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Performance Modelling in Sports Science

Translation of a complex physiological performance model into a high-performance software simulation – with optimisation algorithms, parallelisation, and interactive results visualisation.

Client/Company/Industry

TRAINALYZED GmbH

Duration

49 months

Product

Software

Expertise

Software Development

Goal

TRAINALYZED develops software for training and nutrition planning based on physiological data. At the core of their product is a mathematical model that derives precise statements about athletic performance and training effects from measured data. The goal was to translate this model into a scalable, high-performance software simulation and integrate it into the existing web platform.

Tasks

  • Translation of the mathematical model into a software simulation using Python, NumPy, Pandas, and Jupyter Notebook
  • Research into mathematical solution and optimisation methods using SciPy and SymPy
  • Algorithm design and adaptation of existing optimisation procedures
  • Ongoing integration of proof-of-concept implementations into the simulation architecture
  • Dependency and sensitivity analyses to explore the parameter space
  • Results visualisation with Matplotlib and Bokeh
  • Development of file import routines for domain-specific formats (fitparse, Cheetah)
  • Reduction of computation times through parallelisation and JIT compilation with Numba and multiprocessing

Challenges

The project had a strong research character: translating a complex mathematical model into working software required not only programming skills but also deep understanding of the underlying physiology and mathematics.

The high number of independent model parameters made advanced optimisation algorithms necessary. At the same time, the simulation needed to remain scalable as data throughput increased - requiring targeted performance optimisations through JIT compilation and parallelisation.

Programming Languages

Python

Technologies

NumPy, SciPy, Pandas, Numba, Bokeh, Jupyter Notebook, Django

Project Image

Symbolic image of digital performance diagnostics.

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Takeaway

The result is a robust, scalable simulation that delivers precise performance predictions for athletes and patients based on physiological data. The combination of scientific modelling, algorithm optimisation, and efficient implementation forms the analytical core of the TRAINALYZED product.

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