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Related Experiment Video

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Swimming Performance Assessment in Fishes
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Streamlining performance prediction: data-driven KPIs in all swimming strokes.

Craig A Staunton1, Michael Romann2, Glenn Björklund1

  • 1Swedish Winter Sports Research Centre, Department of Health Sciences, Mid Sweden University, Östersund, Sweden.

BMC Research Notes
|February 19, 2024
PubMed
Summary
This summary is machine-generated.

Key Performance Indicators (KPIs) for men's swimming, including duration before wall contact (In5) and free swimming speed (FSS), were identified to optimize training. These metrics enhance performance across various swimming strokes.

Keywords:
Competitive swimmingData analysisKey performance indicatorsPerformance predictionTraining strategies

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Area of Science:

  • Sports Science
  • Biomechanics
  • Performance Analytics

Background:

  • Optimizing swimming performance requires identifying key metrics that correlate with race outcomes.
  • Traditional performance analysis often overlooks stroke-specific nuances in elite competition.

Purpose of the Study:

  • To identify Key Performance Indicators (KPIs) for men's swimming strokes using advanced statistical methods.
  • To enhance training strategies and performance optimization in elite male swimmers.
  • To analyze data from the 2019 European Short-Course Swimming Championships.

Main Methods:

  • Principal Component Analysis (PCA) and Multiple Regression Analysis were employed.
  • Data from all men's individual 100m races at the 2019 European Short-Course Championships were analyzed.
  • Bland and Altman analyses were used to assess prediction accuracy.

Main Results:

  • Duration from 5m prior to wall contact (In5) is a universal KPI. Free Swimming Speed (FSS) is key for continuous strokes (Breaststroke, Butterfly).
  • Duration from wall contact to 10m after (Out10) is crucial for touch-turn strokes (Breaststroke, Butterfly).
  • The regression model accurately predicted swim times with negligible bias across all strokes.

Conclusions:

  • Swift turns and consistent speed are critical for optimizing swimming performance.
  • The identified KPIs and predictive model offer a data-driven approach for coaches and athletes.
  • Insights facilitate enhanced training, performance goal-setting, and competition strategy.