Quant Library · The athlete · Any
Fitness-Fatigue and the Taper
Two leaky buckets, and the day the gap between them is widest.
Banister's impulse-response model of training, with the taper search that finds when to cut load and by how much.
Inputs
2What it takes
| Name | Type | Units | Where it comes from |
|---|---|---|---|
| daily load | list | arbitrary units | your training log |
| tau_fitness, tau_fatigue, k optional | floats | days, days, - | fitted per athlete, or 45, 15 and 2 |
Outputs
2What it gives back
| Name | Type | Units | Notes |
|---|---|---|---|
| fitness, fatigue, performance | arrays | arbitrary units | |
| best taper | (length, cut) | days, fraction |
Method
How it works
- Convolve the load with two decaying exponentials, one slow and one fast.
- 2 further steps in the licensed specification
Assumptions
- Training effects add linearly and decay exponentially, which is a summary rather than a physiology.
Limits
- The units are arbitrary: the model ranks plans, it does not predict times. Fitting the constants needs a season of honest load and test data.
In the package
- The full specification: inputs, method, assumptions and limits
- The validation panel as measured on the day of purchase
- A reference implementation extracted from the running source
- Perpetual commercial use for one entity
Reference modules:
- 1 module(s), named in the licensed specification and shipped in the package
Read this before you buy. The platform's own walk-forward tests say the closing line forecasts football better than these models do. What they give you is a coherent price for every market and match, before the market opens, with the band and the working attached. Any model here that has been beaten by something simpler says so in its limits, and the validation panel above is generated from the platform's result files rather than typed in.