Amateurs definitely overthink intensity structure. In fact amateurs overthink structure in general. Many dramatically undervalue low intensity aerobic volume.
This fantastic thread emphatically demonstrates how the best train, in general.
Large volumes of aerobic riding. Some higher intensity. More race specific as the season approaches. Great fueling, great recovery. Repeat, repeat repeat.
Basically, if you want to be your best. Time is your most valuable asset in endurance sport.
Without being time rich, you will never reach your true potential. Agonizing over 5w increments and exact interval prescriptions is literally wasted time. Time which many desperately need to patiently build their vastly undeveloped aerobic base.
Agonizing over the icing is pointless if you have no cake
My coach Isaiah Newkirk and FasCat founder Frank Overton were hired as the data scientists for Human Powered Health Women’s team. Listened to a podcast about their experiences:
I’m no pro/elite, and so this was mildly interesting as I don’t know much about the role of data scientists on world tour pro teams. I have a phone consult next week so let me know if you have any questions.
I listened to this earlier as I’m fascinated with this type of stuff and is aligned to my field of work somewhat (data/ information management) and was a good overview.
Not sure what I expected but i was a little disappointed. It felt like the same type of stuff we all track in our various tools (wko5, intervals, TP, GC) etc but I appreciate not everyone goes to the level of detail and data analysis as some of us on this forum
The out sourced model of data management though they refer to (at scale - large roster teams with multi year data and competing priories) was interesting but again surprised that some of this was not handled “in-house”
That being said, right people with right skills to do the right job - let the coaches and directors focus on the athletes informed by the data collated by others to generate insight - is an approach I support.
The part I enjoyed the most was the thought process going into the consumers of the data, and how to present the info. Would have liked more details, but understand they weren’t given. I also assumed that it would be done in-house. Believe they mentioned that as outsiders, they are providing a somewhat unbiased, or perhaps lets say less biased, point-of-view.
MvdP has updated his zones definition. Now it looks more realistic when compared to timepoint in season and TT results from previous races. Of course, we don’t know if AeT aligns with the 5 zones definition.
However, given his weight and superior level something fairly far above 300W is really plausible. This also puts all his recent endurance rides which we’ve seen here into perspective. But it should not be forgotten, while lower Z2 for him, it’s stll metabolically extremely demanding. A challenge for bigger/larger riders.
I’ve got a neighbor about my size (90+kg) and his ftp is something like 175W, so his endurance rides are in the 100-120W while my endurance target is 175-210W. Being bigger is no guarantee of a higher ftp. However in the pro/elite ranks I think it might be relative safe to generalize and say bigger usually has higher ftp?
Larger people are only able to process a small percentage more calories than a smaller person per hour. It’s this reason that smaller riders have an advantage over longer hilly races. Particularly, if they have long sustained climbs. The larger riders are not able to match the calories per kg (c/kg, I just invented that) of the smaller riders. Regardless of having the exact same w/kg metrics. They simply aren’t able to replace the lost glycogen as fast as the smaller riders over time.
Hence, in the modern era. Long stages races are ‘generally’ won by small or very lightweight riders.
For one day races, particularly flat terrain. This is mostly irrelevant. Power usually wins. So, there is hope for the heavies
Just aim at Paris Roubaix, ain’t no micro climber winning that anytime soon. Not including Pogacar. He’s not subject to conventional metrics, he’s actually a robot.
I get it you don’t like an observation made by seiler, but anecdotal evidence in this thread does nothing to disprove what is a general consensus about tid. Tveiten 's results are more applicable to the population than how mvdp trains.
