Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland.
Health Research Institute, University of Limerick, Limerick, Ireland.
J Sports Sci. 2021 Oct;39(19):2161-2171. doi: 10.1080/02640414.2021.1923205. Epub 2021 May 10.
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.
训练负荷（TL）是一个可改变的风险因素，它可以为从业者提供减轻受伤风险和提高运动表现的机会。然而，从业者经常遇到的一个问题是 TL 数据缺失的问题。本研究的目的是检查团队运动中缺失 TL 数据的影响，并提供一种实用有效的缺失值插补（MVI）方法来解决这个问题。收集了 10 名男性职业足球运动员（年龄 24.8±5.0 岁；身高 181.2±5.1cm；体重 78.7±6.4kg）在 32 周赛季中的 10 名男性职业足球运动员的每节感知用力评分（sRPE）数据。数据以 5%至 50%的幅度随机缺失，增量为 5%，并使用 12 种 MVI 方法进行插补。使用归一化均方根误差和平均绝对偏差来衡量表现。在所有缺失水平下，最适合的 MVI 方法是每日团队平均值（DTMean）。不处理缺失的 sRPE 数据可能会导致其他 TL 指标（例如，急性慢性工作量比、训练单调性、训练紧张度）的计算更不准确。DTMean MVI 方法可为从业者提供一种实用有效的方法来解决缺失 TL 数据的负面影响。
