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Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models.

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Summary

Artificial neural networks (ANNs) offer superior backstroke start performance prediction compared to linear models. ANNs provide more robust and accurate predictions of 5-meter start times for elite swimmers.

Keywords:
artificial neural networkscompetitive swimmingkinematicskineticslinear mathematical modelstart time

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Area of Science:

  • Sports Science
  • Biomechanics
  • Performance Analysis

Background:

  • Accurate prediction of swimming start performance is crucial for elite athletes.
  • Mathematical modeling aids in understanding and optimizing athletic movements.
  • Comparing linear and non-linear models can reveal superior predictive capabilities.

Purpose of the Study:

  • To compare the predictive accuracy of non-linear (artificial neural networks) and linear mathematical models for backstroke start performance.
  • To evaluate model robustness when applied to different backstroke start techniques (horizontal vs. vertical handgrip).

Main Methods:

  • Ten swimmers performed 15m backstroke starts under controlled conditions.
  • Dual-camera videography and force plates captured kinematic and kinetic data.
  • Artificial neural networks and a linear model were used to predict 5m start times.

Main Results:

  • Artificial neural networks demonstrated greater robustness and accuracy than the linear model across both handgrip conditions.
  • ANNs achieved a significantly smaller mean absolute percentage error in predicting start times.
  • The best ANN validation showed a lower mean absolute error compared to the linear model.

Conclusions:

  • Artificial neural networks are recommended for predicting backstroke start times due to their superior accuracy and robustness.
  • The findings highlight the potential of ANNs in analyzing and optimizing elite swimming performance.