Let me talk from my experience about this. In my previous Xert membership 2 years ago l usually did endurance workouts around LTP in my yellow days partly driven by the ego of showing high average power and speeds. Moreover I starting racing in zwift and you feel the need to show in your outside rides that you are legit. Yeah l know it’s stupid…
Having a relatively high LTP this strategy led me to a chronic fatigued state and ultimately to burn out. I was unable to complete hard high intensity workouts and was forced always to choose moderate intensity in my blue days. At the end all my rides were moderate…l learnt the lesson. Now when my status is yellow l ride easy and long if I have time. As a result l feel strong and motivated in the high intensity workouts when it’s time to really go hard.
In summary, IF YOU HAVE THE TIME, my advice is go easy and long in your endurance rides. You don’t even need a power meter. RPE should be king in these rides. Keep your mouth closed and breath through your nose.
Btw l just listened to one of the first Xert podcasts where Dr Cheung talks about this strategy as his way to train.
The latest changes to the signature extraction (late last year), should do a better job of balancing your signature and reducing the tendency for LTP from creeping up for some users. As mentioned previously, keeping your PP up to date will improve the system’s ability to balance things.
Yes I noticed that. You must do some all out sprints from time to time for the system to capture a realistic PP. I think my previous LTP was too high because my PP was underestimated. Now the former is about 30w less with a peak power about 200w higher so I get higher XSS values in endurance rides which makes me happier!
But hasn’t that approach (optimizing xss/fatigue by staying close to LTP) the problem that one basically never trains at actual race pace (= SS/Threshold/OU’s for many race formats).
That’s where is see where old school training plans have a plus over xert as they incentivize to improve “time in zone” at those high but <TP pace.
This is helpful for a 4month Xert user … as I too cannot maintain meaningful training if I target Endurance rides at LTP. It helps to understand ‘why’ rides are set as they are so I can easier make informed decisions ta. One wee question - you mention LT1 - how does that compare to LTP in Xert’s model?
In my opinion a relevant factor is total volume of training. If you train 20hrs+ per week I think that endurance workouts around LTP (or LT1) should be considered as difficult in the 80/20 scheme for the distribution of sessions since it will be difficult to recover form that with such a high volume. Key workouts in an ideal week for me would be one high intensity workout, an endurance ride around LTP, and one long endurance ride below 60%TP. The rest short easy endurance and strength.
Xert since Christmas vs Traineroad since Christmas last year.
All of my numbers are better - my Z2 power has gone from about 230/240 to close to 270/280 for a similar heart rate (intevals.icu power/hr). My estimated power numbers are all higher (est TP ~370-380 vs 340ish last year).
But my performance in races last year was infinitely better. Last year I podiumed most things I entered. This year I just can’t hold the pace I’m required to hold - aka, just above and just below threshold - low 300s for extended periods.
I find this a fascinating topic In theory, it might actually start (at very, very small amounts) above LTP. Between LTP and TP, you’re burning carbs & producing lactate, but at that intensity, the lactate can still be burned aerobically. Lactate doesn’t start accumulating until higher intensities. So rather than your MPA staying equal to Peak Power when riding at “sweet spot”, maybe MPA dips down (marginally) before stabilizing when riding at intensities between LTP & TP? And the closer to Threshold you ride, the further MPA could drop down before stabilizing?
But that would still result in no modelling of the cumulative impact of fatigue, which is the issue at hand. To my eye this is a linear Vs nonlinear accumulation issue.
Do consider the competition. With many more riding Zwift in the off-season, competition is increasing. If your numbers are better and you’re not competing at the same level, there can be other factors involved.
I’m at my best fitness ever … but then so are my riding partners (need to ban them from using the system). Everyone in our group is hitting new levels.
It’s not about linearity but about how it gets applied to different systems at different intensities. I.e. it’s no longer linear to TP but linear(ish) to LTP. It’s in the works in our Xert 2.0 model.
Still all of this is new to the entire cycling community. Going to be presenting some of our research in Florence in a couple of weeks. There is increasing interest in XSS vs. TSS to model both fitness and fatigue in more than one dimension.
Isn’t that what I just suggested? (fatigue accumulation being nonlinear above TP).
Do consider the competition. With many more riding Zwift in the off-season, competition is increasing
Does anyone racing at 4.5-5.5wkg not train systematically in the offseason? The point I made was that whilst some numbers are better, the numbers that matter (for pretty much any bike racing not on the track) are not. But as ever I do appreciate the gaslight =].
There are more of them and yes, many just ride themselves into shape in the early season. We see a lot new users come on board in the early spring. Not everyone wants to be on a trainer, including those even at the highest levels … at least in the past. With Zwift and other virtual platforms, more are opting to ride in the winter, even just for fun. I see more and more riders that would otherwise be training their way into fitness in the past, hit the spring in full fitness. So yeah, even if your numbers improved, you could easily not see improved results. Then again, fitness isn’t racing either so if you podiumed one year with less fitness and didn’t podium the next year with better fitness, that’s not unusual on its own. It’s just the nature of racing.