{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T03:00:40Z","timestamp":1792897240926,"version":"4.5.0"},"reference-count":41,"publisher":"Human Kinetics","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,1]]},"abstract":"<jats:p>\n                    <jats:italic>\n                      <jats:bold>Purpose:<\/jats:bold>\n                    <\/jats:italic>\n                    The study had 3 purposes: (1)\u00a0to develop an index using machine-learning techniques to predict the fitness status of soccer players, (2)\u00a0to explore the index\u2019s validity and its relationship with a submaximal run test (SMFT), and (3)\u00a0to analyze the impact of weekly training load on the index and SMFT outcomes.\n                    <jats:italic>\n                      <jats:bold>Methods:<\/jats:bold>\n                    <\/jats:italic>\n                    The study involved 50 players from an Italian professional soccer club. External and internal loads were collected during training sessions. Various machine-learning algorithms were assessed for their ability to predict heart-rate responses during the training drills based on external load data. The fitness index, calculated as the difference between actual and predicted heart rates, was correlated with SMFT outcomes.\n                    <jats:italic>\n                      <jats:bold>Results:<\/jats:bold>\n                    <\/jats:italic>\n                    Random forest regression (mean absolute error\u2009=\u20093.8\u2009[0.05]) outperformed the other machine-learning algorithms (extreme gradient boosting and linear regression). Average speed, minutes from the start of the training session, and the work:rest ratio were identified as the most important features. The fitness index displayed a very large correlation (\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u2009.70) with SMFT outcomes, with the highest result observed during possession games and physical conditioning exercises. The study revealed that heart-rate responses from SMFT and the fitness index could diverge throughout the season, suggesting different aspects of fitness.\n                    <jats:italic>\n                      <jats:bold>Conclusions:<\/jats:bold>\n                    <\/jats:italic>\n                    This study introduces an \u201cinvisible monitoring\u201d approach to assess soccer player fitness in the training environment. The developed fitness index, in conjunction with traditional fitness tests, provides a comprehensive understanding of player readiness. This research paves the way for practical applications in soccer, enabling personalized training adjustments and injury prevention.\n                  <\/jats:p>","DOI":"10.1123\/ijspp.2023-0444","type":"journal-article","created":{"date-parts":[[2024,2,25]],"date-time":"2024-02-25T06:25:40Z","timestamp":1708842340000},"page":"443-453","source":"Crossref","is-referenced-by-count":26,"title":["Predicting Soccer Players\u2019 Fitness Status Through a Machine-Learning Approach"],"prefix":"10.1123","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5858-2758","authenticated-orcid":true,"given":"Mauro","family":"Mandorino","sequence":"first","affiliation":[{"name":"Performance and Analytics Department, Parma Calcio 1913, Parma, Italy"},{"name":"Department of Movement, Human and Health Sciences, University of Rome \u201cForo Italico,\u201d Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6509-7531","authenticated-orcid":true,"given":"Jo","family":"Clubb","sequence":"additional","affiliation":[{"name":"Global Performance Insights Ltd, London, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1082-0200","authenticated-orcid":true,"given":"Mathieu","family":"Lacome","sequence":"additional","affiliation":[{"name":"Performance and Analytics Department, Parma Calcio 1913, Parma, Italy"},{"name":"Laboratory of Sport, Expertise and Performance (EA 7370), French Institute of Sport (INSEP), Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"100","reference":[{"issue":"2","key":"r1","series-title":"Sports Med","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s40279-014-0253-z","article-title":"Monitoring training load to understand fatigue in athletes","volume":"44","author":"Halson SL","year":"2014","unstructured":"Halson SL. 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