Citation

Tenan MS. Sports Med. 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, Adis International)

DOI

10.1007/s40279-023-01829-w

PMID

37027076

Abstract

Data are often recorded from athletes to make decisions regarding the mitigation of injuries or the enhancement of performance. However, data collection in the real-world is difficult, and it is common for data to be missing from a particular training session due to equipment malfunction, athlete non-compliance, etc. The statistical community has long recognized that proper handling of missing data is vital to unbiased analyses and decision making, yet most dashboards in sport science and medicine do not recognize the issues introduced by missing data and practitioners are largely unaware that their displays are conveying biased information. The goal of this leading article is to show how real-world data can violate the 'missing completely at random' assumption in an American Football example and then demonstrate some potential imputation solutions which appear to maintain the underlying properties of the data in the presence of missingness. Whether data are aggregated on a dashboard as simple histograms and averages or with higher-level analytics, violating the 'missing completely at random' assumption results in a biased dashboard. Practitioners need to insist that dashboard developers perform missing data analyses and impute data as needed so valid data-driven decisions can be made.

Language: en