OK I have searched and I see nothing that talks to what seems to concern me with MPA. I suspect that if I went out far enough I may see some different approaches to the calculation but I am curious how after repeated intervals where you pull MPA down to near break through level it returns to 100%. Is there any published research that supports this case? Intuitively to me it seems there may be some issues with this. I would think that each time you stress the system that it means you do not return to the former state and you do this enough there comes a time where the tank is empty. Also I question if the decay rates are the same for usage as in recovery? and how they relate to the % over or under TP you are in each case? In other words MPA is somewhat similar to W’ but is there any suggestion in literature that your available energy or power remains a constant regardless of how you use it up? My thinking is that each time you draw this down past a certain point that it can not go back to 100% but maybe returns to 90- 95%% of the last high recovery point. ie if the last recovery took you to 90% of your rested MPA the next would be 90% of that or to 81% of fresh MPA and so on? In other words it represents both physical/ muscular and CNS fatigue. I would like to understand this more.
I think this boils down to being able to predict for each individual how they recuperate from a BT effort. In theory, you would be able to do that again, after a while, but most people will not.
Probably a holy grail…
I assume you already reviewed these articles:
There is likely more explained in one or more of the podcasts.
I don’t fully understand how it works, but in practice it seems to work as described.
Thanks for those but yes I have reviewed that data. The key is that MPA is similar to but exactly like W’ so of course there is no literature specific to MPA since that is an Xert specific metric. But W’ is in literature so since they have similar purposes so I have done many and and played with making smart workouts that use the recovery and depletion of MPA as the determinant of the interval length. In a short time domain maybe one can fully recover to 100% MPA if the work was relatively close to TP but as it nears PP I start to think that the MPA use and recovery model starts to breakdown?
Similar in concept to W’, but different in how they’re applied/utilized. W’ is merely a capacity, and the model assumes that you can fully expend it at whatever rate you want to (i.e. if your W’ is 20kJ, then you could sprint at 20,000 W for 1 second, which we know isn’t physiologically possible). Instead, HIE serves as a capacity and is rate-limited by MPA, meaning it can never be fully depleted, since MPA becomes a limiter.
The assumption that MPA goes completely back to PP might be an oversimplification for now, but it works really well for nowl I think trying to predict exactly how much your fitness signature is going to change over time is all but impossible to answer, since sub-TP efforts have wildly varying reasons for termination of exercise (glycogen depletion, boredom, calcium fatigue, core temperature, etc), whereas acute peripheral (muscular) fatigue is easier to model (CP/W, FTP/FRC, and Xert’s Fitness Signature).
I have a question on this topic of MPA and this was the thread closest to it so I’m resurrecting this instead of starting a new and probably duplicative thread. My question is this: I’d like to understand the thinking/data behind not having any sort of time decay built into MPA over the course of a ride. My personal experience, albeit an n=1 sample size, has been that trying an all out max effort after 4 hours of solid endurance pace (but basically zero time above threshold) yields a dramatically different result than if I try an all out max effort after a good 30 minute warmup. I’ve tested this out quite a few times since encountering this MPA concept here just because I wanted to know if my legs did indeed actually have that kind of power available and it was just in my head that they were tired. But my results show that, for me, after a solid 3.5-4 hours I cannot get much past 70% of my MPA. I’m wondering if it is really just me and most athletes/cyclists can indeed hit full MPA after several hours of riding?
There are some ways to model MPA after a long ride at the moment. You can modify the signature, refresh to see what your signature might be after 4 hours of riding. Some of things that erode your signature during that time are in the power data but some are related to other things outside your power data (such as fuelling, sleep, soreness, prior rides, hydration, etc. etc.) so getting a signature, i.e. to reflect that isn’t so simple.
Think of MPA and your signature like this: would you do an FTP test after 4 hours of riding? Likely not so those efforts aren’t yet included in your fitness signature. Those numbers represent your best numbers.
One thing to note is that professionals don’t see a degradation in their signature over time the same way others do. Hence, if you can hit your MPA after 4 hours, it would mean that you’ve trained well for that and likely fuelled and hydrated well.
How would I go about modifying my signature to capture that MPA decay over time within a ride? The ‘signature decay method’ setting says it just applies to time between activities, not during. I’ve messed with the three variables in advanced MPA analysis on my rides but none of that seems to show any effect of time decay?
I get that pros would definitely experience a different result than me when doing an all-out effort over time since they have trained for that, but ostensibly they started out at some point where after a 4 hour training ride they had very little left in their legs for an all out effort and built up to where they are now. I also 100% agree that other variables play a role in what is achievable as a ride goes on in duration and that has definitely been my experience too.
So it seems generally agreed upon that as the duration of a ride increases, the MPA decreases by some amount for every rider, even pros. The extent to which it decreases is dependent on all those variables mentioned, but what’s critical in what you note is that some of that decrease can be trained away (i.e. what the pros do to prepare for a final sprint, breakaway, etc…). How could I use xert to monitor improvements/achievements as I start working on training that will lessen the decay in my MPA when I get no feedback from xert on whether I achieved any sort of breakthrough or improvement in the results of a sprint or breakaway effort at the end of a longer training ride?
It seems to me that including some sort of time decay into the MPA calculation over a ride of any duration makes sense. It could be a simple linear model where the slope changes depending on athlete, or even an exponential/polynomial type model with upper and lower limits to recognize that MPA doesn’t eventually go to zero if somebody does something crazy like a 24 hour ride. What I’m gettting at is that this seems to be a relevant modification to the MPA model and would be very helpful for training purposes, particularly for those who race. The one ‘wrong’ assumption that seems to be agreed-upon is that MPA remains constant over any ride duration…so why is that how it currently works?
To be fair, it becomes ‘easy’ to pick on Xert because its MPA is so central to the analysis.
Has anyone asked why HR zones or Power zones don’t change over a 4+ hour ride? It’s the same concept, so it’s not that Xert is the only one doing it ‘wrong’. Just providing some additional things to think about…
I think the issue is more evident using a different example. Say you ride “Sweet Spot”, 90-95% of your TP for an extended time, this is a difficult effort that most riders can’t do beyond 1.5-2h. If I understand correctly, Xert would say at the end of this effort you have all your MPA available (since you didn’t ride above or at TP) and this is clearly wrong.
Any plans to address this blind spot in the model?
Suppose it was addressed perfectly, 100% accurately. What benefits would it give? Honest question.
I know it seems like I’m picking on xert. It seems no matter how carefully I word my notes here, the tone of my writing is harsher than what I intend to convey. I bring all this up because, after what I consider still a limited level of experience with xert, I believe its model/approach towards cycling fitness measurement and development is better than others out there that rely heavily on models with known flaws. That said, xert seems so close to being yet another level superior by calibrating its approach just a bit to better reflect what I think most of us seem to feel as reality through our training experiences within a ride. And yes, @havella your example put the issue in much clearer terms. I won’t pretend to understand the model, but it does seem that MPA and the decay/rebound of it, and corresponding breakthroughs, are a central element in developing a fitness signature which is then used to calibrate workouts. Thus, getting it right (well…striving to get as close to right as possible since there probably is no such thing as a single ‘right’ solution here) to know when a real breakthrough has been achieved and how that might impact fitness signature seems kind of important. As I think more about this, it almost seems like the concept of a 2-dimensional fitness signature is outmoded. Fitness signatures should likely have a 3rd axis of time and the decay rates of various points along the X/Y power curve differ by athlete and can change/improve with training. And just to note: I know of no other such tool/software that captures this reality, but I want to hold xert to a higher standard because it is, well…better.
The power produced in a HR zone is likely to change over a 4+ hour ride, so HR zones have a degree of compensation.
I think what Scott and Armando are saying is that though they may agree the implementation and the science are not there. For instance my decline may be different than yours so what physiological marker will guide the math? We loose energy reserve over time at effort, you hear it all the time from pros who are at the peak of fitness, they worked too hard earlier and could not put out the power at the end, their maximum power was not available. The problem is how do you model that? Maybe in future something like HRV and or DFA a1 may be something that can give real time feedback to the model to tell it how your body is responding at that very moment. What is cool about DFA a1 is it does respond to acute stressors. As well with learning HRV also has the ability to say if you are to perform. I think this is the next frontier in athletic performance.
- It would update your signature and produce breakthroughs.
- It would predict performance better for long events.
In general, wouldn’t you prefer a more accurate model vs a less accurate?. If you consider my example (long 95% TP interval), I think it becomes evident.
Wouldn’t this make you doubt of the model in general?..What is so precisely different between an effort at 98% of TP and one at 101% TP ???. Both are hard, both consume your MPA, but only one gets counted in the model.
I look forward to the day when my ability to execute/repeat hard efforts deep into a workout/race/group ride is something that can be measured as I train for improvement. I see no other system out there in a good position to do this. The W’bal/CP model has the same blind spot as MPA in this regard, but I see xert’s system here, and the group of insiders working on it, as more willing and able to challenge and improve upon these older models since they’ve already done so. Its why I’m being so annoying with repeated posts in this thread. The concept of a dynamic model for MPA seems a classic statistics problem: not out of reach, particularly with the trove of empirical power data Xert has. I like the idea of developing workouts where you deliberately bounce up against some portion of realistic MPA and can really fine tune that as opposed to just guessing and going for the same repeated static targets until failure and then limping home or curling up in the fetal position on the garage floor.
Ok, I promise that I’ll shut up now.
Is doing a BT effort a good ammount of time into your ride an idea? Would this give you a Fitness Signature that represents what you are able to do with some fatigue of the elapsed time of ride?
IOW SMART intervals good. %FTP blocks bad.
Xerters don’t let friends ride blue blocks.
Yes that means riding indoors but two HIIT workouts a week will do if you want to ride the rest outdoors.
No point chasing rainbows and unicorns.
Just be happy riding the rainbow gauge.
Hi @xertedbrain . Could you please comment on this?. I just want to be able to understand how to use Xert model in the best way possible. Thanks