Ha! This is pretty well known on the science end of things, though I always like it when other people learn this. It’s why acknowledging uncertainty and not having strong opinions on very specific training matters is probably the best indicator that someone knows what they’re talking about.
You can likely draw some inferences from large datasets like TR has. However, data drawn from databases or registries is fundamentally low-quality evidence that is pseudo-experimental at best.
You can definitely learn from it, and that would be easier with ML, but it’s just lots of data. Not high quality experimental data. It’s the same way how registry data does not supplant the need to perform clinical trials in medicine. The only way we will have certain conclusions is by conducting high-quality primary research. Not retrospective data mining existing data.
There will be fun insights to be had. But I don’t expect it to change the face of training. The answer is still going to be to ride your bike more.
many top-level coaches are apparently completely unperturbed by facts and new insights, and instead want to stick with what they have learnt 20 years ago.
Many athletes as well. And people in general. It’s another reason why the fundamentals of training are unlikely to change significantly. Most people just do whatever they feel like doing and then find creative ways to justify it.
Maybe its really about how fast you pedal your bike. Is high cadence climbing gonna make a return??? That was of course what helped Lance win 7 tours…or at least that is what all the forum chatter was back in the mid 00’s
No doubt there’s some truth here but it is also a pretty arrogant take on the researchers part. Coaches are on the front lines, they know what works because they have seen the long term results. How many researchers collect data for an athletes entire career? The reality is coaches know what work, physiologists try to figure out why. Science has never driven training… it’s the coaches and athletes.
Here is a great article on the history of long distance training by Steve Magness. Reality is there isn’t really anything new… but is rather more of a cycle. Maybe we are just at the end of that 20 years and about to see something old become new again.
Yes, but I was still surprised by what is actually not yet established. The discussion centered around super compensation, and according to this researcher, super compensation has been established in terms of glycogen processing capabilities (I cannot recall the specifics with sufficient certainty).
I have colleagues in astronomy, dealing with large datasets or datasets with large margins of error is by no means new. Although I have to admit, it is a very different way of thinking.
In sports science the N is typically very, very small, especially once you go in-depth and e. g. insist that people use that fancy SRM lab-grade ergometer. The sheer number of questions you could answer with TR’s database is quite intriguing.
I noticed this in a few places (as a layperson). E. g. Alex Dowsett complained that some of his teams were clueless or didn’t care about about basics like aerodynamics and nutrition. And many of the athletes who are pushing the boundaries have taken an active interest in optimization.
Thing thing is, they all know what works. And it’s not super complicated. I’d refer anyone to the last Empirical Cycling podcast where Kolie and Rory sketch out two basic build plans: endurance (base), threshold - build out TTE, VO2, race prep (short intervals), taper. It’s classic build the house, raise the roof (vo2) stuff. Kolie even says that endurance and threshold (of any variety (tempo, sweet spot, ftp)) is the ‘peanut butter and jelly’ of training.
Everything is made out to be so complicated but if the average amateur rider just did the PB&J of training they’d reach 95% of their potential without bespoke coaching, magic intervals, crazy complicated over/under sets, AI, HRV, whoop, or any other fancy gizmo.
Magness has finally launched a YT channel and he’s been doing these 30 minute deep dives on training. They are really great IMO.
Recently, I really liked his mini lecture on the history of endurance training.
I think it applies to cycling as the basics are pretty similar.
I also want to say that I suspect that the value of training data is probably pretty low. TR, TP, Zwift, Strava, Wahoo, Garmin, Intervals.icu, etc. all have massive amounts of training data.
How is anyone monetizing it?
Is the value of the data of 1M athletes any better than that of 500 athletes?
These companies have had our data for years, if not decades and they have come up with zilch to actually improve training. TR has had to come up with AI FTP guesstimating because people don’t want to test. They had to come up with red light / green light because people can’t tell that their legs are fried and that they should hold back.
So, I’m not optimistic that any company will come up with some game changing software or device that fundamentally changes training.
Hard agree. Apart from the fundamental principles that “more volume is better than less volume”, “at some point you’re going to.have to go harder”, and “you can only do what you can recover from” pretty much (or absolutely) nothing in big data will help you if you’ve got to the intermediate stage and are looking to push on from there.
Humans are weird. They respond very differently to training stimuli. What’s perfect for Rider A might not work at all for Rider B, even if they’re practically indistinguishable. Ultimately, we’re all an experiment with 1 subject and we’re all going to have to FAFO once the noob gains have been milked.
I’ve had a coach before that I could call or text at any time. I didn’t, because I don’t really need their opinion 24/7.
Training isn’t ever an emergency.
You may personally like the ability to ask someone a question at 3am, but I can’t for the life of me think of a situation where not getting a response immediately will have any significant impact on your training.
Same with nutrition. If you’re just talking from a performance perspective, like 99% of nutrition is simply “eat enough.”
So you may like having a product that does this, but it won’t have any noticeable impact on your training, which is more what I was trying to refer to in my comment.
Also, LLMs already exist which can basically do what you’re suggesting - answering a question in the middle of the night, asking what meals you can cook based on available ingredients. Just play around with ChatGPT 4.
Though I also wonder whether simple questions like you suggested are more effectively answered by googling and learning about the topic, than from asking an LLM.
These people do exist, not many but a few. They would be schooled in all those aspects. The ambition was to be a coach to top level pros. For many reasons it never happened. You can find them at top level fitness centers. They can be pricy but then we spend big money on marginal gains. 1st world problems.
You still would have to feed the AI coach all this data. You’d have to log all of your food or maybe wear the AI coach so that they can record and analyze your every move and every bite of food. You’d have to respond to RPE surveys accurately and not try and game the AI like TR users often try to do. You’d have to sleep with a sleep monitor and maybe wear a chest strap 24/7. You might even have to do an actual FTP test.
I think we are a long, long way off from this level of virtual coach.
My read of the article is a bit different: it is a pendulum that swings one way or another (e. g. high volume at low intensity vs. intervals), but the amplitude decreases (the pendulum does not swing from extreme to the other, it does get closer to a middle ground). Instead you see an evolution of training principles, which are kept and adapted.
I think looking back at the last decade, I expect nutrition is going to be a major theme. Athletes and coaches will push too hard, test the limits and then back off again. The pendulum will eventually swing back, but not to 40–60 g/h.
You conflate TR‘s approach to training with the validity of its dataset.
You cannot answer some of the questions with TP data. Or, at best, you have a smaller dataset. Some examples that come to mind: compare the relative performance and performance gains with athletes who do less volume, but are more consistent (say 95+ % consistent) vs. those who do more volume, but are only 90+ % consistent. Consistent means that they stick to a training plan as prescribed. You could break this down into gender, age brackets, hours on the bike per week, etc. The dataset is big enough so that you can clean it (e. g. you could omit data collected from single-sided power meters or known problematic power meters), and still have a good chance to obtain good sample sizes.
Many studies in sports sciences are conducted with more average, fit individuals and not necessarily with top-level athletes. These studies reveal information about the inner workings of the human body, which could still be applicable to top-level athletes.
TR’s dataset is the largest that I am aware of. If you are only interested in workouts completed, there are bigger ones out there, but if you want data on adherence to given training plans, I suspect it is the biggest.
“Sources without bias”, that’s something that doesn’t exist. Scientists are used to dealing with biased samples, systematic errors in sources and the like.
IMHO that’s too extreme a take. This center has decades worth of experience working with many, many top athletes and many, many top-level coaches. Part of their day-to-day work is to support top-level coaches and athletes. They have their own institutional experience to contribute, which is different from any single coach. Many of them were/are top-level athletes themselves. I’d also be careful to characterize a one-sentence summary of a long discussion.
In my experience (different field), there is a surprising amount of arrogance (e. g. theoretical and experimental physicists talk less and in many cases do not hold each other in high regard, which I still find surprising). Coaches that are resistant to new knowledge are no better than sports scientists who are disdainful towards coaches. Interesting stuff happens when both sides are open to trying new ideas.
I don’t see anything particularly significant about Strava at all.
What we see now from the UK is that the USA seems to be focussed on getting muscles and obeying the automobile, and that’s pushing the weightlifting and off roading trend. Thats fine for USA and countries that model themselves that way.
In Europe we see the opposite, a trend to reclaim the streets from cars and make the roads more accessible and useful to things that aren’t cars - so we should see an uptick in road cycling which will enable more road racing.
The vanity and bodyshaming my generation tried to alleviate from daughters has instead been replicate manyfold onto our sons. Getting ripped has already landed in our teenage males so that will dominate and reoccur in 20 years as they hit their forties.
Online training will shift from this piece of code to that piece of code, and like disc brakes and electronic shifting, the claims will far exceed the gains as they compete to release new features every season to win market share as is the way of things.
I’m interested to see what happens with in person clubs as they are dominated by old people in my area, maybe that will continue as people seek to avoid the effects of aging - but younger people are already finding new ways to train and socialise, I think the flashmob running events were really innovative.
I hope that the success we’re seeing in accepting neurodiversity will be replicated in our schools and sports and expanded to accepting physical diversity too. The hundred plus year obsession with body types determining sporting success just needs to go away.
Moreover the obsession with records and winners that drives doping needs to be replaced with a new found respect for athletes wherever they finish a race. International racing will start to focus on countries with a believable anti doping program and a professionalised attitude to sports so that people can expect a career sports rather than hoping a win will take them out of poverty.
Next week looks like more base work. A few weeks after that. Some days in the basement doing whatever TR says. Been doing it since 2016. Probably do it another 15. Then move to e racing only.
Strava’s capturing of local knowledge of the best routes to ride in a given area in its heat maps is pretty amazing. After that it’s just a fifth rate lame social media site where 99% of its users don’t use any of the social media aspects.
