Heart rate variability (HRV) is a non-invasive biomarker that reflects autonomic nervous system dynamics, providing valuable insights into physiological adaptation, stress, and recovery in athletes. Among the various HRV metrics, the root mean square of successive differences (RMSSD) has emerged as a robust and practical measure due to its strong association with parasympathetic activity, ease of calculation, and reliability in both short- and ultra-short-term recordings. This review examines the methodological considerations for using HRV to monitor training adaptations and recovery status in athletic populations. We highlight the superiority of routine, near-daily HRV measurements over isolated assessments, emphasizing the utility of weekly averages and the coefficient of variation (CV) to capture both chronic adaptations and acute homeostatic perturbations. Additionally, we discuss the selection of HRV devices, data recording procedures, and strategies to enhance athlete compliance. While RMSSD offers significant advantages for field-based monitoring, we also address its limitations, including its sole focus on parasympathetic activity and susceptibility to external confounders. Future directions include the integration of HRV data with other physiological markers and machine learning algorithms to optimize individualized training and recovery strategies. This review provides sport scientists and practitioners with evidence-based recommendations to enhance the application of HRV in both research and real-world athletic settings.

Background: Monitoring autonomic balance provides valuable insights into recovery status and physiological readiness, both of which are essential for performance optimization in athletes. The Stress Score (SS) and Sympathetic–Parasympathetic Ratio (SPS), derived from Poincaré plot heart rate variability (HRV) indices, have been proposed as practical markers of sympathetic activity and overall autonomic balance. However, these traditional calculations often require lengthy recordings and specialized software, limiting their feasibility in field settings. This study introduces modified versions of these metrics derived from ultra-short-term (1 min) time–domain HRV recordings: the Modified Stress Score (MSS) and Modified Sympathetic–Parasympathetic Ratio (MSPS). Methods: Competitive male athletes (n = 20, age = 21.2 ± 2.1 year, height = 183.6 ± 8.9 cm, weight = 79.2 ± 10.3 kg) completed a maximal exercise test with HRV recorded before and after exercise. Results: Following natural log-transformation, MSS and MSPS demonstrated strong correlations with SS and SPS across all time points (r = 0.87–0.94, all p < 0.01) and displayed the expected physiological responses to exercise and recovery. Conclusions: These findings suggest that MSS and MSPS are practical, accessible tools for assessing autonomic balance in athletes. Their application may enhance our ability to monitor recovery status, guide individualized training strategies, and optimize performance in applied sport settings.

Objectives: Field-based testing is commonly used to evaluate key physical qualities related to soccer performance. However, limited research has examined the degree of shared variance among measures of aerobic capacity, change of direction (COD), and explosive power in youth athletes. This study investigated the relationships between the 20 m shuttle run (20MSR), T-test (TT), and vertical countermovement jump (CMJ) to determine their unique and overlapping contributions to each other’s performance in competitive youth soccer players. Methods: Twenty-five competitive male youth soccer players (13.7 ± 0.8 years) completed standardized assessments of TT, CMJ, and 20MSR during pre-season evaluations. Pearson correlations and hierarchical multiple regression analyses were used to examine associations and independent variance explained among the performance measures. Results: Large, significant correlations were observed between TT and CMJ (r = −0.65, p < 0.001), TT and 20MSR (r = −0.59, p < 0.001), and CMJ and 20MSR (r = 0.53, p = 0.007). CMJ explained 42.3% of TT variance, whereas adding 20MSR did not significantly improve model fit (ΔR² = 0.087, p = 0.062). Across models, aerobic capacity did not contribute significant unique variance beyond neuromuscular performance. Conclusions: COD and lower-body power share a common physiological foundation in youth soccer athletes, while aerobic capacity represents a distinct performance domain. When field tests are administered under applied conditions typical of youth soccer environments, TT and CMJ demonstrate substantial shared variance, whereas 20MSR remains largely independent. Therefore, the findings support the continued use of multi-modal testing batteries in practice.