Watt Matters is a blog about training and racing with power and other related musings.

Aside from writing items here on occasions, I also provide cycling performance improvement services via coaching, aerodynamics testing and host a cycling tour.

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Insensitive / TSS^

No, it's not a "someone made an inappropriate remark" story! It's another power meter, cycling training item. So for those non-PM using readers who's eyes roll around the top of their head when I go on about this stuff, then you can look away now :D .....

There have been lots of comments lately on the Google groups wattage forum about the Normalised Power (NP) algorithm and whether it could be improved. The discussion, as they often do, has drifted a bit from that into - "could the Training Stress Score (TSS) metric be improved?"

Well can they? Possibly.
Should we bother? I'm not so sure it matters.

Maybe it's because of the insensitivity of these things. Lemme show you an example.

I have produced a standard Performance Manager Chart (PMC). It covers my riding since I started back on the bike last year (~ 7 months).

As an experiment, I decided to add onto it another version of the PMC, with data based on an augmented TSS (TSS^). In this case, the calculation of TSS is not a function of the ratio of NP to Functional Threshold Power (FTP) but expressed as a ratio to Maximal Aerobic Power (MAP).

Now there is no particular reason for doing it other than curiosity, nor would there be any great sense given the underlying physiological and other rationale for choosing FTP as the anchor point. But that's not my point. It's an experiment to see what it means, from the point of view of how we actually use the information to monitor and guide our training.
MAP for most people is typically 25% +/-3% higher than FTP and so by anchoring an augmented TSS calculation to MAP instead of FTP, that will of course change the way TSS is calculated (since now I get a much lower weighting for threshold work and have to exceed MAP for gains to be multiplied).

And the impact of changing the TSS calculation? Well that'll change the PMC and how we interpret our training, right? Well, maybe.

Here's the PMC chart with two sets of lines for ATL, CTL & TSB. Default time constants used. One is based on TSS, the other (right hand axis) is based on the augmented TSS, “TSS^”. As always, click on the pic for a closer look.

Anyway the fact that the augmented ATL^, CTL^ and TSB^ mimic the same patterns, just with different absolute values, should not be a surprise since there is a reasonably consistent relationship between FTP & MAP.

Of course the relationship between FTP & MAP does vary (which it has during the period in the above chart), and when it does there will be deviations (as can be seen in the different slopes of the CTL and CTL^ lines).

But even so, just look at how closely the TSB and TSB^ lines track each other. Yet I have changed the TSS weighting formula quite a bit by anchoring to MAP instead of to FTP.

So if I showed you those charts independently, and multiplied the right hand axis values by two, you simply would not know the difference and it certainly wouldn't provide any different or additional insight into what was going on with my training.

So what would a PMC look like using these other “improved” formula for NP and/or TSS? That's what I'd like to see. Can it really provide us with a better insight into what's going on with our training?

I suspect all the mucking about with alternative NP or TSS formula would do is simply produce slight variations in the PMC (maybe absolute numbers a little different here and there) but the underlying training patterns that emerge would be the same and the interpretation would be the same. And even if the patterns are different, we still have to look at them in the context of the composition of our training, rest of life factors etc just like we do now (or should do).

Basically the modelling is pretty insensitive.

But let's see some examples folks....

I'm always open to looking at things in different ways to help garner additional insight.

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