التفاصيل البيبلوغرافية
| العنوان: |
Football Movement Profile–Based Creatine-Kinase Prediction Performs Similarly to Global Positioning System–Derived Machine Learning Models in National-Team Soccer Players. |
| المؤلفون: |
Schuth, Gabor, Szigeti, György, Dobreff, Gergely, Pašić, Alija, Gabbett, Tim, Szilas, Adam, Pavlik, Gabor |
| المصدر: |
International Journal of Sports Physiology & Performance; Sep2024, Vol. 19 Issue 9, p874-881, 8p |
| مصطلحات موضوعية: |
PREDICTION models, SOCCER, GLOBAL Positioning System, DESCRIPTIVE statistics, CREATINE kinase, MACHINE learning, BODY movement, ATHLETIC ability, COMPARATIVE studies |
| مصطلحات جغرافية: |
HUNGARY |
| مستخلص: |
Purpose: The relationship between external load and creatine-kinase (CK) response at the team/position or individual level using Global Positioning Systems (GPS) has been studied. This study aimed to compare GPS-derived and Football Movement Profile (FMP) –derived CK-prediction models for national-team soccer players. The second aim was to compare the performance of general and individualized CK prediction models. Methods: Four hundred forty-four national-team soccer players (under 15 [U15] to senior) were monitored during training sessions and matches using GPS. CK was measured every morning from whole blood. The players had 19.3 (18.1) individual GPS-CK pairs, resulting in a total of 8570 data points. Machine learning models were built using (1) GPS-derived or (2) FMP-based parameters or (3) the combination of the 2 to predict the following days' CK value. The performance of general and individual-specific prediction models was compared. The performance of the models was described by R2 and the root-mean-square error (RMSE, in units per liter for CK values). Results: The FMP model (R2 =.60, RMSE = 144.6 U/L) performed similarly to the GPS-based model (R2 =.62, RMSE = 141.2 U/L) and the combination of the 2 (R2 =.62, RMSE = 140.3 U/L). The prediction power of the general model was better on average (R2 =.57 vs R2 =.37) and for 73% of the players than the individualized model. Conclusions: The results suggest that FMP-based CK-prediction models perform similarly to those based on GPS-derived metrics. General machine learning models' prediction power was higher than those of the individual-specific models. These findings can be used to monitor postmatch recovery strategies and to optimize weekly training periodization. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |