TrackIQ – How Power Distribution Really Makes You Faster

TrackIQ is the computational core of RaceYourTrack. The algorithm answers a fundamental question in cycling: How should power be distributed along a route to achieve the fastest possible time without increasing overall load?

Instead of assuming constant power across the entire course, TrackIQ accounts for the real physical differences between individual sections of a route. Climbs, descents, rolling resistance, and aerodynamics all determine how effectively an additional watt is converted into speed. One watt applied on a climb saves significantly more time than the same watt applied at high speed on flat terrain or downhill.

TrackIQ systematically exploits this effect. The route is divided into sections with similar physical characteristics. For each section, the algorithm computes the speed that results from a given power output. Power is then distributed in such a way that total riding time is minimized, while overall load remains controlled.

This load is governed by two intuitive parameters. The Intensity Ratio (%) defines how high the targeted average load may be relative to the individual threshold power. In addition, the Maximum Threshold Power (%) limits how strong individual power peaks are allowed to be. This prevents unrealistic all-out efforts and results in a strategy that can actually be ridden.

The outcome is a segment-based power plan: higher power where it saves significant time, deliberate restraint where additional watts offer little benefit. In many cases, this leads to noticeable time gains without increasing total work—purely through smarter distribution.

Technical Appendix (for Nerds)

At its core, TrackIQ minimizes total ride time along the route. Speed is determined by the applied power and the fundamental resistive forces of the rider–bike system.

Symbolically, the objective can be written as:

$$ \text{Ride time} \rightarrow \min $$

Power influences speed through rolling resistance, gradient, and aerodynamic drag. The route is discretized into segments within which power is assumed to be constant.

Load control is not handled via a free energy budget, but through simple relative constraints:

$$ P_{\text{eff}} = r_{\text{int}} \cdot P_{\text{threshold}} $$

and

$$ P_i \le r_{\text{max}} \cdot P_{\text{threshold}} $$

Here, $r_{\text{int}}$ denotes the Intensity Ratio (%), and $r_{\text{max}}$ the Maximum Threshold Power (%). Additional smoothing terms ensure that power does not change abruptly between neighboring segments.

The result is a time-optimal, physically consistent, and realistically rideable power strategy.