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Getting the Most From Your Data: Using Statistical Process Controls for Data Quality Assurance in Sport Science Data.

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Statistical process control (SPC) enhances sport science data quality by monitoring performance metrics and identifying anomalies. This method helps ensure reliable data for athlete welfare and decision-making.

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Area of Science:

  • Sport Science
  • Data Analytics
  • Quality Assurance

Background:

  • The surge in sport data allows for detailed player performance and injury monitoring.
  • Leveraging this data requires robust methods for quality assurance.
  • Statistical Process Control (SPC) offers a framework for monitoring and improving data quality.

Purpose of the Study:

  • To demonstrate the application of SPC in sport science for data quality assurance.
  • To provide practical guidance for sports practitioners using SPC.
  • To offer a template for implementing SPC with sport-specific data.

Main Methods:

  • Utilized Statistical Process Control (SPC), a quality control method.
  • Employed run charts with mean centerlines and control limits for visualization.
  • Applied SPC to sport science data, including simulated examples.

Main Results:

  • SPC effectively visualizes data trends and identifies deviations from the norm.
  • Control limits in SPC help distinguish common cause variation from special cause variation.
  • Demonstrated SPC's utility in detecting potential issues in sport performance data.

Conclusions:

  • SPC is a valuable tool for ensuring data quality and integrity in sport science.
  • Practitioners can use SPC to monitor athlete data and inform performance decisions.
  • The methodology presented serves as a practical guide for implementing SPC in sports settings.