Thanks STJ!

Sure thing!

Best way to think of it would be a traditional scatterplot with training load on the x axis and some performance measure on the y - it could be a race, an FTP test etc. If you place all of your tests on there you'll probably have a chart of somewhat escalating dots, i.e. as load goes up performance will tend to generally go up to a point. Importantly, the shape of this 'generally going up' will vary between athletes (& it might even go down at some point!)

If we draw a 'line of best fit' through these points we have a basic predictive model. The accuracy of the model in any complex system (e.g. Human Physiology :-), will in some part be related to the 'bendiness' of the line. A neural network is bendy, a banister model straight.

When we talk about the 'predictive capability' of a model, we're talking about how far each true point lies from the line. The overall model error can be measured in the 'root mean square error', i.e. take the distance of each point from the line, square it (to eliminate negatives), take the average of those and then take the square root of that. When we do this, we have a measure for how good our model is (lower RMSE = better model).

While it varies between athletes (as shown in the data table), the average neural network model (for my data-set) has approximately half the error of the average Banister model, i.e. the model line falls an average of 2x closer to each point. So, if the average predictive error is 10W (from the model line to the actual performance) in a given athlete's Banister model it is likely to be closer to 5W when a Neural Network is used.

As it stands, there really isn't a lot out there in the way of predictive modeling/machine learning software for the endurance sports demographic so, yes, at the moment, Neural Networks take a little DIY coding. Speaking more generally about performance modeling software, Phil Skiba's RaceDay was one implementation of the Banister model and I know Mark Liversedge is also implementing in Golden Cheetah, but nothing that I know of on the commercial front using Neural Networks as predictive models at this point. Rest assured, they're coming though! :-)

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Alan Couzens, M.Sc. (Sports Science)
Exercise Physiologist/Coach
Twitter: https://twitter.com/Alan_Couzens
Web: https://alancouzens.com