▲ 35 r/fascatcyclingtraining+1 crossposts

The Tour de France Watts/Kg it takes to win each Stage

Hey all - I built a physics model that predicts the watts/kg it'll take to win each stage of the Tour, and I'm going to grade it against what the winners actually do.

How it works: I took the elevation profile of the decisive part of each stage (the final climb, the TT, the sprint windup), estimated the power demand for that effort and duration, and then cross referenced the historical watt/kg numbers we've seen at those durations from real Tour power files. The results are a predicted w/kg for the winning move.

Then after the stage finishes we're gonna calculate what the winner actually produced and put the prediction next to the real number, so you can see how close the physics got. The fun part is watching where it nails it and where the racing breaks the model: crosswinds, a break sticking, a GC truce, a tactical slow climb.

Full disclosure this lives on my company's site: I'm the Head Coach @ FasCat Coaching, so there's a coaching angle on the page. The model is what I wanted to share, and see if you had different thoughts or ideas of how the stages were going to play out.

https://fascatcoaching.com/pages/tour-de-france-watts-per-kg/

What would you weight differently for each stage? Who's your pick for the podium? I'd love to see Jonas and Seixas give Pogacar a challenge

u/frankatfascat — 1 month ago
▲ 26 r/fascatcyclingtraining+1 crossposts

Unbound Gravel Power Analysis from the men's winner: Mads Wurtz Schmidt

We analyzed Mads Wurtz Schmidt's winning Unbound Gravel 200 power file with CoachCat's AI, and his win was all about elite-level Durability: 302w, pushing 1,000kJ's per hour for nine hours and fifteen minutes, with only a 5.5% fade in the last hour. 🤯🤯

Total mechanical work was 9,378 kJ, or 142 kJ/kg after normalizing for his roughly 66 kg. Durability is highly trainable and is a good metric to track in addition to FTP.

FTP is what you can do fresh; Durability is what you can do for hours and hours (in this case analysis)

Read the full analysis here:

https://fascatcoaching.com/blogs/training-tips/unbound-gravel-power-analysis-mads-wurtz-schmidt/

u/frankatfascat — 2 months ago

Inside World Tour AI Programs

AI is the moneyball moment for cycling. Statistics found undervalued players. Multi-year physiological power data analysis finds more watts.

Every other day, there's a headline about World Tour teams and AI. Click through, and you get almost no substance. INEOS + Netcompany announced their partnership in broad strokes. UAE hasn't said a word. Neither has Visma–Lease a Bike.

So I wrote what I believe teams are building behind a veil of secrecy, based on 2 years of building proprietary AI models at FasCat and analyzing close to 1M power files over 20 years.

• Why a generic LLMs on ride files gives you advice that the apps were giving in 2014

• What it takes to train a model on real exercise physiology vs. just feeding it data

• How Olympic champion Kristen Faulkner built her own AI and used it to win 3 golds at Pan Ams

• A 3-layer breakdown of how AI would analyze Stage 20 of the 2026 Tour de France: peak power on Alpe d'Huez, durability after 4,500m of climbing already in the legs, and GC modeling for the DS before the stage starts

Sir Dave Brailsford has said AI will help win the Tour within 5 years. I think its two.

Within 24 months every World Tour team will have an AI program. The teams that build them well will win races. Teams that don't will be dropped.

https://fascatcoaching.com/blogs/training-tips/inside-the-ai-programs-world-tour-teams-are-building/

u/frankatfascat — 2 months ago