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The current investigation aimed to adopt machine learning (ML) techniques to understand the impact of training load parameters on the recovery status of athletes. Twenty-six adult soccer players were monitored for six months, during which internal and external load parameters were daily collected. Players\u2019 recovery status was assessed through the 10-point total quality recovery (TQR) scale. Then, different ML algorithms were employed to predict players\u2019 recovery status in the subsequent training session (S-TQR). The goodness of the models was evaluated through the root mean squared error (RMSE), mean absolute error (MAE), and Pearson\u2019s Correlation Coefficient (r). Random forest regression model produced the best performance (RMSE=1.32, MAE=1.04, r = 0.52). TQR, age of players, total decelerations, average speed, and S-RPE recorded in the previous training were recognized by the model as the most relevant features. Thus, ML techniques may help coaches and physical trainers to identify those factors connected to players\u2019 recovery status and, consequently, driving them toward a correct management of the weekly training loads.<\/jats:p>","DOI":"10.2478\/ijcss-2022-0007","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T13:20:44Z","timestamp":1673961644000},"page":"1-16","source":"Crossref","is-referenced-by-count":8,"title":["Analysis of Relationship between Training Load and Recovery Status in Adult Soccer Players: a Machine Learning Approach"],"prefix":"10.2478","volume":"21","author":[{"given":"M.","family":"Mandorino","sequence":"first","affiliation":[{"name":"Department of Movement, Human and Health Sciences , University of Rome \u201cForo Italico\u201d Rome , Italy"},{"name":"Performance and Analytics Department , Parma Calcio 1913, 43121 , Parma , Italy"}]},{"given":"A.J.","family":"Figueiredo","sequence":"additional","affiliation":[{"name":"University of Coimbra, Research Center for Sport and Physical Activity , Faculty of Sport Science and Physical Education , Coimbra , Portugal"}]},{"given":"G.","family":"Cima","sequence":"additional","affiliation":[{"name":"Computer, Control and Management Engineering Department , Sapienza University of Rome , Rome , Italy ."}]},{"given":"A.","family":"Tessitore","sequence":"additional","affiliation":[{"name":"Department of Movement, Human and Health Sciences , University of Rome \u201cForo Italico\u201d Rome , Italy"}]}],"member":"374","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"2026042811035259019_j_ijcss-2022-0007_ref_001","doi-asserted-by":"crossref","unstructured":"Ahmad, M. 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