Training load monitoring in team sports: a practical approach to addressing missing data

Training load (TL) is a modifiable risk factor that may provide practitioners with opportunities to mitigate injury risk and increase sports performance. A regular problem encountered by practitioners, however, is the issue of missing TL data. The purpose of this study was to examine the impact of missing TL data in team sports and to offer a practical and effective method of missing value imputation (MVI) to address this. Session rating of perceived exertion (sRPE) data from 10 male professional soccer players (age, 24.8 +/- 5.0 years; height, 181.2 +/- 5.1 cm; mass, 78.7 +/- 6.4 kg) were collected over a 32-week season. Data were randomly removed at a range of 5-50% in increments of 5% and data were imputed using 12 MVI methods. Performance was measured using the normalized root-mean-square error and mean of absolute deviations. The best-fitting MVI method across all levels of missingness was Daily Team Mean (DTMean). Not addressing missing sRPE data may lead to more inaccurate calculations of other TL metrics (e.g., acute chronic workload ratio, training monotony, training strain). The DTMean MVI method may provide practitioners with a practical and effective approach to addressing the negative consequences of missing TL data.

Automated summary

This study explores the impact of missing training load data in team sports and presents a practical and effective method of missing value imputation to address this issue. The Daily Team Mean imputation method was found to be the best-fitting across all levels of missingness, providing practitioners with a useful tool to prevent inaccurate calculations and negative consequences of missing TL data.