40 Goals

Over January 2019 I implemented the Banister model in GoldenCheetah, along the way I learned a little about its strengths and weaknesses. 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. 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. In some ways this post is a longer written form of this tutorial I recorded covering Banister and GoldenCheetah. The Banister Impulse Response model 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. Originally proposed for working with collegiate swimmers it was reworked in 1990 for working with running and of course also applicable for cycling. Each type of sport needed a w

In this post I'm going to describe a new metric Power Index , which is used to find maximal efforts in general data without any knowledge of an athlete and a Submaximal Effort Filtering Algorithm using a modified form of a convex hull search algorithm. These approaches were developed to support the implementation of the Banister IR model in GoldenCheetah . Performance testing is useful When it comes to tracking and modelling performance you can't beat a performance test. Whether it's a set of tests to exhaustion, a 40km TT or just tracking your time up a local hill, they're really useful. If your performance improves then you know your training is working, if your performance goes down then maybe you're fatigued or losing fitness with too little training. Either way you have concrete evidence. And it should be no surprise that performance tests are the main inputs into most performance models. If we want to model and predict maximal performance, generally we n