A central subject of research in sports science and sports practice is the relationship between load and performance - not only in terms of training effort and performance, but also in terms of physiological load and resulting performance in competition.
Numerous models have been developed to measure and/or optimize these relationships. Most of these approaches are based on so-called closed systems, in which the dynamics are described by deterministic functions and the input data are given by a predefined data stream.
In contrast, there is the concept of open systems: Here, the time-dependent result values are not determined by closed functions, but are calculated step-by-step from event-driven state transitions. One advantage of this approach is that changes in system or context conditions can be taken into account during the calculations.
The PerPot (Performance Potential) model uses such a dynamic state-transition approach, which is first briefly introduced using the example of performance optimization in the marathon.
The usability of PerPot for doping detection is then presented using the example of tennis: Here, the effect of doping is simulated using variation of the controlling physiological parameters in order to infer "inconsistencies" in the athlete's physiological profile from the discrepancy between expected and registered performance.

This approach is currently being investigated in the EU project Match Point.