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From Fellrnr.com, Running tips
→Flaws in the TSB model
** K<sub>a</sub> - the scaling constant for acute (fatigue) effect of training
** K<sub>f</sub> - the scaling constant for fitness effect of training.
** The ratio of K<sub>a</sub> to K<sub>f</sub> defines the benefit of training. If they are both the same value, then you get no benefit from training, but if K<sub>a</sub> is larger than K<sub>f</sub> then training improves your performance over time. ** If you consider the case of someone starting to exercise exactly the same amount every day, you’d find a change in CTL and ATL that would reach a steady level over time. Without a difference in K<sub>a</sub>/K<sub>f</sub>, the values of would cancel each other out, so CTL-ATL would be zero, showing no improvement from that exercise. See the graphs below.* A third flaw is that TSB assumes a single value for that represents the adaptation to performance. In practice, different types of adaptation occur at different rates. {| class="wikitable" |- valign="top"|[[File:TSB-KaKfSame.jpg|right|none|500px|A graph showing someone start an exercise program with the same workout every day. You can see that the TSB (green line) returns to zero, indicating no improvement in performance.]]|[[File:TSB-KaKfDiff.jpg|right|none|500px|This is the same exercise, but with K<sub>f</sub> twice the value of K<sub>a</sub>. Now the exercise program predicts an improvement in performance.]]|}
=An Advanced TSB=
One approach to addressing these flaws is to use the calculated [[Training Monotony]] to change the TSB calculation. Higher levels of monotony should reduce the CTL value, increase the ATL value, and increase the time constant used to reduce the effect of a workout on ATL. I've built this into my [[SportTracks Dailymile Plugin]].
