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

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.