Movement Variability

Learn about Movement Variability and how it is displayed via standard deviation in our reports.

Written by Matthew Kowalski
Updated over a week ago

The standard deviation is a measure of the dispersion of a set of values around its average (i.e., the variability of movement between each pitch). Therefore, a larger standard deviation reflects a wider range of joint angles (or velocities) at a given point in time.

Understanding the causes and consequences of movement variability is very much an active area of research. Traditionally, practitioners wanted to remove all variability between pitches so that each pitch looks like the “ideal”. Furthermore, it was assumed that as people become fatigued, their variability must increase as they move away from the “ideal” pitch. However, more recent research suggests that there is no single “ideal” pitch for all individuals, and the standard deviations may increase or decrease with fatigue depending on the kinematic variable. Therefore, a small or large standard deviation is not necessarily a good or bad thing, but instead requires a more nuanced interpretation.

For example, it is advantageous to use more shoulder external rotation during a pitch since this allows the pitcher to throw harder. Thus, we would hope to see a large and consistent (i.e., low standard deviation) external rotation measurement between pitches. Conversely, it may be advantageous for other secondary variables for pitching, such as lead knee flexion, to be relatively more variable between pitches to disguise the timing of the pitch and distribute loads throughout different parts of the lower extremity. Thus, we may hope to see relatively larger standard deviations in the knee flexion demonstrated between pitches. However, when pitchers get fatigued, previous research has shown that external rotation variability may increase, but knee flexion variability may decrease (Grantham et al., 2014), resulting in decreased pitching performance.

Although the previous paragraph highlights a relatively narrow example, it highlights the general point that interpreting the variability in movement using the standard deviation is context and metric dependent.

Did this answer your question?