biostudies-literatureUnknown25(22)Abrunedo-Lombardero JUnderstanding how training and match load influence autonomic recovery is essential for optimizing athlete monitoring. This proof-of-concept study aimed to examine the impact of training and match load on next-day heart rate variability (HRV) and to explain how different load measures influenced the internal response, using SHapley Additive Explanations (SHAP) to interpret machine learning models. Five semi-professional basketball players (23 ± 5 years; 191 ± 7 cm; 90 ± 11 kg) were monitored throughout a competitive season. HRV and load metrics were recorded daily. Differences in the natural logarithm of the root mean square of successive differences (LnRMSSD) across Non-Training, Training, and Match days were analyzed using linear mixed models. Additionally, a Gradient Boosting Machine moSensors (Basel, Switzerland)6928https://www.ebi.ac.uk/biostudies/studies/S-EPMC12656197biostudies-literatureAn Explainable Machine Learning Approach to Explain the Effects of Training and Match Load on Ultra-Short-Term Heart Rate Variability in Semi-Professional Basketball Players.PMC12656197Abrunedo-Lombardero JPadron-Cabo AVelez-Serrano DIglesias-Soler EAlvaro-Meca AfalseAn Explainable Machine Learning Approach to Explain the Effects of Training and Match Load on Ultra-Short-Term Heart Rate Variability in Semi-Professional Basketball Players.Understanding how training and match load influence autonomic recovery is essential for optimizing athlete monitoring. This proof-of-concept study aimed to examine the impact of training and match load on next-day heart rate variability (HRV) and to explain how different load measures influenced the internal response, using SHapley Additive Explanations (SHAP) to interpret machine learning models. Five semi-professional basketball players (23 ± 5 years; 191 ± 7 cm; 90 ± 11 kg) were monitored throughout a competitive season. HRV and load metrics were recorded daily. Differences in the natural logarithm of the root mean square of successive differences (LnRMSSD) across Non-Training, Training, and Match days were analyzed using linear mixed models. Additionally, a Gradient Boosting Machine mo2025-01-01T00:00:00Z2025 Nov2026-05-19T03:27:11.788Z2026-05-19T03:12:21.899ZS-EPMC126561974130513610.3390/s25226928