{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T02:31:11Z","timestamp":1785810671324,"version":"3.56.0"},"reference-count":28,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%).<\/jats:p>","DOI":"10.3390\/s23031227","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T01:36:26Z","timestamp":1674437786000},"page":"1227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Predicting Injuries in Football Based on Data Collected from GPS-Based Wearable Sensors"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1206-2076","authenticated-orcid":false,"given":"Tomasz","family":"Pi\u0142ka","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Adam Mickiewicz University, 61-614 Pozna\u0144, Poland"},{"name":"KKS Lech Pozna\u0144, 60-320 Pozna\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bart\u0142omiej","family":"Grzelak","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Adam Mickiewicz University, 61-614 Pozna\u0144, Poland"},{"name":"KKS Lech Pozna\u0144, 60-320 Pozna\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7129-5059","authenticated-orcid":false,"given":"Aleksandra","family":"Sadurska","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Adam Mickiewicz University, 61-614 Pozna\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9969-5257","authenticated-orcid":false,"given":"Tomasz","family":"G\u00f3recki","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Adam Mickiewicz University, 61-614 Pozna\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2897-3176","authenticated-orcid":false,"given":"Krzysztof","family":"Dyczkowski","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Adam Mickiewicz University, 61-614 Pozna\u0144, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1136\/bjsports-2013-092383","article-title":"Muscle injury rates in professional football increase with fixture congestion: An 11-year follow-up of the UEFA Champions League injury study","volume":"47","author":"Bengtsson","year":"2013","journal-title":"Br. 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