40 Goals

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...

Back around the year 2000 or so power meter usage was in its infancy with SRM and Powertap devices becoming available at almost affordable prices. At the same time access to the internet was becoming increasingly popular with the emergence of ISPs such as AOL and Compuserve. A few relatively well-heeled and tech savvy early adopters bought power meters and joined a growing online community to share their experiences using and working with these new devices. One early community was a topica mailing list that went on to become the wattage google forum .  The early discussions there focussed mostly on hardware, firmware, calibration, installation and so on. Eventually the forum became dominated by discussions related to analysis of data using CyclingPeaks metrics such as NP, TSS and so on . Looking back at the discussions there it has been saddening to see how little the literature ever featured. Over time some of the discussions there became encoded in self-titled ' pithy p...

In this post I'm going to explain what the GoldenCheetah OpenData project is and how you can work with the data it has collected using Jupyter notebooks . GoldenCheetah OpenData Project Large collections of sports workout data are generally not open to the general public. Popular sites like Strava, TodaysPlan, TrainingPeaks collect large volumes of athlete data, but quite rightly do not publish this data publicly. But there is a growing appetite for such data, to inform development of new tools and to feed into models and machine learning algorithms. So I started a project to do it, the GoldenCheetah OpenData project. My first priority was to make sure we did the right thing, in the right way to protect user privacy and comply with GDPR regulations. As a result, we anonymise all the data before sending it out of GoldenCheetah and remove personally identifiable information and personal metadata. Crucially, we get the user's explicit consent to share anything (and offer opt...