These new coefficients are less interesting as they just translate units. Performance (t) = p0 + PTE(t)* k1 - NTE(t)* k2 So, the formula for Performance curve is actually: So, at this point we need to find some co-efficients to translate from these arbitrary units to those that represent actual performance measures (e.g. This represents performance taking into account adaptations and likely fatigue, but it is still in the arbitray units we used as an input (e.g. If we subtract NTE from PTE we get a third curve the Performance Curve. These are represented in 2 model parameters t1 for PTE decay, and t2 for NTE decay. These curves represent accumulated load, but with different time decay constants - so that NTE residual fatigue will clear in 5-10d, where the PTE adaptations will remain for 30-50d. It is also common to refer to the Positive Influence curve as Positive Training Effect (PTE) or just fitness and the Negative Influence curve as Negative Training Effect (NTE) or just fatigue. As the names suggest they represent accumulated training load that will have a positive impact on performance (by eliciting physiological adaptations) or that has a negative impact on performance (tired or sore legs, residual fatigue). Fortunately, a large proportion of the users of GoldenCheetah have that.įigure 1: Overview of the Banister IR modelĪt the heart of the Banister model are two curves the Positive Influence curve and the Negative influence curve. This of course means it needs a fair amount of historic data to work with. The key element to all of this is that the Banister model learns the individual's response to an impulse and uses that to predict future performance. For cycling a 6 minute TTE test was also used. 1500m) and express the test result as a point score when compared to the world record for that distance (e.g. The test Banister used for running was to perform a maximal effort for a standard distance (e.g. The resulting parameter estimates can then be used to predict future performance. Over time the TRIMPs from workouts are accumulated into a performance curve which is fitted to actual performance tests. To support this he invented a HR based metric called TRIMP to quantify the load of any type of running workout. Each type of sport needed a way of calculating impulse (aka training load) and a way of quantifying performance (e.g. Originally proposed for working with collegiate swimmers it was reworked in 1990 for working with running and of course also applicable for cycling. In 1975 Eric Banister proposed an impulse-response model that could be used to correlate past training with changes in performance in order to predict future improvements from future training. In some ways this post is a longer written form of this tutorial I recorded covering Banister and GoldenCheetah. I've also added a bit at the end covering some of the things I'm looking to do with this next from potential model improvements through to deep learning. This post is about that explaining the Banister model and how it relates to the PMC, how it has been implemented in GoldenCheetah and what it's limitations are. Over January 2019 I implemented the Banister model in GoldenCheetah, along the way I learned a little about its strengths and weaknesses.
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