Summary of the metrics of the different trained algorithms based on mean and 95% confidence intervals (calculated using t-Student) for the Testing process in each dataset.

Summary of the metrics of the different trained algorithms based on mean and 95% confidence intervals (calculated using t-Student) for the Testing process in each dataset.

<p>Summary of the metrics of the different trained algorithms based on mean and 95% confidence intervals (calculated using t-Student) for the Testing process in each dataset.</p>

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Bibliographic Details
Main Author:	Jaime B. Matas-Bustos (21772430) (author)
Other Authors:	Antonio M. Mora-García (21772433) (author), Moisés de Hoyo Lora (21772436) (author), Alejandro Nieto-Alarcón (21772439) (author), Francisco T. Gonzalez-Fernández (21772442) (author)
Published:	2025
Subjects:	Genetics Biotechnology Mathematical Sciences not elsewhere classified training load management signal processing principles permutation tests revealed modern machine learning medical professionals involved footballer workload footprint elite competition settings discrete time series derive two representations controlling training monotony chronic workload ratio advanced feature engineering type ii errors interpretability framework based differential operations — temporal matrix termed div >< p auc ), precision temporal variations reducing type recall auc generalizable framework accuracy — mean ), aw ), fwf matrix xlink "> world data using real uefa tournaments top division sports science spain ’ significant association recent research prevent non injury risk injury prevention injury forecasting heatmap visualizations gps data findings indicate empirical validity contact injuries confidence intervals conducted exploratory bilinear modeling
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