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=Flaws in the TSB model=
There are a number of practical flaws in the TSB model. * The first flaw in the TSB model is that it ignores [[Overtraining]], as and predicts that any possible workload produces improved fitness. The second and somewhat related flaw Related to this problem is that it the model ignores [[Training Monotony]]. Research has shown that training with a high level of monotony reduces the benefits and increases the fatigue from a given level of training. * The second flaw is that the values for the constants used in the model are specific to each individual, and possibly to the particular training regime. The verification of the model reverse engineered the constants so that the model accurately predicted the performance changes. These constants are represented below as ** N<sub>a</sub> - the decay constant for acute (fatigue) effect of training.** N<sub>f</sub> - the decay constant for fitness effect of training.** 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.
=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]].