Comparison of imputation methods for missing rate of perceived exertion data in rugby

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dc.contributor.authorEpp-Stobbe, Amarah
dc.contributor.authorTsai, Ming-Chang
dc.contributor.authorKlimstra, Marc
dc.date.accessioned2022-11-04T17:40:48Z
dc.date.available2022-11-04T17:40:48Z
dc.date.copyright2022en_US
dc.date.issued2022
dc.description.abstractRate of perceived exertion (RPE) is used to calculate athlete load. Incomplete load data, due to missing athlete-reported RPE, can increase injury risk. The current standard for missing RPE imputation is daily team mean substitution. However, RPE reflects an individual’s effort; group mean substitution may be suboptimal. This investigation assessed an ideal method for imputing RPE. A total of 987 datasets were collected from women’s rugby sevens competitions. Daily team mean substitution, k-nearest neighbours, random forest, support vector machine, neural network, linear, stepwise, lasso, ridge, and elastic net regression models were assessed at different missingness levels. Statistical equivalence of true and imputed scores by model were evaluated. An ANOVA of accuracy by model and missingness was completed. While all models were equivalent to the true RPE, differences by model existed. Daily team mean substitution was the poorest performing model, and random forest, the best. Accuracy was low in all models, affirming RPE as multifaceted and requiring quantification of potentially overlapping factors. While group mean substitution is discouraged, practitioners are recommended to scrutinize any imputation method relating to athlete load.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThis work was part of a project funded by Mitacs. The Mitacs Accelerate PhD Fellowship has been awarded to A.E.-S., and the project was supervised by M.K. and M.-C.T.en_US
dc.identifier.citationEpp-Stobbe, A., Tsai, M., & Klimstra, M. (2022). “Comparison of imputation methods for missing rate of perceived exertion data in rugby.” Machine Learning & Knowledge Extraction, 4(4), 827-838. https://doi.org/10.3390/make4040041en_US
dc.identifier.urihttps://doi.org/10.3390/make4040041
dc.identifier.urihttp://hdl.handle.net/1828/14390
dc.language.isoenen_US
dc.publisherMachine Learning & Knowledge Extractionen_US
dc.subjectsports
dc.subjectfootball
dc.subjectathletic performance
dc.subjectstatistical models
dc.subjectmachine learning
dc.subject.departmentSchool of Exercise Science, Physical and Health Education
dc.titleComparison of imputation methods for missing rate of perceived exertion data in rugbyen_US
dc.typeArticleen_US

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