With the introduction of the Adaptive Training (AT) beta questions have been raised about the underlying algorithms. One recurring response has been to assume AT is a black-box controlled by artificial intelligence:
It must be nice to be an AI and not have to explain yourself. Just spit out a result and produce a “new baseline. Trust it. Bleep!” But nobody does, yet. It’s going to take TR a long time to wean us off the explanations everybody is asking for.
It feels counter-intuitive though that modern training logic is suddenly obsolete and replaced by black-box-AI that cannot be explained. While acknowledging that this is a beta release with some known bugs, it is still interesting to explore the underlying logic. Below a best effort attempt in four graphs.
The TR team added Progression Level (PL) scores to existing and new workouts. Note that these scores do not change with FTP and are a fixed workout parameter, similar to TSS and IF. As TR plans historically were based on gradually increasing TSS over time, it is reasonable to expect a correlation between PL and TSS. The graph below shows indeed a strong linear correlation between the two parameters for sweet spot (SS) and threshold (TH) workouts. PL is not the same as TSS though, as it is used to scale workouts appropriately across zones and workout durations.
When plotting progression levels recommend by AT as a function of time (taken from the calendar), another linear correlation is visible. AT is programmed to linearly increase TSS over time for SS and TH zones. Missing several workouts for a specific zone leads to a step function down (orange arrow), hitting a breakthrough workout for a specific zone leads to a step function up. Note that contrary to the previous graph, this correlation is likely user dependent. One would expect to see a steeper slope of progression for users with a lower-than-average W/kg and vice-versa.
Effect of updating FTP
This is where the beta release looks pretty buggy, as changing FTP sometimes leads to a full reset of all PLs. But when it does work, it looks like the graph below. Starting from an FTP of 250W – and having successfully completed workouts at this PL – adjustments were made in both directions. When FTP is lowered, the SS PL does not change. This makes sense given the history of completed workouts at 250W FTP. Although the SS PL does not change, the same workouts are performed at lower average power and kJ and are easier to complete, which was the intent of reducing FTP in this situation.
When FTP is increased, the SS PL is adjusted downwards and again along a linear slope. The slope seems to be chosen such that total energy (kJ) of a SS workout at the recommended PL is roughly equal before and after the FTP increase. For the example below, the SS workout Newcomb (60 min, 65 TSS, 0.81 IF, PL 3.1) at 250 FTP (640 kJ) is replaced by Mount Field (60 min, 53 TSS, 0.73 IF, PL 1.6) at 260W FTP (637 kJ).
The link between PL and nominal FTP
Based on the previous graph one can also visualize how changes in SS progression levels and FTP are correlated. The mid-point of the PL scale (1-10) is 5.5. Let’s assume a user that starts at this mid-point with an FTP of 250W. Assume that over time this user improves the SS PL to a score of 9.1. As seen in the graph this equates to a nominal FTP of 110% or 275W. Updating FTP from 250W to 275W will trigger the AT algorithm to reduce SS PL back to the midpoint of 5.5.
In this example an increase in SS PL of +1 can be translated to +2.8% FTP, which is a surprisingly steep slope. But from the second graph above one can see that progression is driven by about +1 SS PL per two weeks, and extrapolating that makes +3 SS PL for a 6-week plan. That is equal to an 8.4% improvement in FTP which is close to the 7.9% average gain per plan reported by users (link). It means users on average should be able to successfully complete several plans without changing FTP, and not changing FTP after every plan could actually be beneficial as it leads to a larger variety in workouts. The steep curve also makes it easier to accommodate users with high PL-variability across zones.
In conclusion, the linear logic used to assign progression levels for SS and TH workouts and adapting progression levels over time seems in-line with TR’s previous plan implementation, while providing more flexibility and a more gradual increase in training load over the course of a plan.
Let me know your thoughts.