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Related Experiment Video

Updated: Apr 4, 2026

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Predictive Modeling in Race Walking.

Krzysztof Wiktorowicz1, Krzysztof Przednowek2, Lesław Lassota2

  • 1Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, 35-959 Rzeszów, Poland.

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Summary

This study introduces advanced multivariable models to predict race walking performance based on training loads. A modified LASSO regression with nonlinear terms achieved the smallest prediction error, optimizing training strategies.

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Area of Science:

  • Sports Science
  • Biomechanical Engineering
  • Data Science

Background:

  • Optimizing athletic training requires accurate performance prediction.
  • Traditional linear models may not capture the complex relationship between training load and race outcomes.

Purpose of the Study:

  • To develop and compare linear and nonlinear multivariable models for predicting race walker performance.
  • To identify a model that minimizes prediction error for 3km race results based on training data.

Main Methods:

  • Collected training data from 21 race walkers over 122 training plans.
  • Utilized linear and nonlinear multivariable modeling techniques.
  • Employed leave-one-out cross-validation to select the optimal model.
  • Investigated nonlinear modifications to linear models, including quadratic terms.

Main Results:

  • Nonlinear models demonstrated improved prediction accuracy over linear models.
  • A modified LASSO regression incorporating quadratic terms yielded the smallest prediction error.
  • This optimal model also simplified the structure by removing non-significant predictors.

Conclusions:

  • Nonlinear multivariable models offer superior prediction capabilities for race walker performance compared to linear models.
  • The proposed modified LASSO regression provides an effective tool for optimizing race walking training programs.
  • Data-driven modeling can significantly enhance athletic performance prediction and training efficiency.