Endurance athletes have historically generally used either training levels or zones based on an anchor point (typically some form of threshold) or performance models (like the critical power model) to target specific time and power-based training targets. In WKO4, Dr. Andrew Coggan and the rest of the team introduced a new approach focused on blending the best of both of these to optimize training zones by individualizing them to each athlete’s unique physiology.
THE CHALLENGE OF THE CURRENT SYSTEMS
While threshold-based zones are solid, they are a generalization developed through the review of data from thousands of endurance athletes. Statistically speaking, this bell curve process creates a solid basis for a good portion of athletes, but it does not take into account any athlete uniqueness. For example, here are the power duration curves of two different athletes, with lines added to demonstrate where the Coggan classic training levels intersect the curve. Both of these athletes have similar Functional Threshold Power (FTP) and similar fitness.
Athlete 1: From the slow progression of the curve through the training levels, we see the classic curve of a pure steady-state/TTer phenotype. This indicates this phenotype’s ability to extend “time in zone” at levels with little power degradation, without the ability to punch the high watts output per level.
Athlete 2: For the classic sprinter/pursuer phenotype, we see a much steeper progression of the curve through the Coggan classic levels and the ability to punch through high numbers. Should these two athletes be training by the same threshold-based system if the goal is to maximize their training results and performance? Are they getting the same results?
With the introduction of the new iLevels in WKO4, athletes are able to optimize their training levels to their own unique physiology and daily fitness. These new training levels work by blending modeled functional threshold power (mFTP) and modeled power duration data to track with your actual capabilities and ensure that training targets (power and time) are optimized to produce maximal results.
POWER VERSUS DURATION
One of the challenges of the Coggan classic levels is that interval time frames were fixed, based on the desired physiological response. This idea is carried over into the new iLevels while recognizing and recommending a range of interval times that represents an “interval time in zone target.”
POWER VERSUS FITNESS
Since the new iLevels system is driven by the power duration curve and modeled FTP, the training levels will automatically update with any changes (even micro changes) in performance fitness to ensure accurate interval targets as athletes train and de-train. Let’s take a look at our two athletes again and see how their iLevels compare.
We can easily see the differences in both targeted power and duration for work above FTP. These are driven by each athlete’s power duration curve, optimizing the training levels. New Training Levels and Terms By now, you’ve likely noticed that there are some new terms in the training levels and that some of the former terms are no longer used.
There are other articles covering these topics in more detail, but here are some basic definitions.
- Pmax: The maximum amount of power that can be generated for a very short period of time (at least a full pedal revolution with both legs). Units are watts (W) or watts per kilogram (W/kg).
- Functional Reserve Capacity (FRC): The total amount of work that can be done during continuous exercise above FTP before fatigue occurs. Units are kilojoules (kJ) or kilojoules per kilogram (kJ/kg). For example, 1000 watts for one second is one kJ. So if your FRC is 15 kJ you should in theory be able to hold 1000 watts for 15 seconds or 500 watts for 30 seconds.
- Modeled Functional Threshold Power (mFTP): The model-derived highest power a rider can maintain in a quasi-steady-state without fatiguing.
The goal of these changes to the classic training levels approach is simple: we want to help you maximize your training.
This article was written by Tim Cusick. You can read the original blog post here.