The Xert Ratio question (per Armando's facebook question)

Armando started a very interesting discussion on Facebook recently. He asked what is the result of the math ratio HIE*1000/(PP-TP), what is interesting you get a very similar numerical range if you invert the equation and remove the visibility constant (1000) ie (PP-TP)/HIE.

It seems that most people fall into a very small range between 28 and 32 or for the inversion the range goes between 31 and 36. I am, for lack of a better term, calling this XMR or the Xert Magic Ratio which seems to indicate that your signature is within the reasonable range.

It is an interesting discussion and for those who do not follow the FB thread I thought I would post it over here to further the discussion and thoughts on what this really means?


here are some graphs from the FB data @xertedbrain @ManofSteele
and it seems 80% fall into the middle bin
starting to look sort of normally distributed?

1 Like

Thanks for posting it here Ron!

That’s very interesting! It being a ratio with a result that lies in a narrow range, there are inferences you could draw from it. I wonder how many make physiological sense. I fall into the big bucket at 32.8.

By the way, is the second plot the number of data points within the ranges? If so I count 34 total, and the large bucket has 20, or about 60% (instead of 80%). Did I read that correctly?

Thanks for posting here! (I’m one of those who refuse to have a FB account.)

1 Like

Just hypothesizing…

So, (assuming someone who is already maxed out in training load) as TP comes closer to PP (probably PP also comes down) HIE must also come down. Or, if HIE goes up, PP probably also goes up (and TP probably down). It all jives with what I’ve been reading / hearing on podcasts. As one energy system adapts from training stimulus our other energy systems lose functional capacity from disuse.

I think that may not apply to someone who’s training load is still increasing. One can increase both HIE and TP and go from a ratio on the lower end to a ratio on the higher end. But the ratio likely asymptotically reaches a maximum value, at which point you have to give up something to gain something. Makes sense to me!

here is why I think there is no physiological correlation:
many different sets of numbers can reach the very same ratio.

The key point I see is 1- the delta between PP and TP is related to the size of HIE. The bigger the gap the higher the HIE.So it is a way to see that HIE is in the right range for the difference between your PP and TP. Since PP is an easy number to check and TP is an estimate then PP becomes the anchor number. The other two need to stay in a narrow band for the signature to be correct. That is my hypothesis.

So what does it mean if my number is 42.74? My signature is bad/wrong? I have a fair amount of power data in my profile.

My guess is that either your HIE is too high or your PP is too low for your TP. In other words there is a range of HIE that work with a certain difference between PP and TP and that ratio seems to be around 31

Interesting, mine works out at 34.92, but I do have a fairly flat PD curve, I’m very much a diesel engine type. In fact I’m learning to sometimes flag some outdoor breakthroughs (especially on climbs) as I just can’t complete workouts once I get them. I suppose now when I get one I could check it with this equation.

So my rides are entirely outdoor free rides with no indoor trainer workouts yet. I also have a fair amount of mountain bike rides with no estimated XSS. I have a feeling my MPA, if I’m understanding this number correctly, is being estimated too high as I’m probably riding with a lot more fatigue (due to mtb) than Xert shows. I think you might be right that my HIE is high; it usually is 30+ and that seems high compared to the numbers I can find from others. I do think my TP is also estimating a little low due to MPA being high.

Would this explain my ratio being off? And if so, would being more vigilant about entering XSS for non-power mtb rides lead to better future data? I’d like to get my signature dialed in before winter gets here and I’m primarily on the trainer.

Compare your PP/HIE here: Are there errors in my Fitness Signature? – Xert (

That chart is a normalized scatter plot so no doubt outliers are possible.
Also consider the data set is primarily from road/tri bike riders.

To me, that example data illustrates rather than refutes physiological correlations. One correlation in that example data is that HIE and PP rise and fall together, which is what the people at Xert have apparently found in the data they have of real fitness signatures.

That there are many ways to get to the same ratio is exactly the point – how you train the different systems determines your fitness signature, but however you train them the ratio of HIE*1000/(PP-TP) will for most people stay within a narrow range. We could say that is a physiological system constraint.

1 Like

I don’t know whether Xert uses current calculated fatigue (blue/yellow/red) when determining a fitness signature from a breakthrough effort. So… entering XSS for non-power rides may not affect your signature. Or it may. :man_shrugging: It’s a good thing to do in any case.

It could also be that your PP is estimated too low (that would bring your ratio down).

I hypothesize (without any data) doing a lot of mountain biking, depending on terrain, leads to high HIE and PP and lower TP, because those are the kind of efforts required for mountain biking. I really want to get a power meter on my mountain bike and see if my signature comes out differently – efforts on the mountain bike require much more out of PP around here.

This is very helpful. It confirms my suspicion that my HIE is too high and my TP is too low. Unfortunately I can’t see where things have gone off track in my data. I’ll reach out to support for some assistance in tracking this down.