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Global and individualized models were constructed using machine learning techniques, with the single best algorithm chosen for each model. The model performance was compared with a baseline intercept-only model.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Prediction error (root mean square error [RMSE]) was lower than baseline for the group models (11.8 vs. 14.1 and 0.22 vs. 0.29 for AM PRS and HRV change, respectively). At the individual level, prediction accuracy outperformed the baseline model but varied greatly across participants (RMSE range 5.5\u201323.6 and 0.05\u20130.44 for AM PRS and HRV change, respectively).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>At the group level, daily recovery measures can be predicted based on commonly measured variables, with a small subset of variables providing most of the predictive power. However, at the individual level, the key variables may vary, and additional data may be needed to improve the prediction accuracy.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00421-024-05530-2","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T11:07:27Z","timestamp":1718881647000},"page":"3279-3290","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Predicting daily recovery during long-term endurance training using machine learning analysis"],"prefix":"10.1007","volume":"124","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0014-5878","authenticated-orcid":false,"given":"Jeffrey A.","family":"Rothschild","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Stewart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5334-8831","authenticated-orcid":false,"given":"Andrew E.","family":"Kilding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel J.","family":"Plews","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"issue":"4","key":"5530_CR1","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1152\/japplphysiol.00973.2003","volume":"96","author":"J Achten","year":"2004","unstructured":"Achten J, Halson SL, Moseley L, Rayson MP, Casey A, Jeukendrup AE (2004) Higher dietary carbohydrate content during intensified running training results in better maintenance of performance and mood state. 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