1) Chronic training load, or CTL, provides a measure of how much an athlete has been training (taking into consideration both volume and intensity) historically, or chronically. It is calculated as an exponentially-weighted moving average of daily TSS (or TRIMP, etc.) values, with the default time constant set to 42 d. CTL can therefore be viewed as analogous to the positive effect of training on performance in the impulse-response model, i.e., the first integral term in Eq. 1, with the caveat that CTL is a relative indicator of changes in performance ability due to changes in fitness, not an absolute predictor (since the gain factor, ka (or k1), has been eliminated).
2) Acute training load, or ATL, provides a measure of how much an athlete has been training (again, taking into consideration both volume and intensity) recently, or acutely. It is calculated as an exponentially-weighted moving average of daily TSS (or TRIMP, etc.) values, with the default time constant set to 7 d. ATL can therefore be viewed as analogous to the negative effect of training on performance in the impulse-response model, i.e., the second integral term in Eq. 1, with the caveat that ATL is a relative indicator of changes in performance ability due to fatigue, not an absolute predictor (since the gain factor, kf (or k2), has been eliminated).
3) Training stress balance, or TSB, is, as the name suggests, the difference between CTL and ATL, i.e., TSB = CTL – ATL. TSB provides a measure of how much an athlete has been training recently, or acutely, compared to how much they have been training historically, or chronically. While it is tempting to consider TSB as analogous to the output of the impulse-response model, i.e., as a predictor of actual performance ability, the elimination of the gain factors ka and kf (or k1 and k2) means that it is really better viewed as an indicator of how fully-adapted an individual is to their recent training load, i.e., how “fresh” they are likely to be.
http://www.cyclingpeakssoftware.com/...gerscience.asp
Part 3 addresses the limitation of the performance manager that inspired my thread. The performance manager does not attempt to predict performance whereas the TRIMP system did.
The equations for exponentially weighted moving averages and the required smoothing factors are in my thread.