wko+: Speaking the Lingo

“SPEAKIN’ IN ENGLISH….?”
- Jeannie from Ferris Bueller’s Day Off.

While my athletes are far too polite to express themselves in the way that Jeannie does in that legendary movie, I am sure that on more than one occasion, when I go off on one of my wko+ related rants with a lot of terms and vernacular that they don’t 100% understand, they have felt the same way. So, this is for them, a (hopefully, somewhat) concise list of definitions of terms associated with the training software wko+. I was asked to put together a wko+ for Dummies. I’m not sure this qualifies, more like a ‘wko+ for quite intelligent folk who have some questions :-)’ A more thorough explanation can be found here...

http://home.trainingpeaks.com/power411.aspx

So let’s dive right in with the ‘mother of them all’ – Normalized Power.

Normalized Power:

Normalized Power (NP) is a similar statistic to average power but is calculated a little differently. While average power simply takes all of the samples from your powermeter and divides them by the number of samples, Normalized Power uses a tricky little weighting system to come up with a number that is more in line with the true physiological effort of a given session.

The calculation:

Let’s say we do 2 rides – one that is completely even paced (on a trainer or velodrome) such that if we took a random sample at the beginning, the middle and the end, they would all read 200 watts. The other ride is an extreme ‘poker paced’ ride where we focus on being strong at the end. We ride the first 1/3 at 100W, the second 1/3 at 200W and the last third at 300W.

Average power for both rides is identical:
(200+200+200)/3 = 200
(100+200+300)/3 = 200

However, from a physiological perspective, they are quite different (especially for an athlete with say a Functional Threshold of 250W!!), with the first being much more ‘pleasant’ For this reason, Dr. Andy Coggan came up with a formula to weight this variability according to its physiological difficulty. In this case, samples are raised to the 4th power, an average is taken and then the fourth root is taken of that. From our example above:

(200^4+200^4+200^4)/3 = (1600000000+1600000000+1600000000)/3 = 4800000000/3 = 1600000000

Then taking the 4th root of 1600000000 = 200NP (the same as the average power)

However, in the second scenario:

(100^4+200^4+300^4)/3 = (100000000 + 1600000000 + 8100000000) = 9800000000/3 = 3266666667

Then taking the 4th root of 3266666667 = 239NP (39 watts greater than the average power).



The 4th power curve (above) looks a lot like a lactate curve, doesn’t it? This general trend of physiological effort, measured by things like blood lactate increases exponentially with increasing workload. This is the very concept behind Normalized Power – if you put out double the wattage, say go from 200-400W, anyone who has trained with power can attest that it is a whole lot more than twice as hard. Furthermore, when an average athlete jumps from 200-400W, the time to exhaustion is significantly more than halved.

Variability Index (VI):

A follow up term from the above, initially coined by Charles Howe, is the Variability Index.

The calculation:

This is simply the normalized power divided by the average power. So, in the scenario above, we would say that the 100/200/300 ride had a variability index of 1.20 (239NP/200AP).

This is a good indication of how ‘smooth’ the ride was. In an Ironman context, VI numbers are typically low, ranging from 1.0 to 1.05 for flat courses and 1.05-1.1 for hilly courses.

There is an optimal VI for each athlete on each course that gives the athlete the greatest speed for the lowest effort. This is related to course factors such as the number and grade of hills, which reward the athlete with more relative speed for a given power and the physiological peculiarities of the athlete – while it may be tactically optimal to put out 300W going up the hill and 100W descending for an average of 200W, if 300W puts the athlete over their threshold and the descent isn’t long enough to clear the lactate, tactically optimal doesn’t matter!

For this reason, the best way to determine an optimal VI for a given athlete is with multiple race sims on a given course using differing power output strategies and seeing which affords the athlete the greatest speed/power combo.

Intensity Factor

Also related to Normalized Power, the intensity factor is simply the Normalized Power of a given ride divided by the athlete’s Functional Threshold.
By most definitions, Functional Threshold refers to the athlete’s maximal power output over 1hr. In some sense, it is a proxy for the athlete’s power at a maximal lactate steady state. In other words, it typically represents a power level in which blood lactate levels begin to plateau and thus exercise duration is no longer limited by lactate accumulation, but rather begins to become limited by glycogen depletion.

In real world competition, durations in excess of 90 minutes (e.g. half marathons for good athletes) typically elicit lactate levels below the athletes maximal lactate steady state, and are thus not limited by lactate accumulation, but rather glycogen depletion, while for competition durations of <60 minutes, e.g. 10K run races, lactate steadily accumulates throughout the race and the associated acidosis ultimately limits performance. Personally, I think a 90 minute power is more truly indicative of this ‘functional threshold’ but the 1hr mark is more consistently used, is near enough and is easier to work with, so it gets the ‘thumbs up’.

So, if we accept 60 minutes as the point at which this ‘functional threshold’ from cardiovascular to metabolic limitation occurs, the intensity factor represents the % of functional threshold power for the ride. From the example above, if we assume the athlete has a functional threshold of 300W, the intensity factor of the ride would be 239W/300W = 0.80

Training Stress Score (TSS)

Here is where the real fun begins :-)

Training Stress Score represents a number combining the volume and intensity of a given ride to give a summary of how “hard” the ride was in an overall sense.

Because it represents volume and intensity in an appropriately weighted number, it can also be considered a proxy for the glycogen (energy) cost of a given ride.

Calculation:

It is calculated as IF^2*100*Ride Duration in hours.

The IF^2 represents the relationship between glycogen depletion and training intensity and it agrees very well with what our lab results would suggest for an average athlete.

Because TSS is a relative rather than an absolute measure, perhaps the best ‘rule of thumb’ way to look at it is:

100 TSS = ~100% of your personal glycogen stores.

If you have 1000kcal of glycogen at your disposal then each TSS is worth ~10kcal of glycogen.If you have 1500kcal at your disposal then each TSS is worth ~15kcal of glycogen.

In this way, athletes can have rides with very different work (kj) and power outputs but the same relative training stress.

From our example above, if the 100/200/300 descending ride was a 2hr jaunt for an athlete with an FTP of 300W (culminating with 40mins at FTP), the training stress of that ride would be:

IF^2*100*Ride Duration in Hours

= 0.8^2*100*2 = 64*2 = 128TSS.

Because this represents 128% of the athlete’s theoretical glycogen stores, this would be a very tough workout to complete without supplemental carbohydrate. While, by using 60mins rather than 90mins as the ‘functional’ threshold, we likely ‘low-ball’ the athletes true glycogen capacities to some extent, (with a true starting # probably closer to 150 for well trained athletes), for most athletes a 128TSS ride with no carbs is going to have the athlete on the verge of seeing stars :-)

In a ‘chronic’ context, trained athletes can replenish ~60% of their glycogen stores within 12hrs and ~85% within 20hrs (Casey et al. 1985). Therefore, from a training prescription stance, athletes should be wary of unsupplemented sessions that use more than 85% of their energy stores/85TSS per day. These can be supplemented at a rate of ~240-300kcal/hr (25TSS/hr) at low intensities (IF’s of ~0.7) and therefore, in practice, with appropriate nutrition, we see, providing intensity is kept moderate, repeated training days of up to 160TSS (3hrs/day) are possible over the long term in well trained athletes. Which brings us to our next term….

Chronic Training Load (CTL)/”Fitness”

The chronic training load simply represents your long term tolerance to a given relative training stress.

In its simplest sense, CTL can be thought of as a rolling long term average (the default is 6 weeks) of the athlete’s relative training load.

In this sense it is often used synonymously with fitness, assuming that fitness is related to long term work capacity and indeed, for a given athlete on a given season it has been empirically validated that the athletes highest potential performance will occur at their highest CTL. In my opinion, it is certainly a good indicator of “base fitness”, where increasing the long term capacity to do work is a major training objective.

Using this simplified model, we would expect that after 6 weeks of a given training load, maximal fitness is attained. However, in ‘real world’ studies comparing mathematical modelling with actual performance, the true ‘lifespan’ of performance improvements from a given training load is actually much longer than 6 weeks (Good news for the time limited athlete committed to consistency!!) so the model is modified with an exponent.

This is where the math starts to get a little complicated:

Chronic Training Load = [Todays TSS * (1-e^(-1/42)] + {Yesterdays CTL * (e^(-1/42)]

In this series, as time goes on, and yesterday’s CTL gets bigger, the relative benefit to today’s CTL gets smaller. This can be expressed graphically as follows in response to a constant training load of 100TSS



This model follows the actual empirically validated time course of aerobic training adaptation. In other words, if you are following a training program in which the workload is not consistent enough to put together 4-6 months of consistent training, the fitness benefit of a given training load will not be realized. In other words, you will be training excessively for the fitness adaptations that you are achieving – you will be overtraining. Likewise, if a given training stimulus is continued for too long, the fitness benefits will plateau, while fatigue will continue to accrue. In this sense, you are also ‘overtraining’. Monitoring Chronic Training Load helps to give a big picture appraisal on both fronts.

In terms of specific values, as mentioned in the section on TSS, most novice to intermediate athletes will be able to handle a long term program of 100TSS/day providing intensity is kept moderate and nutrition is good. Top age group athletes will typically be in the range of 120TSS/d Elite/pro very well trained athletes may be able to put together 6 months of 150-160TSS/d training to achieve a CTL approaching 150. However, achieving a given CTL is no guarantee of performance. You can have great base fitness, but without a corresponding high workrate/FTP, it will not reflect in performance. Both are needed.

It is useful to look at CTL along with FTP. For instance, if an athlete’s CTL is 100TSS/d in season 1 with an FTP of 240W and remains at 100TSS/d in season 2, but with an FTP of 280W, despite the same relative workload, their absolute workload has improved by 290kj/day!!

Considering the risks of inconsistency associated with ‘overdoing it’. Those who monitor CTL will quickly realize that a very moderate, long term approach to training is best.

Acute Training Load (ATL)/”Fatigue”

The acute training load represents your short term training loading in TSS/day. In this sense it is a good indication of how much load you’ve put yourself through over the last little bit.

Mathematically it is expressed the same way as CTL, however, the default constant is now 7 days instead of 6 weeks.

In order to gain fitness, ATL (fatigue) must exceed your current CTL (fitness). By how much is the million dollar question which is frequently only answered by experimentation.

Tolerance to fatigue will change with each athlete and with the point in the season, with athletes typically tolerating greater levels of fatigue after coming back from a recovery period and lower levels as the season goes on.
Additionally, tolerance to fatigue will be greatly impacted by the type of training (intensity) and by the athlete’s nutrition.

Training Stress Balance (TSB)/”Freshness”

The difference between ATL and CTL alluded to above has a mathematical equivalent: TSB.

Training Stress Balance is simply CTL minus ATL or, the difference between what you can tolerate long term and what you put yourself through (short term). Thus it is a good indication of how ‘fresh’ you are at any one point.

If your ATLCTL then you are training more than you can tolerate and are ‘digging a hole’. Providing you give yourself a chance to climb out of that hole in a timely manner by not digging too deep and by incorporating planned rest periods, this is the key to improvement.

Over the course of a season, TSB will typically initially ramp down as the athlete gets back into training after a worthwhile break, will hit a season low during the early season “base” period then will progressively rise as the athlete approaches the competitive period. This pattern is shown below in response to typical season loading below:




The athlete’s ‘freshness’ is indicated by the yellow line.

At the start of the season, the athlete starts from a freshness of zero after an off-season (not fit or fatigued). The first month of training is taken gently (30-60% of peak load) but still elicits a good chunk of fatigue. You can see the effect of the unloading weeks on the athlete’s freshness. The 2nd month is the toughest of the season in terms of fatigue. Despite a greater load in the third and fourth month, the athlete is better able to deal with the load and so ‘freshness’ is higher. By the third unloading period, the athlete’s freshness is back to the zero baseline and they are ready for a C competition. After another loading phase, the athlete unloads and then seeks to maintain their fitness through the length of the competition period. At the end of this, the athlete further unloads during the taper and gets ‘super fresh’ for the main competition of the year.

As mentioned, ‘optimal TSB’ will vary for different athletes at different times of the year. However, when the athlete has had a good season, it is nice to have a ‘blueprint’ of what fitness and fatigue dynamics led to this result.

Hopefully this article will answer some of the many questions out there surrounding wko+. It is an incredibly powerful coaching tool when used consistently and appropriately.

Train Smart.

AC.