How to Easily Make Sense of Your Training Load Data Using TSB

How to Easily Make Sense of Your Training Load Data Using TSB

Training Stress Balance (TSB) is a concept I first heard of in Training and Racing with Power Meter by Allen and Coggan in 2010 and I immediately found it very interesting and tried implementing couple of times.

TSB revolves, similar to Banister model, around Chronic (CTL) and Acute training load (ATL). Chronic load is usually the rolling average of the last 4-6 weeks (this is called time constant) and represents “baseline” and current level of fitness. Acute training load is usually rolling average of the last 5-14 days (usually 7) and represents “freshness” (or opposite, “overload”).

Training Stress Balance is then TSB = CTL – ATL, and represents their “interaction”, basically hinting about how much are the recent loads higher/lower compared to more chronic loads. If one increases ATL too quickly and too much over the CTL, one will probably suffer from fatigue-related problems (and possibly injuries). If ATL is below CTL, one will probably feel much fresher (as in tapering), but will soon start de-training.

The goal of training is hence to slowly raise (or keep relatively constant) CTL (or as I would like to say “raising the floor” while other might call it “slow cooking the athlete”) with occasional high/fast increases in ATL either to peak or to provide overload.

I have been playing with TSB and tried to actually predict the performance, but I haven’t got any better results than Banister model. The TSB model is very simple:

Test Result = CTL * k1 – ATL * k2 + test_result0

One needs to optimize five parameters to find the smallest sum of squares:

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I am a physical preparation coach from Belgrade, Serbia, grow up in Pula, Croatia (which I consider my home town). I was involved in physical preparation of professional, amateur and recreational athletes of various ages in sports such as basketball, soccer, volleyball, martial arts and tennis. Read More »

Home Forums How to easily make sense of your training load data using TSB

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