructs of the Performance Manager, performance depends
not only on TSB, but also on CTL (in keeping with saying that “form equals
fitness plus freshness”). The “art” in applying the Performance Manager
therefore lies in determining the precise combination of TSB and CTL that
results in maximum performance. To put it another way: in the
Performance Manager concept, an individual’s CTL (and the “composition” of the
training resulting in that CTL – see more below) determines their performance potential
(at least within limits), but their TSB influences their ability to fully
express that potential. Their actual performance at any point in time will
therefore depend on both their CTL and their TSB, but determining how much
emphasis to accord to each is now a matter of trial-and-error/experience, not
science.
To better illustrate the conceptual differences
between the impulse-response model and the Performance Manager, consider Figure
3 below, which illustrates the effect on CTL, ATL, and TSB of a square-wave
increase in daily training load from 0 to 100 TSS/d as of January 1st:
Figure 3. Changes in chronic training load (CTL), acute
training load (ATL), and training stress balance (TSB) in response to a
square-increase in daily training load from 0 to 100 TSS/d.
As shown in the figure, both CTL and ATL respond to this
sudden increase in training in an exponential fashion, just like the fitness
and fatigue components of the impulse-response model, and with identical time
courses (since the time constants are the same). As well, TSB shows an initial
reduction followed by an exponential rise, which is qualitatively
similar to the time course of performance as predicted by the impulse-response
model. However, the minima in TSB occurs later than the reduction in
performance would be predicted to occur by the impulse-response model when
using the same time constants (i.e., 42 and 7 d for τa
and τf (or τ1 and τ2),
respectively). Moreover, unlike performance as predicted using the
impulse-response model, TSB never exceeds its initial level, but instead rises
monoexponentially to eventually equal CTL (and ATL). This differing behavior is
a consequence of the elimination of the gain factors ka and kf
(or k1 and k2) from the impulse-response
model, as well as the substitution of exponentially-weighted moving averages
for the integral sums.
After both retrospective and prospective
evaluation of this approach by the author and a few others, the idea was shared
with additional coaches and cyclists, until eventually a pool of approximately
two dozen “beta testers” was created (see Acknowledgements). These individuals
used the Performance Manager during the 2005 and 2006 racing seasons, and in
doing so provided valuable feedback about how to best apply the approach. As
part of this developmental process, a number of variations on the approach described
above were tried (e.g., dynamically-varying time constants), but none proved to
be discernably better than the original, and simpler, formulation. The decision
was therefore made to incorporate the idea into version 2.1 of the program WKO+
as a new chart.
Applying the Performance Manager concept
Because successfully using the Performance
Manager entails some degree of “art”, it is anticipated that users may require
some time to become good “artists”. The following hints, tips, caveats, and
limitations are offered in hopes of speeding up this process.
1) The
concepts embodied in the Performance Manager apply regardless of how the
training load is quantified. In other words, it is at least theoretically
possible to use this approach to evaluate and manage one’s training when the
latter is quantified using, e.g., TRIMP scores instead of TSS. At the present
time, however, WKO+ only allows use of TSS as the “input function”. Thus, to
obtain good results using the Performance Manager approach, it is important
that these TSS values be based upon valid, up-to-date estimates of an athlete’s
functional threshold power. This is especially true since the TSS calculated
for a particular workout varies as a function of the square of intensity factor
(IF) (i.e., TSS = duration (h) x IF2 x 100), and hence the inverse
square of the assigned functional threshold power (since IF = normalized
power/functional threshold power). In other words, decreasing the value assumed
for functional threshold power by, say, 4% (e.g., using 240 W instead of 250 W)
increases the TSS for a particular workout by just over 8%. In turn, this will
have commensurate effects on CTL, ATL, and TSB. Indeed, it is sometimes
possible to identify periods of consistent over- or underestimation of
functional threshold power when an individual’s actual response to training
deviates significantly from that expected based on the Performance Manager
approach.
2) The
Performance Manager approach is predicated on the assumption that an athlete
will use their powermeter during every workout and race, such that a value for
TSS is available for every workout. However, it is not at all uncommon for
individuals to choose to race without their powermeter, for data files to be
corrupted during collection (e.g., if the memory of the powermeter is exceeded)
or lost during downloading, for the powermeter to stop working entirely, etc.
In such cases, it is necessary to estimate any missing TSS, or again the output
of the Performance Manager will be distorted. This is especially true of CTL,
and hence also of TSB, due to the long time constant used in its calculation.
Missing values for TSS can be estimated a number of different ways:
a)
from a “library” of comparable workouts that the athlete has performed
previously, the file(s) from which can be copied into the Calendar of WKO+ on the
appropriate date;
b)
from heart rate data (if available), which can be used to estimate the
normalized power that was sustained, and hence to calculate TSS manually (see
formula under point #1 above). These data can then be entered directly into
WKO+ by first manually creating a workout in the Calendar, and then overriding
the TSS value that is assigned by default.
c)
by simply assuming a value for IF, and then calculating TSS
manually and entering into the program as described under b). When using this
approach, it is useful to recall the typical IF values associated with
different types of training sessions and races, i.e.,
<0.75 level 1 recovery
rides
0.75-0.85
level 2 endurance training sessions
0.85-0.95 level
3 tempo rides, various aerobic and anaerobic interval workouts (work and rest
periods combined), longer (>2.5 h) road races
0.95-1.05 level
4 intervals (work period only), shorter (<2.5 h) road races, criteriums,
circuit races, 40k TT (by definition)
1.05-1.15 shorter
(e.g., 15 km) TT, track points race
>1.15 level
5 intervals (work period only), prologue TT, track pursuit, track miss-and-out
Although intuitively approach b
might seem best (since it is based on actual data), in reality there is little
to recommend this more complex approach over the other two, as it is often
possible for experienced powermeter users to estimate their TSS just as, if not
more, accurately without ever knowing their heart rate. Moreover, any error
introduced into the calculation of CTL, etc., as a result of poorly estimating
the true TSS for one or two missing workouts is likely to be minimal. On
the other hand, if a large amount of data are missing (e.g., >10% of all
files for a particular block of time), then the output of the Performance
Manager calculations during and following that period should interpreted with
considerable caution. In turn, this emphasizes the importance of racing
with a powermeter, since individuals often incorporate frequent racing into
their training program when attempting to peak.
3) Related
to the above, the long time constant for CTL means that data must be collected
for a fairly long period of time before the Performance Manager calculations
can be considered accurate (cf. Fig. 3). Obviously, however, a new powermeter
user will not have a large database of files that can be analyzed to determine
their starting point. Similarly, a long-time powermeter user who hasn’t paid sufficient
attention to tracking changes in their functional threshold power may not wish
to rely on their previous data, or they may be without their powermeter for a
lengthy period of time (e.g., while it is being repaired). In such cases, it
may be necessary to “seed” the model with starting values for CTL and ATL, by
using the “Customize this chart” option for the Performance Manager chart in
WKO+. The appropriate value to use can be estimated by realizing that most
people train at an intensity resulting in 50-75 TSS/h (i.e., average weekly IF
is usually between ~0.70 and ~0.85). Those who train more, mostly or entirely
outdoors, and/or in a less structured fashion would likely fall towards the
lower end of this range, whereas those who train less, frequently indoors,
and/or in a more structured fashion would tend to fall towards the upper end of
this range. Unless there is a specific reason to do otherwise (e.g.,
transitioning from using a spreadsheet to track TSS to using the Performance
Manager within WKO+), the same value should be assigned to CTL and ATL (i.e.,
TSB is assumed to be zero). Over time, of course, an individual’s CTL will
become evident, in which can it may prove necessary, or at least desirable, to
go back and revise these initial estimates. Of course, as discussed under point
#2 above the calculated values for CTL, ATL, and TSB should be interpreted
cautiously following such a “seeding” until sufficient data are available.
4) As
should be evident from previous discussion, the Performance Manager concept is
especially useful when attempting to reach peak performance on a particular
date. In practice, this entails deciding how much ATL, and hence CTL, should be
reduced so as to result in an increase in TSB. Or, to put it another way, how
much “fitness” should be given up or sacrificed in order to create more
“freshness”. Since each individual is different and since the answer to this
question may depend in part of the particular aspect of performance that one is
attempting to maximize, previous experience is often the best guide here. Thus,
one valuable approach is to use the Performance Manager as a “lens” through
which to view previous attempts at peaking, and then to use the knowledge
gained by doing so to modify, or simply try to replicate, that experience. Alternatively
and/or in addition, the following approximate guidelines may prove
useful when analyzing prior data: a TSB of less than ‑10 would usually
not be accompanied by the feeling of very “fresh” legs, while a TSB of greater
than +10 usually would be. A TSB of -10 to +10, then, might be considered
“neutral”, i.e., the individual is unlikely to feel either particularly
fatigued or particularly rested. The precise values, however, will depend not
only on the individual but also the time constants used to calculate CTL and
ATL (see more below), and therefore should not be applied too literally.
5) Although
most attention is likely to be focused on application of the Performance
Manager to managing the peaking process, other benefits to this approach
clearly exist and should not be overlooked. For example, experience to date indicates
that, across a wide variety of athletes and disciplines (e.g., elite amateur
track cyclists, masters-age marathon MTB racers, professional road racers), the
“optimal” training load seems to lie at a CTL somewhere between 100 and 150
TSS/d. That is, individuals whose CTL is less than 100 TSS/d usually feel that
they are undertraining, i.e., they recognize that they could tolerate a heavier
training load, if only they had more time available to train and/or if other
stresses in life (e.g., job, family) were minimized. (Note that this does not
necessarily mean that their performance would improve as a result, which is why
the word “optimal” in the sentence above is in quotes). On the other hand, few,
if any, athletes seem to be able to sustain a long-term average of >150
TSS/d. Indeed, analysis of powermeter data from riders in the 2006 Tour de
France and other hors category international stage races indicates that the hardest
stages of such races typically generate a TSS of 200-300, which illustrates how
heavy a long-term training load of >150 TSS/d would be (since the average
daily TSS of, e.g., the Tour de France is reduced by the inclusion of rest days
and shorter stages (e.g., individual time trials), and it is generally
considered quite difficult to maintain such an effort for 3 wk, much less for
the 3+ mo it would take for CTL to fully “catch up”). Of course, even if
this general guideline of 100-150 TSS/d eventually proves to be incorrect, this
does not change the fact that the Performance Manager approach makes it
possible to quantify the long-term training load of any athlete in a manner
that a) takes into account, via TSS, the volume and intensity of their training
relative to that individual’s actual ability (i.e., functional threshold
power), and b) does so in a manner that is consistent with the effective time
course of adaptation to training, as determined using the impulse-response
approach.
6) In
addition to the absolute magnitude of CTL, considerable insight into an
individual’s training (and/or mistakes in training) can often be obtained by
examining the pattern of change in CTL over time. Specifically, a long (e.g.,
4-6 wk) plateau in CTL during a time when a) the focus of training has
not changed, and b) the athlete’s performance is constant is generally
evidence of what might be termed “training stagnation” – that is, the
individual may feel that they are training well, by being very “consistent” and
repeatedly performing the same workouts, but in fact they are not training at
all, but simply maintaining, because the overload principle is not being
applied. On the other hand, attempting to increase CTL too rapidly, i.e., at a
rate of >5-7 TSS/d/wk for four or more weeks, is often a recipe for
disaster, in that it appears to frequently lead to illness and/or other
symptoms of overreaching/overtraining. Of course, since changes in CTL are
“driven” by changes in ATL, this means that any sudden increase in the training
load (e.g., training camp, stage race) must be followed by an appropriate
period of reduced training/recovery, so as to avoid too great of an overload.
To state this idea yet another way: failure to periodically “come up for air”
by allowing TSB to rise towards, if not all the way to, neutrality may lead to
problems, because the training load is being increased too rapidly without
allowing for adequate recovery (i.e., ATL >> CTL for too long).
7)
The default time constants of the Performance Manager, i.e., 42 d (6 wk)
for CTL and 7 d (1 wk) for ATL were chosen as nominal values based on the scientific
literature. As with the fitness component of the impulse-response model, the
precise time constant used to calculate CTL in the Performance Manager has a
limited impact, and although users may still wish to experiment with changing
this value, there seems little to be gained from this approach. On the other
hand, the calculations in the Performance Manager are sensitive to the time
constant used to calculate ATL, and hence TSB (since TSB = CTL – ATL). Thus,
part of the art of using the Performance Manager consists of learning what time
constant for ATL provides the greatest correspondence between how an athlete
actually feels and/or performs on a particular day vs. how they might be
expected to feel or perform based on their CTL/ATL/TSB. Again, experience
indicates that younger individuals, those with a relatively low training load,
and/or those preparing for events that place a greater premium on sustained
power output (e.g., longer time trials, 24 MTB races, long distance triathlons)
may obtain better results using a somewhat shorter time constant than the
default value, e.g., 4-5 d instead of 7 d. Conversely, masters-aged athletes,
those with a relatively high training load, and/or those preparing for events
that place a greater premium on non-sustainable power output (e.g., shorter
time trials, criteriums) may obtain better results using a somewhat longer time
constant than the default value, e.g., 10-12 d instead of 7 d. (Of course,
since athletes preparing for longer events often – but not always – “carry”
higher overall training loads, this tends to constrain the optimal time
constant more than would otherwise be the case.)
8) While
the Performance Manager is an extremely valuable tool for analyzing training on
a macro scale, it is important to also consider things on a micro scale as
well, i.e., the nature and demands of the individual training sessions that
produce the daily TSS values. That is, the “composition” of training is just as
important as the overall “dose”, and the usefulness and predictive ability of
the Performance Manager obviously depends on the individual workouts being
appropriately chosen and executed in light of the individual’s competition
goals. To give an example: an elite pursuiter might build their CTL up to the
same high level during both a road-focussed, level 2/3/4 intense period of
training early in the season and during a track-focussed, level 5/6/7 intense
period of training immediately before the national championships, but even
after a comparable period of tapering (to achieve the same positive TSB, i.e.,
to gain the same amount of “freshness”) you still would not expect them to
perform as well in an actual pursuit earlier vs. later in the season. Conversely,
however, they likely would perform better in a road time trial earlier vs.
later in the season, because the training they were performing at that time
would have been more appropriate, or more specific, for that event. In both
cases, however, CTL, ATL, and TSB would still be good indicators training load
and adaptation. Moreover, it is important to note that this limitation is not
unique to the Performance Manager approach, but also applies to the
impulse-response model as well. Indeed, as Morton et al. (1990) emphasized in
discussing the parameters of the criterion performance tests used to establish
the time constants and gain factors of the impulse response model: “They must
represent best-effort performances on a standard test that is appropriate
in length and intensity of effort to the competition event being prepared for.”
(emphasis added) In other words, the specificity principle always applies, and
this fact should not be overlooked when using (or evaluating) the Performance
Manager.
Aknowledgments
I would like to thank the following members of
the “eweTSS” list on topica.com for the extremely valuable feedback that they
provided during the development and implementation of this analytical tool:
Hunter Allen, Tom Anhalt, Gavin Atkins, Andy Birko, Lindsay
Edwards, Mark Ewers, Sam Callan, Chris Cleeland, Tony Geller, Dave Harris, Dave
Jordaan, Kirby Krieger, Chris Merriam, Jim Miller, Chris Mayhew, Dave Martin, Scott
Martin, Phil McNight, Rick Murphy, Terry Ritter, Ben Sharp, Alex Simmons, Phil
Skiba, Ric Stern, Bob Tobin, John Verheul, Frank Overton, Lynda Wallenfells, and
Mike Zagorski
(And if any of these folks or those that they train have
really been kickin’ butt recently, now you know at least part of the reason why.
J)
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