Author: Jo Clubb
Source: YouTube (2026)

This video from Global Performance Insights provides a comprehensive guide on how to use the Smallest Worthwhile Change (SWC) to interpret athlete monitoring data. It moves beyond traditional P-values to focus on practical, meaningful changes in performance.

Core Concepts of Smallest Worthwhile Change (SWC)

• Definition: SWC represents the smallest change in a variable (e.g., jump height) that is considered practically meaningful, helping practitioners distinguish between “measurement noise” and real biological change [01:37].
• Calculation: For team sports, it is commonly calculated as 0.2 times the standard deviation of the group [01:57].
• Magnitudes of Change: Rather than a simple “yes/no” threshold, changes can be categorized based on the SWC [03:04]:
  • Small: 1x SWC
  • Moderate: 3x SWC
  • Large: 6x SWC
  • Very Large: 10x SWC

Practical Application & Tools

The video demonstrates how to implement these concepts using the Action Apps athlete data management system, which integrates with PowerBI for custom visualizations [04:53].

• Squad Dashboards: Allow coaches to quickly identify which athletes have had meaningful increases or decreases in metrics like countermovement jump height or depth [06:29].
• Individual Tracking: The presenter shows how to combine SWC with Z-scores (comparing a data point to an athlete’s own history) to flag individuals who may be experiencing fatigue or positive adaptation [09:12]. For example, an athlete with a Z-score below -1.5 and a “small decrease” in output via SWC should be investigated [09:44].

Typical Error and Measurement Noise

• Typical Error (TE): Represents the normal measurement variability of a test [11:09].
• Combining SWC + TE: To be more confident that a change is “real,” some practitioners suggest only flagging changes that exceed the SWC plus the Typical Error [11:47].

Key Takeaway

Not every change in athlete data matters. By embedding SWC logic into monitoring dashboards, sports scientists can move away from “statistically significant” and focus on practically significant insights that directly inform training and recovery decisions [13:16].

This summary was generated with the assistance of Gemini based on the original article, with the aim of translating the research into practical insights for coaches and practitioners.